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Skip to comments.The Golden Calf of Evolution is on Fire
Posted on 08/23/2005 10:39:22 AM PDT by woodb01
The Golden Calf of Evolution is on Fire
NoDNC.com staff
The recent notice that Harvard was going to engage in advocacy research (its difficult to call the advocacy science) shows how concerned the evolution camp is about the theory of intelligent design. Contrary to popular myth, the theory of evolution has many holes. The only way evolution continues to survive is because people dont actually stop to think about the absurd things that evolution requires one to accept on totally blind faith.
If in fact evolution were truly a science, then according to the scientific method, challenges to the theory of evolution, even a challenge calling itself intelligent design would be readily accepted. The whole notion of science is to put forth a theory, and then work to further develop the theory, or abandon it, based on challenges to discrete aspects of that theory. Real science not only accepts those challenges, but encourages them to ensure its accuracy. Evolutionists routinely censor and attack all dissent.
Now why would real scientists be so concerned about intelligent design? Why would prestigious Harvard University commit to invest a million dollars annually in a new program dedicated on the origins of life in relation to evolution? And as Harvard chemistry professor David Liu noted "My expectation, is that we will be able to reduce this to a very simple series of logical events that could have taken place with no divine intervention."
That is an interesting statement from a scientist. In professional circles, this is called confirmatory bias and it is not about science, but about making additional theories fit the predefined outcome that you want them to fit. It is advocacy research and not science. After all, with evolution, there is no way to test or verify history, so it is routine to just create anything you can imagine to fill the void, anything except intelligent design. Taking their cues from cults, when something doesnt fit, just make up something that cant be verified.
The secret of why Darwinists (evolutionists) see intelligent design as a threat is because in its simplest form, it is not only verifiable, but intelligent design is an ideal corollary [FN1] to the Second Law of Thermodynamics. Paraphrased that law says:
Any system, on its own, moves from order to disorder, and eventually becomes totally random.
The Second Law of Thermodynamics is considered an absolute, solid, verified truth in science. The reason it is considered a law in science is because it is said to apply to all matter in the entire universe and in all situations and circumstances. It has been tested, re-tested, verified, and re-verified and found to be a universal scientific truth.
Why is the Second Law of Thermodynamics Important?
Evolution defies the Second Law of Thermodynamics. In plain terms, it expects people to accept, on blind, unverifiable faith, that out of disorder, and through a bunch of accidents, order is created--, disorder becomes order.
Another way of looking at that would be to think of a deck of cards, carefully shuffled and thrown high in the air. With the expectation that eventually an accident would happen which would cause all 52 cards in the deck, to fall in perfect order, and perfectly aligned. [FN2]
Now we get to the interesting part, the part that absolutely horrifies Darwinists and all evolutionists in particular. INTELLIGENT DESIGN IS THE COROLLARY [See FN1] TO THE SECOND LAW OF THERMODYNAMICS!
With external inputs of energy, directed in a specialized way, disorder and randomness can be ordered.
Any system, whether open or closed, requires specialized work or specialized energy input to go from disorder to order. This same specialized work or specialized energy input is also required just to maintain order.
Lets look at it this way. If you work at a desk, or construction, or homemaker, or whatever your job is, there are parallels. Evolutionists expect you to believe that if you leave a mess long enough, a set of accidents will eventually occur that will organize all your papers, build a new house, or clean each room in your house, etc. This is plain nonsense and not science.
Evolutionists realize that a COROLLARY to the Second Law of Thermodynamics is both science, is testable, is verifiable, and is true. This is why they are terrified. For evolution to work it requires that a settled scientific LAW be changed to accommodate it. Evolutions FALSE COROLLARY to the Second Law of Thermodynamics expects one to accept the following paraphrased idea:
With external inputs of energy, random or disordered energy creates order.
In more evolutionary terms, enough accidents, stacked on top of each other, for a long enough period of time, creates order and perfection. Never mind that evolution also says that natural selection destroys all accidents that dont have almost immediate usefulness. It is lunacy to believe that from random occurrence you gain greater and greater order. It then becomes zealous fanaticism when you deny that this is anything more than a secular fundamentalist belief system. In fact, this is in direct defiance of the Second Law of Thermodynamics. Under evolution, instead of moving toward disorder, we are moving toward order.
On one hand we hear that life has developed and evolved through accidents that create the variations of the species. And in contradiction to everything coming about because of these accidents, Darwins evolutionists say that natural selection does away with the accidents and chooses the superior accidents. On one hand we have life being created, derived, developed and sustained through accidents, and on the other hand we have life being destroyed and killed off (natural selection) because the accidents arent the right type of accident.
STOP AND THINK about what evolution demands you to believe. Disorder creates order, accidents fix things. This is not only intellectually dishonest, it is absurd when you stop to think about it.
Is this Corollary Theory of the Second Law Intelligent Design Testable?
Routinely we hear from the evolution crowd that intelligent design is not testable. Not only is this blatantly false, the Corollary to the Second Law of Thermodynamics (intelligent design) has been proven over, and over, and over again. In fact, it continues to be proven many thousands of times a day.
Every time a pharmaceutical medication is taken to treat a disorder, whether it is physical or mental, it is a test of the theory of intelligent design. The Pharmaceutical companies that research new drug applications to treat disease not only defy natural selection but direct energy and efforts to cure a disorder which results in a medication to treat the disorder.
Every time a doctor performs a necessary surgery, that is successful, it is not only a test of intelligent design, but proof that it is valid. The Physician brings order to disorder and again defies natural selection.
Over and over again, architect, electrical engineer, physicist, chemist, veterinary, and any number of professions routinely cheat natural selection with intelligent design. Over and over again evolutions accidents and natural selections are overcome by intelligent design.
Is it any wonder that the evolution crowd is terrified by intelligent design? Proving intelligent design disproves evolution. When considering intelligent design as a corollary to the Second Law of Thermodynamics, as well as easily tested and verified, its no wonder evolutionists are frightened.
Why so narrowly confined?
When major problems with evolution are raised, such as the INPUTS to the whole evolutionary process, evolutionists shriek, almost in horrified pain that doesnt apply, or thats another area. Take for example the origins of life itself. When raising the proposition that the origins of the chemical INPUTS to life, and the origins of life itself are critical building blocks to verify whether or not evolution is valid, routine shrieks of abiogenesis or some other silly segment of the process is invoked to defend the indefensible. These silly segmentations, which alone may disprove evolution, are routinely segmented out of the idea of evolution. These things are treated almost as if they must be warded off with some magical talisman or incantation against any evil spirits that might challenge the evolutionary cult. Evolutionists hide behind these silly, ridiculous, and utterly absurd notions that you can build valid science on a small piece of a process and leave out all of the pieces that the process depends on.
When parts of the process not only demonstrate that the sacred theory of evolution may be invalid or false, the shrieks of heresy and blasphemy are raised. This isnt science, it is utter madness disguised as science. And certainly I can understand why the issue of the initial origins of life terrify evolutionists. The idea of abiogenesis expects one to accept on blind faith that life just magically appeared from some accidents with rocks, water, and a few base chemicals. Evolution suggests that right after that life was created, it began evolving. This is difficult to believe when you stop and think about it. Life magically appears from rocks, water, and a few chemicals? Im still amazed that all those alchemists in the middle ages couldnt find a way to do something as simple as turning lead into gold. If they had simply applied evolutions teachings, water would have been gold, diamonds, and every other form of precious gem.
Evolutionary theory demands that only physical / material properties can be evaluated. This notion completely ignores the fact that human beings have the ability to reason, to think through things, to make value judgments, to make decisions, to choose right or wrong, to have order and structure or to have disorder and chaos.
This is another point of conflict, if you accept evolutions true premises, only natural selection is valid and all of our morals, values, and social structures arent valid. But they exist and their very existence proves that evolution has more holes. So what do the evolutionists do? No problem, they say that social structures just dont apply. Its not material so we wont even consider it.
Evolution by other names is the law of the jungle, survival of the fittest, kill or be killed, a form of natural eugenics, etc. So, if you remove the social structures, the laws, rules, morals, values, the social structures, all you have are wild animals.
The law of the jungle part of evolution is a glaring defect and a strong demonstration that evolution misses the mark. There is something more to human life than just kill or be killed. So what do the evolutionists do? They simply spout their dogma that doesnt apply, were only looking at the material world! Its easy to understand why they would do this, under the idea of eugenics, Hitler slaughtered millions.
If you stop and think about what evolutionary processes was required to create emotions, social structure, values, order, and the awareness of self, it is easy to understand why evolutionists are terrified of this. By their nature, by what these things ARE, they are not natural evolutionary occurrences. By themselves, they could not have come about by any type of evolutionary theory known today. So having these artificial structures imposed on evolution disproves evolution.
Evolutions true believers treat any challenge to their sacred cow as blasphemy or heresy --, I guess thats a normal reaction to a religious belief.
Evolutionists are terrified. And the debate must be contained. If the debate is not contained, the public school indoctrination and the cult of evolution will collapse. Once people actually stop and think about the blind leaps of faith that evolution requires, it will be seen as the cult it is. Evolution is nothing but wild religious beliefs clothed with the appearance of science.
The golden calf of evolution is on fire. As more and more people actually stop and THINK THROUGH the lunacy that evolution expects you to believe on totally blind faith, evolution will finally be seen for what it truly is, a religion pretending to be science. At that point the fire consuming the golden calf of evolution will turn it to ashes.
[FN1] A corollary is something that is generally a natural consequence of the thing it is related to. So when a corollary is based on something that is already proven, the corollary generally does not require much proof because it is accepted and understood. For example, water freezes and turns to ice at about 32 degrees (F) depending on atmospheric conditions. A corollary would be that water melts as it rises above 32 degrees (F).
[FN2] Before all of the shrieks from the Darwinists, what I have just outlined is called an analogous syllogism, it is a writing tool to help understand complex issues.
Additional Resources:
DNA: The Tiny Code That's Toppling Evolution (DNA is PROVING that evolution is a hoax)
The controversy over evolution includes a growing number of scientists who challenge Darwinism. (The fraud of Darwinism...)
Einstein Versus Darwin: Intelligent Design Or Evolution? (Most LEGITIMATE Scientists do NOT agree with Evolution)
Whats the Big Secret? (Intelligent Design in Pennsylvania)
What are the Darwinists afraid of? (The fervent religious belief in evolution)
The Little Engine That Could...Undo Darwinism (Evolution may be proven false very soon)
Unfortunately, when it comes to the subject of evolution, blind fanaticism override common sense and reason.
I just can't believe that order "magically" appears from disorder. The whole idea that a mess cleans itself up, and makes itself neat and orderly is completely beyond rational thinking... It shows how absurd the "scientific" arguments from evolutionists really is... Come on folks, stop and THINK about what evolution insists you believe!
put your drinks down first.
Please name a scientist who has asked you to believe that magic creates things.
Common sense says that the sun revolves around the Earth.
This debate is so stupid. Creationists are like people arguing with Neil Armstrong after he got back from the moon that the Earth is actually flat.
... or pour a stiff one...
Evolution: The ultimate in revisionist history.
Oh, dude- this should be fun...
Because contrary to your ignorant and incorrect propaganda, there is a *MASSIVE* amount of evidence supporting evolution, it has passed tens of thousands of validation tests, and passed countless falsification tests.
Deal with it.
The "cute" little arguments offered by supporters of evolution simply DEFY common sense.
Endogenous retroviruses defy common sense? How?
Unfortunately, when it comes to the subject of evolution, blind fanaticism override common sense and reason.
And that's just the creationists, who want to blindly rail against evolution without actually being familiar with the evidence.
I just can't believe that order "magically" appears from disorder.
Neither can I. Magic has nothing to do with it. However, being well-versed in information science, and having studied the topic for over three decades, I'm well aware that information can and does arise naturally from "disorder" when certain natural processes are at work. Deal with it.
The whole idea that a mess cleans itself up, and makes itself neat and orderly is completely beyond rational thinking...
Translation: Because *YOU* can't understand how that can happen, it *must* be impossible...
Learn some science before you attempt to critique it.
Where do you start?
Those with a more emotional objection to the evidence have an emotiobnal objection to overcome.
But they have to be able to see that self-created barrier first. And many simply won't.
Does gold burn?
[From: http://www.talkorigins.org/faqs/comdesc/]
29+ Evidences for Macroevolution
The Scientific Case for Common DescentVersion 2.85
Copyright © 1999-2004 by Douglas Theobald, Ph.D.[Last Update: April 15, 2005]
Permission is granted to copy and print these pages in total for non-profit personal, educational, research, or critical purposes.
volution, the overarching concept that unifies the biological sciences, in fact embraces a plurality of theories and hypotheses. In evolutionary debates one is apt to hear evolution roughly parceled between the terms "microevolution" and "macroevolution". Microevolution, or change beneath the species level, may be thought of as relatively small scale change in the functional and genetic constituencies of populations of organisms. That this occurs and has been observed is generally undisputed by critics of evolution. What is vigorously challenged, however, is macroevolution. Macroevolution is evolution on the "grand scale" resulting in the origin of higher taxa. In evolutionary theory it thus entails common ancestry, descent with modification, speciation, the genealogical relatedness of all life, transformation of species, and large scale functional and structural changes of populations through time, all at or above the species level (Freeman and Herron 2004; Futuyma 1998; Ridley 1993).
Common descent is a general descriptive theory that concerns the genetic origins of living organisms (though not the ultimate origin of life). The theory specifically postulates that all of the earth's known biota are genealogically related, much in the same way that siblings or cousins are related to one another. Thus, macroevolutionary history and processes necessarily entail the transformation of one species into another and, consequently, the origin of higher taxa. Because it is so well supported scientifically, common descent is often called the "fact of evolution" by biologists. For these reasons, proponents of special creation are especially hostile to the macroevolutionary foundation of the biological sciences.
This article directly addresses the scientific evidence in favor of common descent and macroevolution. This article is specifically intended for those who are scientifically minded but, for one reason or another, have come to believe that macroevolutionary theory explains little, makes few or no testable predictions, is unfalsifiable, or has not been scientifically demonstrated.
- Universal Common Descent Defined
- Evidence for Common Descent is Independent of Mechanism
- What Counts as Scientific Evidence
- Other Explanations for the Biology
- How to Cite This Document
Scientific Evidence and the Scientific Method
- Anatomical vestiges
- Atavisms
- Molecular vestiges
- Ontogeny and developmental biology
- Present biogeography
- Past biogeography
- Genetic
- Morphological
- Functional
- The strange past
- Stages of speciation
- Speciation events
- Morphological rates
- Genetic rates
The old deck-of-cards analogy.
If you throw an unboxed, unordered deck of cards into the air, the odds are astronomically against them landing in order on the ground.
The odds are so great, that you might say they are a billion to one, maybe more.
Even if you do it a billion times, common sense says that "it won't happen".
"Therefore evolution didn't happen", is the conclusion.
Never mind that nobody has ever said that a microorganism will turn into a giraffe.
The deck-of-cards analogy fails because it assumes the same starting point each time.
Evolution says that you start from the BETTER of the original, or the mutation. The lesser dies out.
If you could put the cards back the way they were before you threw them, then you could choose to keep the new throw or the old throw, depending on whether it was more ordered or not.
Ichneumon on the Scientific Method. It's post 401 and it's excellent.
Ichneumon's legendary post 52. More evidence than you can handle.
Post 661: Ichneumon's stunning post on transitionals.
Or how about:
Background: Retroviruses reproduce by entering a cell of a host (like, say, a human), then embedding their own viral DNA into the cell's own DNA, which has the effect of adding a "recipe" for manufacturing more viruses to the cell's "instruction book". The cell then follows those instructions because it has no reason (or way) to "mistrust" the DNA instructions it contains. So the virus has converted the cell into a virus factory, and the new viruses leave the cell, and go find more cells to infect, etc.
However, every once in a while a virus's invasion plans don't function exactly as they should, and the virus's DNA (or portions of it) gets embedded into the cell's DNA in a "broken" manner. It's stuck into there, becoming part of the cell's DNA, but it's unable to produce new viruses. So there it remains, causing no harm. If this happens in a regular body cell, it just remains there for life as a "fossil" of the past infection and goes to the grave with the individual it's stuck in. All of us almost certainly contain countless such relics of the past viral infections we've fought off.
However... By chance this sometimes happens to a special cell in the body, a gametocyte cell that's one of the ones responsible for making sperm in males and egg cells in females, and if so subsequent sperm/eggs produced by that cell will contain copies of the "fossil" virus, since now it's just a portion of the entire DNA package of the cell. And once in a blue moon such a sperm or egg is lucky enough to be one of the few which participate in fertilization and are used to produce a child -- who will now inherit copies of the "fossilized" viral DNA in every cell of his/her body, since all are copied from the DNA of the original modified sperm/egg.
So now the "fossilized" viral DNA sequence will be passed on to *their* children, and their children's children, and so on. Through a process called neutral genetic drift, given enough time (it happens faster in smaller populations than large) the "fossil" viral DNA will either be flushed out of the population eventually, *or* by luck of the draw end up in every member of the population X generations down the road. It all depends on a roll of the genetic dice.
Due to the hurdles, "fossil" retroviral DNA strings (known by the technical name of "endogenous retroviruses") don't end up ubiquitous in a species very often, but it provably *does* happen. In fact, the Human DNA project has identified literally *thousands* of such fossilized "relics" of long-ago ancestral infections in the human DNA.
And several features of these DNA relics can be used to demonstrate common descent, including their *location*. The reason is that retroviruses aren't picky about where their DNA gets inserted into the host DNA. Even in an infection in a *single* individual, each infected cell has the retroviral DNA inserted into different locations than any other cell. Because the host DNA is so enormous (billions of basepairs in humans, for example), the odds of any retroviral insertion event matching the insertion location of any other insertion event are astronomically low. The only plausible mechanism by which two individuals could have retroviral DNA inserted into exactly the same location in their respective DNAs is if they inherited copies of that DNA from the same source -- a common ancestor.
Thus, shared endogenous retroviruses between, say, ape and man is almost irrefutable evidence that they descended from a common ancestor. *Unless* you want to suggest that they were created separately, and then a virus they were both susceptible to infected both a man and an ape in EXACTLY the same location in their DNAs (the odds of such a match by luck are literally on the order of 1,000,000,000,000,000,000 to 1...), *and* that the infections both happened in their gametocyte cells (combined odds on the order of 1,000,000 to 1) *and* that the one particular affected gametocyte is the one which produces the egg or sperm which is destined to produce an offspring (*HUGE* odds against), and *then* the resulting modified genome of the offspring becomes "fixed" in each respective population (1 out of population_size^squared)...
Then repeat that for *each* shared endogenous retrovirus (there are many) you'd like to claim was acquired independently and *not* from a shared ancestor...
Finally, you'd have to explain why, for say species A, B, and C, the pattern of shared same-location retroviruses is always *nested*, never *overlapped*. For example, all three will share some retroviruses, then A and B will both share several more, but if so then B *never* shares one with C that A doesn't also have (or at least remnants of).
In your "shared infection due to genetic similarities" suggestion, even leaving aside the near statistical impossibility of the infections leaving genetic "scars" in *exactly* the same locations in independent infections, one would expect to find cases of three species X, Y, and Z, where the degree of similarity was such that Y was "between" X and Z on some similarity scale, causing the same disease to befall X and Y but not Z, and another disease to affect Y and Z but not X. And yet, we don't find this in genetic markers. The markers are found in nested sequence, which is precisely what we would expect to see in cases of inheritance from common ancestry.
Here, for example, is an ancestry tree showing the pattern of shared same-location endogenous retroviruses of type HERV-K among primates:
This is just a partial list for illustration purposes -- there are many more.
Each labeled arrow on the chart shows an ERV shared in common by all the branches to the right, and *not* the branches that are "left-and-down". This is the pattern that common descent would make. And common descent is the *only* plausible explanation for it. Furthermore, similar findings tie together larger mammal groups into successively larger "superfamilies" of creatures all descended from a common ancestor.
Any presumption of independent acquisition is literally astronomically unlikely. And "God chose to put broken relics of viral infections that never actually happened into our DNA and line them up only in patterns that would provide incredibly strong evidence of common descent which hadn't actually happened" just strains credulity (not to mention would raise troubling questions about God's motives for such a misleading act).
Once again, the evidence for common descent -- as opposed to any other conceivable alternative explanation -- is clear and overwhelming.
Wait, want more? Endogenous retroviruses are just *one* type of genetic "tag" that makes perfect sense evolutionary and *no* sense under any other scenario. In addition to ERV's, there are also similar arguments for the patterns across species of Protein functional redundancies, DNA coding redundancies, shared Processed pseudogenes, shared Transposons (including *several* independent varieties, such as SINEs and LINEs), shared redundant pseudogenes, etc. etc. Here, for example, is a small map of shared SINE events among various mammal groups:
Like ERV's, any scenario which suggests that these shared DNA features were acquired separately strains the laws of probability beyond the breaking point, but they make perfect sense from an evolutionary common-descent scenario. In the above data, it is clear that the only logical conclusion is that, for example, the cetaceans, hippos, and ruminants shared a common ancestor, in which SINE events B and C entered its DNA and then was passed on to its descendants, yet this occurred after the point in time where an earlier common ancestor had given rise both to that species, and to the lineage which later became pigs.
And this pattern (giving the *same* results) is repeated over and over and over again when various kinds of molecular evidence from DNA is examined in detail.
The molecular evidence for evolution and common descent is overwhelming. The only alternative is for creationists to deny the obvious and say, "well maybe God decided to set up all DNA in *only* ways that were consistent with an evolutionary result even though He'd have a lot more options open to him, even including parts which by every measure are useless and exactly mimic copy errors, ancient infections, stutters, and other garbage inherited from nonexistent shared ancestors"...
Humans have 23 pairs of chromosomes ---chimps and gorillas have 24 pairs. How many pairs of chromosomes did the "common ancestor" have? Was it 23 or 24 pairs? How do you "evolve" missing or added chromosomes ---that would happen all at one time.And:
The common ancestor had 24 chromosomes.
If you look at the gene sequences, you'll find that Chromosome 2 in humans is pretty much just 2 shorter chimpanzee chromosomes pasted end-to-end, with perhaps a slight bit of lost overlap:
(H=Human, C=Chimpanzee, G=Gorilla, O=Orangutan)
Somewhere along the line, after humans split off from the other great apes, or during the split itself, there was an accidental fusion of two chromosomes, end-to-end. Where there used to be 24 chromosomes, now there were 23, but containing the same total genes, so other than a "repackaging", the DNA "instructions" remained the same.
If a chimpanzee gives birth to a creature with 23 chromosomes, that offspring isn't going to be a well-formed chimpanzee able to survive well.
It is if the same genes are present, which they would be in the case of a chromosome fusion.
Evolve would imply the genetic material changes little by little --not some big loss of two chromosomes at once but I don't see how they'd go away gene by gene.
Tacking two chromosomes together end-to-end is not a "big loss" of genes, and it really is a "little by little" change in the total genetic code. It's just been "regrouped" a bit. Instead of coming in 24 "packages", it's now contained in 23, but the contents are the same.
So how, you might ask, would the chromosomes from the first 23-chromosome "fused" individual match up with the 24 chromosomes from its mate when it tried to produce offspring? Very well, thanks for asking. The "top half" of the new extra-long Chromosome 2 would adhere to the original chromosome (call it "2p") from which it was formed, and likewise for the "bottom half" which would adhere to the other original shorter chromosome (call it "2q"). In the picture above, imagine the two chimp chromosomes sliding over to "match up" against the human chromosome. The chimp chromosomes would end up butting ends with each other, or slightly overlapping in a "kink", but chromosomes have overcome worse mismatches (just consider the XY pair in every human male -- the X and the Y chromosome are *very* different in shape, length, and structure, but they still pair up).
In fact, the "rubbing ends" of the matched-up chimp chromosomes, adhering to the double-long human-type chromosome, would be more likely to become fused together themselves.
For studies in which recent chromosome fusions have been discovered and found not to cause infertility, see:Chromosomal heterozygosity and fertility in house mice (Mus musculus domesticus) from Northern Italy. Hauffe HC, Searle JB Department of Zoology, University of Oxford, Oxford OX1 3PS, United Kingdom. firstname.lastname@example.com that last reference, the Przewalski horse, which has 33 chromosomes, and the domestic horse, with 32 chromosomes (due to a fusion), are able to mate and produce fertile offspring.
An observed chromosome fusion: Hereditas 1998;129(2):177-80 A new centric fusion translocation in cattle: rob (13;19). Molteni L, De Giovanni-Macchi A, Succi G, Cremonesi F, Stacchezzini S, Di Meo GP, Iannuzzi L Institute of Animal Husbandry, Faculty of Agricultural Science, Milan, Italy.
J Reprod Fertil 1979 Nov;57(2):363-75 Cytogenetics and reproduction of sheep with multiple centric fusions (Robertsonian translocations). Bruere AN, Ellis PM
J Reprod Fertil Suppl 1975 Oct;(23):356-70 Cytogenetic studies of three equine hybrids. Chandley AC, Short RV, Allen WR.
Meanwhile, the question may be asked, how do we know that the human Chromosome 2 is actually the result of a chromsome fusion at/since a common ancestor, and not simply a matter of "different design"?
Well, if two chromsomes accidentally merged, there should be molecular remnants of the original chromosomal structures (while a chromosome designed from scratch would have no need for such leftover "train-wreck" pieces).
Ends of chromosomes have characteristic DNA base-pair sequences called "telomeres". And there are indeed remnants of telomeres at the point of presumed fusion on human Chromosome 2 (i.e., where the two ancestral ape chromosomes merged end-to-end). If I may crib from a web page:Telomeres in humans have been shown to consist of head to tail repeats of the bases 5'TTAGGG running toward the end of the chromosome. Furthermore, there is a characteristic pattern of the base pairs in what is called the pre-telomeric region, the region just before the telomere. When the vicinity of chromosome 2 where the fusion is expected to occur (based on comparison to chimp chromosomes 2p and 2q) is examined, we see first sequences that are characteristic of the pre-telomeric region, then a section of telomeric sequences, and then another section of pre-telomeric sequences. Furthermore, in the telomeric section, it is observed that there is a point where instead of being arranged head to tail, the telomeric repeats suddenly reverse direction - becoming (CCCTAA)3' instead of 5'(TTAGGG), and the second pre-telomeric section is also the reverse of the first telomeric section. This pattern is precisely as predicted by a telomere to telomere fusion of the chimpanzee (ancestor) 2p and 2q chromosomes, and in precisely the expected location. Note that the CCCTAA sequence is the reversed complement of TTAGGG (C pairs with G, and T pairs with A).Another piece of evidence is that if human Chromosome 2 had formed by chromosome fusion in an ancestor instead of being designed "as is", it should have evidence of 2 centromeres (the "pinched waist" in the picture above -- chromosomes have centromeres to aid in cell division). A "designed" chromosome would need only 1 centromere. An accidentally "merged" chromosome would show evidence of the 2 centromeres from the two chromosomes it merged from (one from each). And indeed, as documented in (Avarello R, Pedicini A, Caiulo A, Zuffardi O, Fraccaro M, Evidence for an ancestral alphoid domain on the long arm of human chromosome 2. Hum Genet 1992 May;89(2):247-9), the functional centromere found on human Chromosome 2 lines up with the centromere of the chimp 2p chromosome, while there are non-functional remnants of the chimp 2q centromere at the expected location on the human chromosome.
As an aside, the next time some creationist claims that there is "no evidence" for common ancestry or evolution, keep in mind that the sort of detailed "detective story" discussed above is repeated literally COUNTLESS times in the ordinary pursuit of scientific research and examination of biological and other types of evidence. Common ancestry and evolution is confirmed in bit and little ways over and over and over again. It's not just something that a couple of whacky anti-religionists dream up out of thin air and promulgate for no reason, as the creationists would have you believe.
[The poster known as Mr. LLLICHY wrote:] Here is that Vitamin C data
After discovering this same data on another thread along with more discussion than has appeared here (I've taken the liberty of pinging the participants of that discussion), I see what the "mystery" is supposed to be -- it's supposed be why did some sites have multiple mutations while (small) stretches of other sites had none? In other words, why do the mutations appear clustered?
(You know, it would really help if people explained their points and questions in more detail, instead of leaving people to guess what the poster was thinking...)
[LLLICHY wrote:] "U238" that decays thrice, pretty good trick when there is "U238" that does not decay at all in 50,000,000 years.
Actually, no site had mutations "thrice". Three different bases at a given site is only *two* mutations (one original base, plus two mutations from it to something else).
Here's the "mutation map" from the actual DNA data:--1-12--1-1-1-1--------1112112--1---1-11-1--------1 ALL/nNo mutations ("-") in about half the sites, one mutation at several (17) sites, two mutations at three sites.
The first thing to keep in mind that random processes tend to "cluster" more than people expect anyway. People expect "randomness" to "spread out" somewhat evenly, but instead it's usually more "clumped", for statistical reasons that would be a diversion to go into right now. So "that looks uneven" isn't always a good indication that something truly is non-random.
If you don't believe me on that, I wrote a program which made 23 mutations totally at random on a 51-site sequence, then repeated the process to see what different random outcomes would look like:10 X$=STRING$(51,"-") 20 FOR I=1 TO 23 30 J%=INT(RND*51)+1 40 C$=MID$(X$,J%,1) 50 IF C$="-" THEN MID$(X$,J%,1)="1" ELSE MID$(X$,J%,1)=CHR$(ASC(C$)+1) 60 NEXT I 70 PRINT X$ 80 GOTO 10Yeah, it's BASIC, so sue me. Here's a typical screenful of the results:-21---1---2---111----2-----2-1121-------1---1--11-1 -1--1--21-11---1-1--1-1---1----1---21-11111---11--- 3-11---3-----1-----11-2-1---1--1----3--2---1--1---- ---1-1--22--1-1--2-2111--1-1111---1------1-------1- ---32----1-11-1-----1---2-231----1------1-----11--1 ----2---21--1---4----1-------------11-1--111-11-211 11--1-1---1-----1--1------1----3111--1----111-2-1-2 1112---1-3-1----1-1-----1-1------121--111-------1-1 -111121--1----1----1-1-1-1-11-2---1-1-------1-111-- -----------11-1---11-11--------21----12211--1---131 --1-211-1-1----21--11-1-2----1--1----11---11-----11 12---1-13------------2---21-21---11-1-1-1--2------- -----2-1---1-1----21--11-11-1---111-1--111-----2--1 -----1-----1-1-1-1---1-2----11-21-11--1-111---1-21- ---11--1-1-122-1-1-1--1-----2-1-1-1-------1-1---111 --2--11----2--1---12-2----1-1---1-1--1--12----1-1-1 -111-1-----1-1----------1-21111--1-2-11-11-1----11- 11-1--211-1221-----1--1-----11--1-2-1----------11-- -----1-12-11---2-1---11--1-2--1----11---111-1----11 11----1--12---12----1---31---1-11----2--1-11-1----- ---1--111-1--1-1-111----1-21----1-1-3---1------2--1 -2-11----1-1------1------2-1-1--111-111-1-1----1111 1--1--1-1---1-111111--2--1-1------112----2---11----Notice how oddly "clustered" most of them look, including one run which left a 13-site stretch "absolutely untouched", contrary to intuition (while having *4* mutations at a single site!)
Frankly, I don't see anything in the real-life DNA mutation map which looks any different from these truly random runs. Random events tend to cluster more than people expect. That solves the "mystery" right there.
Also, there may be a selection factor -- the GLO gene is a *lot* bigger than this. One has to wonder if this small 51-bp section was presented just because it was the one that looked "least random". That would be a no-no, since one can always hand-select the most deviant subset out of larger sample in order to artificially skew the picture.
However, since there are some interesting evolutionary observations to be made, let's look at that DNA data again, slightly rearranged:TAC CCC GTG GAG GTG CGC TTC ACT CGG GCG GAC GAC ATC CTG CTG AGC CCC PIG TAC CCC GTG GAG GTA CGC TTC ACT CGC GGG GAC GAC ATC CTG CTG AGC CCC BOS TAC CCC GTA GAG GTG CGC TTC ACC CGA GGC GAT GAC ATT CTG CTG AGC CCC RAT TAC CCC GTG GAG GTG CGC TTC ACC CGA GGT GAT GAC ATC CTG CTG AGC CCG MOUSE TAC CCT GTG GGG GTG CGC TTC ACC CGG GGG GAC GAC ATC CTG CTG AGC CCC GUIN PIG TAC CTG GTG GGG GTA CGC TTC ACC TGG AG* GAT GAC ATC CTA CTG AGC CCC HUMAN TAC CTG GTG GGG CTA CGC TTC ACC TGG AG* GAT GAC ATC CTA CTG AGC CCC CHIMPANZEE TAC CCG GTG GGG GTG CGC TTC ACC CAG AG* GAT GAC GTC CTA CTG AGC CCC ORANGUTAN TAA CCG GTG GGG GTG CGC TTC ACC CAA GG* GAT GAC ATC ATA CTG AGC CCC MACAQUEHere I've put spaces between codons, and clustered the closely-related species together: pig/cow as ungulates, rat/mouse for their obvious relationship, guinea pig right below them but separated because of the pseudogene nature of its GLO gene, then primates all in a group, with man's closest relative, the chimp, immediately below him, followed by the more distant orangutan, and the even more distant macaque. Also note that the top four have "working" GLO genes, and the bottom five have "broken" GLO pseudogenes.
First, let's consider just the four species with working GLO genes. Evolution predicts that even over large periods of time, these genes will be "highly conserved", with natural selection weeding out mutations that could "break" the gene. Note that the mutations will still have occurred in individuals of the population, but natural selection will "discourage" that mutation from spreading into the general population.
And before we go any further, let's talk about the "universal genetic code". In all mammals (indeed, in almost all living organisms), each triplet of DNA sites cause a particular amino acid to be formed. The mapping of triplets (called "codons") to amino acids is as follows:
Second Position of Codon T C A G F
TTT Phe [F] TTC Phe [F] TTA Leu [L] TTG Leu [L]
TCT Ser [S] TCC Ser [S] TCA Ser [S] TCG Ser [S]
TAT Tyr [Y] TAC Tyr [Y] TAA Ter [end] TAG Ter [end]
TGT Cys [C] TGC Cys [C] TGA Ter [end] TGG Trp [W]
CTT Leu [L] CTC Leu [L] CTA Leu [L] CTG Leu [L]
CCT Pro [P] CCC Pro [P] CCA Pro [P] CCG Pro [P]
CAT His [H] CAC His [H] CAA Gln [Q] CAG Gln [Q]
CGT Arg [R] CGC Arg [R] CGA Arg [R] CGG Arg [R]
ATT Ile [I] ATC Ile [I] ATA Ile [I] ATG Met [M]
ACT Thr [T] ACC Thr [T] ACA Thr [T] ACG Thr [T]
AAT Asn [N] AAC Asn [N] AAA Lys [K] AAG Lys [K]
AGT Ser [S] AGC Ser [S] AGA Arg [R] AGG Arg [R]
GTT Val [V] GTC Val [V] GTA Val [V] GTG Val [V]
GCT Ala [A] GCC Ala [A] GCA Ala [A] GCG Ala [A]
GAT Asp [D] GAC Asp [D] GAA Glu [E] GAG Glu [E]
GGT Gly [G] GGC Gly [G] GGA Gly [G] GGG Gly [G]
(The above table imported from http://psyche.uthct.edu/shaun/SBlack/geneticd.html, which also has a nice introduction to the genetic code.)
Another version of the same table with nifty Java features and DNA database lookups can be found here.
The thing which is most relevant to the following discussion is the fact that most of the genetic codes are "redundant" -- more than one codon (triplet) encodes to exactly the same amino acid. This means that even in genes which are required for the organism, certain basepair mutations make absolutely no difference if the change is from one codon which maps into amino acid X to another codon which still maps into amino acid X. (This fact allows certain kinds of evolutionary "tracers" to be "read" from the DNA, as described here).
Now back to our DNA data. The redundancy in the genetic code means that some basepair sites will have more "degrees of freedom" than others (i.e., ways in which they can mutate without disrupting the gene's biological function in any way). Let's look at the four species with working GLO genes again:TAC CCC GTG GAG GTG CGC TTC ACT CGG GCG GAC GAC ATC CTG CTG AGC CCC PIG TAC CCC GTG GAG GTA CGC TTC ACT CGC GGG GAC GAC ATC CTG CTG AGC CCC BOS TAC CCC GTA GAG GTG CGC TTC ACC CGA GGC GAT GAC ATT CTG CTG AGC CCC RAT TAC CCC GTG GAG GTG CGC TTC ACC CGA GGT GAT GAC ATC CTG CTG AGC CCG MOUSE T T T A T A T T T A T C C T T T T T T T T A A A A A C A A A A A G C G G G G G C C C --- --- --1 --- --1 --- --- --1 --2 -12 --1 --- --1 --- --- --- --1Under each site of the mouse DNA, I've listed the "alternative" bases which could be be substituted for the mouse base at that site WITHOUT ALTERING THE GENE'S FUNCTION (because of genetic code redundancy). And under that I show the "mutation map" of just those four species.
Note that most of the "alternative" bases are in the third base of each codon, *and* that this is where all but one of the mutations have appeared. This is because these were the sites which were "free" to mutate in the way they did, because the mutation was genetically neutral. That doesn't mean that the first and second sites of each codon were immune from mutation, it's just that when mutations did occur at those sites, natural selection weeded them out quickly because they most likely "broke" the GLO gene for the individuals which received that mutuation. What we see above is the results after natural selection has already "filtered" the undesirable mutations and left the ones which "do no harm".
Additionally, the two sites which have mutated twice (i.e. have a "2" in the mutation map) are ones which had more "allowable" mutations. Also note that the sites which had the fewest allowable alternatives (only one alternate letter allowed) didn't have any mutations fix at those sites, which is unsurprising since a "safe" mutation would be less likely to occur there versus a site that "allowed" two or three alternatives.
All this is as predicted by evolutionary theory, you'll note.
It also explains the one anomoly of the original mutation map, which is that the mutation counts do tend to be higher at the third base of a codon.
However... What about the one exception? The pig DNA has had one mutation at a site which does not encode to exactly the same amino acid (which is the case for *all* the other ones). In the pig DNA, the GGG codon (mapping to Glycine) has changed to a GCG codon (mapping to Alanine). What's up with that? Well, one of two things. First and most likely, just as base values in codons have a built-in redundancy, so do the amino acids which make up the proteins which result from the DNA templates. In other words, certain amino acids can be substituted for other ones at some sites in given proteins without making any functional difference. (This "protein functional redundancy" also has implications for "evolutionary tracer" analysis, see here.) That may well be the case for Alanine versus Glycine in the GLO protein, but I'm not enough of a biochemist to be able to say. The other option is that it *does* make some difference in the function of the pig GLO protein, but not enough to "break" the vitamin-C synthesis (as proven by the fact that pigs *can* synthesize vitamin C). So one way or another, it's not a deal-breaker even though pig GLO will not be 100% identical to cow/mouse/rat GLO. It's yet another "allowable" mutation.
More interesting evolutionary observations: The number of mutational differences between pig/cow is 3, the number between mouse/rat is 4, and the difference between rat/cow is 7 -- all roughly as one would expect from the evolutionary relatedness of these animals (cows/pigs and rats/mice are each closer to each other than the rodents are to the ungulates).
Now let's take a close look at the guinea pig:TAC CCT GTG GGG GTG CGC TTC ACC CGG GGG GAC GAC ATC CTG CTG AGC CCC GUIN PIG --- --1 --- -1- --- --- --- --- --1 --1 --1 --- --- --- --- --- ---The "mutation map" under the guinea pig DNA is compared to the mouse DNA. Fascinating: Note that four of the five mutations are in the third base of a codon, *and* are of the type "allowed" by the genetic code redundancy. This indicates strongly that most of the evolutionary divergence between guinea pigs and mice likely occurred while the guinea pig's ancestors still had a working GLO gene. This is the sort of prediction implied by the evolutionary theory which could be cross-checked by further research of various types, and if verified, would be yet further confirmation that evolutionary theory is likely correct. So far, evolutionary theory has been subjected to literally countless tests like this, large and small, and the vast majority of results have confirmed the evolutionary prediction. This track record is hard to explain if evolution is an invalid theory, as some assert...
Finally, let's look over the primate DNA and mutation map (relative to each other):TAC CTG GTG GGG GTA CGC TTC ACC TGG AG* GAT GAC ATC CTA CTG AGC CCC HUMAN TAC CTG GTG GGG CTA CGC TTC ACC TGG AG* GAT GAC ATC CTA CTG AGC CCC CHIMPANZEE TAC CCG GTG GGG GTG CGC TTC ACC CAG AG* GAT GAC GTC CTA CTG AGC CCC ORANGUTAN TAA CCG GTG GGG GTG CGC TTC ACC CAA GG* GAT GAC ATC ATA CTG AGC CCC MACAQUE --1 -1- --- --- 1-1 --- --- --- 111 1-- --- --- 1-- 1-- --- --- ---Evolutionary theory predicts that because the GLO gene is "broken" in primates (i.e. is a pseudogene), mutations in it are highly likely to be neutral (i.e., make no difference, since it can't get much more broken), and thus mutations are just as likely to accumulate at any site as any other. Is that what we see? Yup. There's no obvious pattern to the mutations between primates in the above mutation map, and unlike the pig/cow/mouse/rat mutation map, the mutations aren't predominantly at the "safer" third base of a codon, nor of a type that would be "safe". In fact, one base has vanished entirely, but no biggie, the gene's already broken.
Also, although primates share a more recent common ancestor than cows/pigs/mice/rats, note that they've already racked up almost as many relative mutations as the cow/pig/mouse/rat DNA. This too is just as evolutionary theory predicts, because many mutations in a functional gene (GLO in this case) will be "non-safe" and weeded out by natural selection, making for a slower mutation fixation rate overall than in a pseudogene (as GLO is in primates) where natural selection doesn't "care" about the vast majority of mutations since *most* are neutral. So pseudogenes accumulate mutations faster than functional genes (even though rate of mutation *occurence* in both are likely the same).
Finally, note that there are ZERO mutational differences between the human DNA and the chimpanzee DNA, our nearest living relative.
I also see some interesting implications in the DNA sequences concerning which specific mutation fixed during what branch of the common-descent evolutionary tree for all the species represented, but reconstructing that would not only take another couple hours, at least, but would be a major bear to code in HTML, since I'd have to draw trees with annotations on the nodes... Bleugh.
In any case, I hope I've clarified some of the methods by which biologists find countless confirmations of evolution in DNA data. This is just a "baby" example, and to be more statistically valid would have to be done over much vaster sections of DNA sequences, but my intent was to demonstrate some of the concepts.
And if such a small amount of DNA as this can make small confirmations of evolutionary predictions, imagine the amount of confirmation from billion-basepair DNA data from each species compared across thousands of species... The amount of confirmatory discoveries for evolution from DNA analysis has already been vast, and promises to only grow in the future. For an overview of some of the different lines of evidence being studied, see The Journal of Molecular Evolution -- abstracts of all articles, current and back issues, can be browsed free online.
Theropod dinosaur to bird evolutionary transition:
The cladogram for the evolution of flight looks like this:
(Note -- each name along the top is a known transitional fossil; and those aren't all that have been discovered.) Here's a more detailed look at the middle section:
Fossils discovered in the past ten years in China have answered most of the "which came first" questions about the evolution of birds from dinosaurs.
We now know that downy feathers came first, as seen in this fossil of Sinosauropteryx:
That's a close-up of downy plumage along the backbone. Here's a shot of an entire fossil
Sinosauropteryx was reptilian in every way, not counting the feathers. It had short forelimbs, and the feathers were all the same size. Presumably, the downy feathers evolved from scales driven by a need for bodily insulation.
Next came Protarchaeopteryx:
It had long arms, broad "hands", and long claws:
Apparently this species was driven by selection to develop more efficient limbs for grasping prey. One of the interesting things about this species is that the structure of the forelimb has been refined to be quite efficient at sweeping out quickly to grab prey, snap the hands together, then draw them back towards the body (mouth?). The specific structures in question are the semilunate carpal (a wrist bone), that moves with the hand in a broad, flat, 190 degree arc, heavy chest muscles, bones of the arm which link together with the wrist so as to force the grasping hands to spread out toward the prey during the forestroke and fold in on the prey during the upstroke. Not only is this a marvelously efficient prey-grabbing mechanism, but the same mechanism is at the root of the wing flight-stroke of modern birds. Evolution often ends up developing a structure to serve one need, then finds it suitable for adaptation to another. Here, a prey-grasping motion similar in concept to the strike of a praying mantis in a reptile becomes suitable for modifying into a flapping flight motion.
Additionally, the feathers on the hands and tail have elongated, becoming better suited for helping to sweep prey into the hands.
Next is Caudipteryx:
This species had hand and tail feathers even more developed than the previous species, and longer feathers, more like that of modern birds:
However, it is clear that this was still not a free-flying animal yet, because the forelimbs were too short and the feathers not long enough to support its weight, and the feathers were symmetrical (equal sized "fins" on each side of the central quill). It also had very reduced teeth compared to earlier specimens and a stubby beak:
But the elongation of the feathers indicates some aerodynamic purpose, presumably gliding after leaping (or falling) from trees which it had climbed with its clawed limbs, in the manner of a flying squirrel. Feathers which were developed "for" heat retention and then pressed into service to help scoop prey were now "found" to be useful for breaking falls or gliding to cover distance (or swooping down on prey?).
Next is Sinornithosaurus:
Similar to the preceding species, except that the pubis bone has now shifted to point to the back instead of the front, a key feature in modern birds (when compared to the forward-facing publis bone in reptiles). Here are some of the forearm feathers in detail:
Long feathers in detail:
Artists' reconstruction:
Next is Archaeopteryx:
The transition to flight is now well underway. Archaeopteryx has the reversed hallux (thumb) characteristic of modern birds, and fully developed feathers of the type used for flight (long, aligned with each other, and assymetrical indicating that the feathers have been refined to function aerodynamically). The feathers and limbs are easily long enough to support the weight of this species in flight. However, it lacks some structures which would make endurance flying more practical (such as a keeled sternum for efficient anchoring of the pectoral muscles which power the downstroke) and fused chest vertebrae. Archaeopteryx also retains a number of clearly reptilian features still, including a clawed "hand" emerging from the wings, small reptilian teeth, and a long bony tail. After the previous species' gliding abilities gave it an advantage, evolution would have strongly selected for more improvements in "flying" ability, pushing the species towards something more resembling sustained powered flight.
Next is Confuciusornis:
This species had a nearly modern flight apparatus. It also displays transitional traits between a reptilian grasping "hand" and a fully formed wing as in modern birds -- the outer two digits (the earlier species had three-fingered "hands") in Confuciusornis are still free, but the center digit has now formed flat, broad bones as seen in the wings of modern birds.
Additionally, the foot is now well on its way towards being a perching foot as in modern birds:
It also has a keeled sternum better suited for long flight, and a reduced number of vertebrae in the tail, on its way towards becoming the truncated tail of modern birds (which while prominent, is a small flap of muscle made to look large only because of the long feathers attached).
From this species it's only a small number of minor changes to finish the transition into the modern bird family.
(Hey, who said there are no transitional fossils? Oh, right, a lot of dishonest creationists. And there are a lot more than this, I've just posted some of the more significant milestones.)
There's been a very recent fossil find along this same lineage, too new for me to have found any online images to include in this article. And analysis is still underway to determine exactly where it fits into the above lineage. But it has well-formed feathers, which extend out from both the "arms" and the legs. Although it wasn't advanced enough to fully fly, the balanced feathering on the front and back would have made it ideally suited for gliding like a flying squirrel, and it may be another link between the stage where feathers had not yet been pressed into service as aerodynamic aids, and the time when they began to be used more and more to catch the air and developing towards a "forelimbs as wings" specialization.
So in short, to answer your question about how flight could have developed in birds, the progression is most likely some minor refinement on the following:
1. Scales modified into downy feathers for heat retention.
2. Downy feathers modified into "straight" feathers for better heat retention (modern birds still use their body "contour feathers" in this fashion).
3. Straight feathers modified into a "grasping basket" on the hands (with an accompanying increase in reach for the same purpose).
4. Long limbs with long feathers refined to better survive falls to the ground.
5. "Parachute" feathers refined for better control, leading to gliding.
6. Gliding refined into better controlled, longer gliding.
7. Long gliding refined into short powered "hops".
8. Short powered flight refined into longer powered flight.
9. Longer powered flight refined into long-distance flying.
Note that in each stage, the current configuration has already set the stage for natural selection to "prefer" individuals which better meet the requirements of the next stage. Evolution most often works like this; by taking some pre-existing ability or structure, and finding a better use for it or a better way to make it perform its current use.
The evidenceThe paleontological (i.e. fossil) evidence for evolutionary transitions is overwhelming to anyone who has actually examined the evidence with an open mind. However, a stubborn person attempting to deny the obvious can rationalize it away by refusing to see the clear sequences of morphological change, and insisting that one can't "prove" that the various fossil specimens are "really" necessarily related. That excuse crumbles when one compares the fossil evidence to the *many* other independent lines of evidence which confirm the fossil evidence. For example, concerning whale evolution:
The evidence that whales descended from terrestrial mammals is here divided into nine independent parts: paleontological, morphological, molecular biological, vestigial, embryological, geochemical, paleoenvironmental, paleobiogeographical, and chronological. Although my summary of the evidence is not exhaustive, it shows that the current view of whale evolution is supported by scientific research in several distinct disciplines.
1. Paleontological evidence
The paleontological evidence comes from studying the fossil sequence from terrestrial mammals through more and more whale-like forms until the appearance of modern whales. Although the early whales (Archaeocetes) exhibit greater diversity than I have space to discuss here, the examples in this section represent the trends that we see in this taxon. Although there are two modern suborders of whales (Odontocetes and Mysticetes), this discussion will focus on the origin of the whales as an order of mammals, and set aside the issues related to the diversification into suborders.
We start with Sinonyx, a wolf-sized mesonychid (a primitive ungulate from the order Condylarthra, which gave rise to artiodactyls, perissodactyls, proboscideans, and so on) from the late Paleocene, about 60 million years ago. The characters that link Sinonyx to the whales, thus indicating that they are relatives, include an elongated muzzle, an enlarged jugular foramen, and a short basicranium (Zhou and others 1995). The tooth count was the primitive mammalian number (44); the teeth were differentiated as are the heterodont teeth of today's mammals. The molars were very narrow shearing teeth, especially in the lower jaw, but possessed multiple cusps. The elongation of the muzzle is often associated with hunting fish - all fish-hunting whales, as well as dolphins, have elongated muzzles. These features were atypical of mesonychids, indicating that Sinonyx was already developing the adaptations that later became the basis of the whales' specialized way of life.
The next fossil in the sequence, Pakicetus, is the oldest cetacean, and the first known archaeocete. It is from the early Eocene of Pakistan, about 52 million years ago (Gingerich and others 1983). Although it is known only from fragmentary skull remains, those remains are very diagnostic, and they are definitely intermediate between Sinonyxand later whales. This is especially the case for the teeth. The upper and lower molars, which have multiple cusps, are still similar to those of Sinonyx, but the premolars have become simple triangular teeth composed of a single cusp serrated on its front and back edges. The teeth of later whales show even more simplification into simple serrated triangles, like those of carnivorous sharks, indicating that Pakicetus's teeth were adapted to hunting fish.
Gingrich and others (1983) published this reconstruction of the skull of
Pakicetus inachus (redrawn for RNCSE by Janet Dreyer).
A well-preserved cranium shows that Pakicetus was definitely a cetacean with a narrow braincase, a high, narrow sagittal crest, and prominent lambdoidal crests. Gingerich and others (1983) reconstructed a composite skull that was about 35 centimeters long. Pakicetus did not hear well underwater. Its skull had neither dense tympanic bullae nor sinuses isolating the left auditory area from the right one - an adaptation of later whales that allows directional hearing under water and prevents transmission of sounds through the skull (Gingerich and others 1983). All living whales have foam-filled sinuses along with dense tympanic bullae that create an impedance contrast so they can separate sounds arriving from different directions. There is also no evidence in Pakicetus of vascularization of the middle ear, which is necessary to regulate the pressure within the middle ear during diving (Gingerich and others 1983). Therefore, Pakicetus was probably incapable of achieving dives of any significant depth. This paleontological assessment of the ecological niche of Pakicetus is entirely consistent with the geochemical and paleoenvironmental evidence. When it came to hearing, Pakicetus was more terrestrial than aquatic, but the shape of its skull was definitely cetacean, and its teeth were between the ancestral and modern states.
Zhou and others (1995) published this reconstruction of the skull of
Sinonyx jiashanensis (redrawn for RNCSE by Janet Dreyer).
In the same area that Pakicetus was found, but in sediments about 120 meters higher, Thewissen and colleagues (1994) discovered Ambulocetus natans, "the walking whale that swims", in 1992. Dating from the early to middle Eocene, about 50 million years ago, Ambulocetus is a truly amazing fossil. It was clearly a cetacean, but it also had functional legs and a skeleton that still allowed some degree of terrestrial walking. The conclusion that Ambulocetus could walk by using the hind limbs is supported by its having a large, stout femur. However, because the femur did not have the requisite large attachment points for walking muscles, it could not have been a very efficient walker. Probably it could walk only in the way that modern sea lions can walk - by rotating the hind feet forward and waddling along the ground with the assistance of their forefeet and spinal flexion. When walking, its huge front feet must have pointed laterally to a fair degree since, if they had pointed forward, they would have interfered with each other.
The forelimbs were also intermediate in both structure and function. The ulna and the radius were strong and capable of carrying the weight of the animal on land. The strong elbow was strong but it was inclined rearward, making possible rearward thrusts of the forearm for swimming. However, the wrists, unlike those of modern whales, were flexible.
It is obvious from the anatomy of the spinal column that Ambulocetus must have swum with its spine swaying up and down, propelled by its back feet, oriented to the rear. As with other aquatic mammals using this method of swimming, the back feet were quite large. Unusually, the toes of the back feet terminated in hooves, thus advertising the ungulate ancestry of the animal. The only tail vertebra found is long, making it likely that the tail was also long. The cervical vertebrae were relatively long, compared to those of modern whales; Ambulocetus must have had a flexible neck.
Ambulocetus's skull was quite cetacean (Novacek 1994). It had a long muzzle, teeth that were very similar to later archaeocetes, a reduced zygomatic arch, and a tympanic bulla (which supports the eardrum) that was poorly attached to the skull. Although Ambulocetus apparently lacked a blowhole, the other skull features qualify Ambulocetus as a cetacean. The post-cranial features are clearly in transitional adaptation to the aquatic environment. Thus Ambulocetus is best described as an amphibious, sea-lion-sized fish-eater that was not yet totally disconnected from the terrestrial life of its ancestors.
In the middle Eocene (46-7 million years ago) Rodhocetus took all of these changes even further, yet still retained a number of primitive terrestrial features (Gingerich and others 1994). It is the earliest archaeocete of which all of the thoracic, lumbar, and sacral vertebrae have been preserved. The lumbar vertebrae had higher neural spines than in earlier whales. The size of these extensions on the top of the vertebrae where muscles are attached indicate that Rodhocetus had developed a powerful tail for swimming.
Gingrich and others (1994) published this reconstruction of the skeleton of
Rodhocetus kasrani (redrawn for RNCSE by Janet Dreyer).
Elsewhere along the spine, the four large sacral vertebrae were unfused. This gave the spine more flexibility and allowed a more powerful thrust while swimming. It is also likely that Rodhocetus had a tail fluke, although such a feature is not preserved in the known fossils: it possessed features - shortened cervical vertebrae, heavy and robust proximal tail vertebrae, and large dorsal spines on the lumbar vertebrae for large tail and other axial muscle attachments - that are associated in modern whales with the development and use of tail flukes. All in all, Rodhocetus must have been a very good tail-swimmer, and it is the earliest fossil whale committed to this manner of swimming.
The pelvis of Rodhocetus was smaller than that of its predecessors, but it was still connected to the sacral vertebrae, meaning that Rodhocetus could still walk on land to some degree. However, the ilium of the pelvis was short compared to that of the mesonychids, making for a less powerful muscular thrust from the hip during walking, and the femur was about 1/3 shorter than Ambulocetuss, so Rodhocetus probably could not get around as well on land as its predecessors (Gingerich and others 1994).
Rodhocetus's skull was rather large compared to the rest of the skeleton. The premaxillae and dentaries had extended forward even more than its predecessors, elongating the skull and making it even more cetacean. The molars have higher crowns than in earlier whales and are greatly simplified. The lower molars are higher than they are wide. There is a reduced differentiation among the teeth. For the first time, the nostrils have moved back along the snout and are located above the canine teeth, showing blowhole evolution. The auditory bullae are large and made of dense bone (characteristics unique to cetaceans), but they apparently did not contain the sinuses typical of later whales, making it questionable whether Rodhocetus possessed directional hearing underwater.
Overall, Rodhocetus showed improvements over earlier whales by virtue of its deep, slim thorax, longer head, greater vertebral flexibility, and expanded tail-related musculature. The increase in flexibility and strength in the back and tail with the accompanying decrease in the strength and size of the limbs indicated that it was a good tail-swimmer with a reduced ability to walk on land.
The particularly well-known fossil whale Basilosaurus represents the next evolutionary grade in whale evolution (Gingerich 1994). It lived during the late Eocene and latest part of the middle Eocene (35-45 million years ago). Basilosaurus was a long, thin, serpentine animal that was originally thought to have been the remains of a sea serpent (hence it is name, which actually means "king lizard"). Its extreme body length (about 15 meters) appears to be due to a feature unique among whales; its 67 vertebrae are so long compared to other whales of the time and to modern whales that it probably represents a specialization that sets it apart from the lineage that gave rise to modern whales.
What makes Basilosaurus a particularly interesting whale, however, is the distinctive anatomy of its hind limbs (Gingerich and others 1990). It had a nearly complete pelvic girdle and set of hindlimb bones. The limbs were too small for effective propulsion, less than 60 cm long on this 15-meter-long animal, and the pelvic girdle was completely isolated from the spine so that weight-bearing was impossible. Reconstructions of the animal have placed its legs external to the body - a configuration that would represent an important intermediate form in whale evolution.
Although no tail fluke has ever been found (since tail flukes contain no bones and are unlikely to fossilize), Gingerich and others (1990) noted that Basilosaurus's vertebral column shares characteristics of whales that do have tail flukes. The tail and cervical vertebrae are shorter than those of the thoracic and lumbar regions, and Gingerich and others (1990) take these vertebral proportions as evidence that Basilosaurus probably also had a tail fluke.
Further evidence that Basilosaurus spent most of its time in the water comes from another important change in the skull. This animal had a large single nostril that had migrated a short distance back to a point corresponding to the back third of the dental array. The movement from the forward extreme of the snout to the a position nearer the top of the head is characteristic of only those mammals that live in marine or aquatic environments.
Dorudon was a contemporary of Basilosaurus in the late Eocene (about 40 million years ago) and probably represents the group most likely to be ancestral to modern whales (Gingerich 1994). Dorudon lacked the elongated vertebrae of Basilosaurus and was much smaller (about 4-5 meters in length). Dorudons dentition was similar to Basilosauruss; its cranium, compared to the skulls of Basilosaurus and the previous whales, was somewhat vaulted (Kellogg 1936). Dorudon also did not yet have the skull anatomy that indicates the presence of the apparatus necessary for echolocation (Barnes 1984).
Gingrich and Uhen (1996) published this reconstruction of the skeleton of
Dorudon atrox (redrawn for RNCSE by Janet Dreyer).
Basilosaurus and Dorudon were fully aquatic whales (like Basilosaurus, Dorudon had very small hind limbs that may have projected slightly beyond the body wall). They were no longer tied to the land; in fact, they would not have been able to move around on land at all. Their size and their lack of limbs that could support their weight made them obligate aquatic mammals, a trend that is elaborated and reinforced by subsequent whale taxa.
Clearly, even if we look only at the paleontological evidence, the creationist claim of "No fossil intermediates!" is wrong. In fact, in the case of whales, we have several, beautifully arranged in morphological and chronological order.
In summarizing the paleontological evidence, we have noted the consistent changes that indicate a series of adaptations from more terrestrial to more aquatic environments as we move from the most ancestral to the most recent species. These changes affect the shape of the skull, the shape of the teeth, the position of the nostrils, the size and structure of both the forelimbs and the hindlimbs, the size and shape of the tail, and the structure of the middle ear as it relates to directional hearing underwater and diving. The paleontological evidence records a history of increasing adaptation to life in the water - not just to any way of life in the water, but to life as lived by contemporary whales.
Evolution of whales from terrestrial mammals(From Plagiarized Errors and Molecular Genetics)That's just a quick layman-level overview of *one* of the many ways that whale evolution has been verified. For more technical examinations along several independent lines of evidence, see for example:
A particularly impressive example of shared retroposons has recently been reported linking cetaceans (whales, dolphins and porpoises) to ruminants and hippopotamuses, and it is instructive to consider this example in some detail. Cetaceans are sea-living animals that bear important similarities to land-living mammals; in particular, the females have mammary glands and nurse their young. Scientists studying mammalian anatomy and physiology have demonstrated greatest similarities between cetaceans and the mammalian group known as artiodactyls (even-toed ungulates) including cows, sheep, camels and pigs. These observations have led to the evolutionist view that whales evolved from a four-legged artiodactyl ancestor that lived on land. Creationists have capitalized on the obvious differences between the familiar artiodactyls and whales, and have ridiculed the idea that whales could have had four-legged land-living ancestors. Creationists who claim that cetaceans did not arise from four-legged land mammals must ignore or somehow dismiss the fossil evidence of apparent whale ancestors looking exactly like one would predict for transitional species between land mammals and whales--with diminutive legs and with ear structures intermediate between those of modern artiodactyls and cetaceans (Nature 368:844,1994; Science 263: 210, 1994). (A discussion of fossil ancestral whale species with references may be found at http://www.talkorigins.org/faqs/faq-transitional/part2b.html#ceta) Creationists must also ignore or dismiss the evidence showing the great similarity between cetacean and artiodactyl gene sequences (Molecular Biology & Evolution 11:357, 1994; ibid 13: 954, 1996; Gatesy et al, Systematic Biology 48:6, 1999).
Recently retroposon evidence has solidified the evolutionary relationship between whales and artiodactyls. Shimamura et al. (Nature 388:666, 1997; Mol Biol Evol 16: 1046, 1999; see also Lum et al., Mol Biol Evol 17:1417, 2000; Nikaido and Okada, Mamm Genome 11:1123, 2000) studied SINE sequences that are highly reduplicated in the DNA of all cetacean species examined. These SINES were also found to be present in the DNA of ruminants (including cows and sheep) but not in DNA of camels and pigs or more distantly related mammals such as horse, elephant, cat, human or kangaroo. These SINES apparently originated in a specific branch of ancestral artiodactyls after this branch diverged from camels, pigs and other mammals, but before the divergence of the lines leading to modern cetaceans, hippopotamus and ruminants. (See Figure 5.) In support of this scenario, Shimamura et al. identified two specific insertions of these SINES in whale DNA (insertions B and C in Figure 5) and showed that in DNA of hippopotamus, cow and sheep these same two sites contained the SINES; but in camel and pig DNA the same sites were "empty" of insertions. More recently, hippopotamus has been identified as the closest living terrestrial relative of cetaceans since hippos and whales share retroposon insertions (illustrated by D and E in Figure 5) that are not found in any other artiodactyls (Nikaido et al, PNAS 96:10261, 1999). The close hippo-whale relationship is consistent with previously reported sequence similarity comparisons (Gatesy, Mol Biol Evol 14:537, 1997) and with recent fossil finds (Gingerich et al., Science 293:2239, 2001; Thewissen et al., Nature 413:277, 2001) that resolve earlier paleontological conflicts with the close whale-hippo relationship. (Some readers have wondered: if ruminants are more closely related to whales than to pigs and camels, why are ruminants anatomically more similar to pigs and camels than they are to whales? Apparently this results from the fact that ruminants, pigs and camels changed relatively little since their last common ancestor, while the cetacean lineage changed dramatically in adapting to an aquatic lifestyle, thereby obliterating many of the features -- like hooves, fur and hind legs -- that are shared between its close ruminant relatives and the more distantly related pigs and camels. This scenario illustrates the fact that the rapid evolutionary development of adaptations to a new niche can occur through key functional mutations, leaving the bulk of the DNA relatively unchanged. The particularly close relationship between whales and hippos is consistent with several shared adaptations to aquatic life, including use of underwater vocalizations for communication and the absence of hair and sebaceous glands.) Thus, retroposon evidence strongly supports the derivation of whales from a common ancestor of hippopotamus and ruminants, consistent with the evolutionary interpretation of fossils and overall DNA sequence similarities. Indeed, the logic of the evidence from shared SINEs is so powerful that SINEs may be the best available characters for deducing species relatedness (Shedlock and Okada, Bioessays 22:148, 2000), even if they are not perfect (Myamoto, Curr. Biology 9:R816, 1999).
Figure 5. Specific SINE insertions can act as "tracers" that illuminate phylogenetic relationships. This figure summarizes some of the data on SINEs found in living artiodactyls and shows how the shared insertions can be interpreted in relation to evolutionary branching. A specific SINE insertion event ("A" in the Figure) apparently occurred in a primitive common ancestor of pigs, ruminants, hippopotamus and cetaceans, since this insertion is present in these modern descendants of that common ancestor; but it is absent in camels, which split off from the other species before this SINE inserted. More recent insertions B and C are present only in ruminants, hippopotamus and cetaceans. Insertions D and E are shared only by hippopotamus and cetaceans, thereby identifying hippopotamus as the closest living relative of cetaceans (at least among the species examined in these studies). SINE insertions F and G occurred in the ruminant lineage after it diverged from the other species; and insertions H and I occurred after divergence of the cetacean lineage.SINE Evolution, Missing Data, and the Origin of WhalesAnd much, much more.
Phylogenetic relationships among cetartiodactyls based on insertions of short and long interpersed elements: Hippopotamuses are the closest extant relatives of whales
Evidence from Milk Casein Genes that Cetaceans are Close Relatives of Hippopotamid Artiodactyls
Analyses of mitochondrial genomes strongly support a hippopotamus±whale clade
A new, diminutive Eocene whale from Kachchh (Gujarat, India) and its implications for locomotor evolution of cetaceans
A new Eocene archaeocete (Mammalia, Cetacea) from India and the time of origin of whales
Mysticete (Baleen Whale) Relationships Based upon the Sequence of the Common Cetacean DNA Satellite1
The Mitochondrial Genome of the Sperm Whale and a New Molecular Reference for Estimating Eutherian Divergence Dates
Limbs in whales and limblessness in other vertebrates: mechanisms of evolutionary and developmental transformation and loss
Eocene evolution of whale hearing
Novel Phylogeny of Whales Revisited but Not Revised
Land-to-sea transition in early whales: evolution of Eocene Archaeoceti (Cetacea) in relation to skeletal proportions and locomotion of living semiaquatic mammals
Subordinal artiodactyl relationships in the light of phylogenetic analysis of 12 mitochondrial protein-coding genes
New Morphological Evidence for the Phylogeny of Artiodactyla, Cetacea, and Mesonychidae
Phylogenetic Relationships of Artiodactyls and Cetaceans as Deduced from the Comparison of Cytochrome b and 12s rRNA Mitochondrial Sequences
Evolution of the vertebrate clotting mechanism
In detail, discuss why gradual evolution of blood clotting with 10 protein feedback loops all working at once is actually quite feasible evolutionarily speaking.
Well, okay, since you insist... Check out The Evolution of Vertebrate Blood Clotting, or The evolution of vertebrate blood coagulation as viewed from a comparison of puffer fish and sea squirt genomes. Excerpt from the latter paper:It is thought that 50100 million years separate the appearances of urochordates (which include the sea squirt) and vertebrates. During that time the machinery for thrombin-catalyzed fibrin formation had to be concocted by gene duplication and the shuffling about of key modular domains. The relative times of duplicative events can be estimated by various means, the most obvious being the presence or absence of a gene in earlier diverging organisms, although it must be kept in mind that lineages may lose genes. Another way to gauge events is from the relative positions of various gene products on phylogenetic trees, earlier branching implying earlier appearance. In this regard, (pro)thrombin invariably appears lower on the phylogenetic trees than do the other vitamin K-dependent factors (Fig. 2).Also, Evolution of enzyme cascades from embryonic development to blood coagulation:
The order of events can also be inferred by considering the most parsimonious route to assembling the various clusters of peripheral domains. Nine of the proteases under discussion can be accounted for by six domain-swapping events (Fig. 5). Indeed, the presence of a multiple-kringle protease in the sea squirt genome provides a reasonable model for a step-by-step parallel evolution of the clotting and lysis systems. It should be noted that a serine protease with only one kringle has been found in the ascidian Herdmania momus (36). Although numerous scenarios have been offered in the past about how modular exchange was involved in generating these schemes (refs. 4, 12, and 3741, inter alia), the new genomic data now provide a realistic set of starting materials.Recent delineation of the serine protease cascade controlling dorsal-ventral patterning during Drosophila embryogenesis allows this cascade to be compared with those controlling clotting and complement in vertebrates and invertebrates. The identification of discrete markers of serine protease evolution has made it possible to reconstruct the probable chronology of enzyme evolution and to gain new insights into functional linkages among the cascades. Here, it is proposed that a single ancestral developmental/immunity cascade gave rise to the protostome and deuterostome developmental, clotting and complement cascades. Extensive similarities suggest that these cascades were built by adding enzymes from the bottom of the cascade up and from similar macromolecular building blocks.That was the abstract. An excerpt from the text:The downstream protease of the vertebrate clotting cascade (Fig. 1d), thrombin, belongs to the same lineage as complement factors C1r and C1s. The upstream and middle proteases of the clotting cascade (factors VII, IX and X) belong to the most modern lineage, that of horseshoe crab clotting factor C. Therefore, the lineage of thrombin is parental to that of the upstream and middle proteases of the clotting cascade (Table 1) and distinguishes it from the other vitamin-K-dependent clotting proteases (factors VII, IX and X, and protein C). This conclusion agrees with sequence and species comparisons implying that thrombin was the ancestral blood-clotting protein [11]. It also suggests that vertebrate blood clotting emerged as a by-product of innate immunity, because the entire functional core of vertebrate clotting shares ancestry with complement proteases.And if that's not enough, you could check these out:Banyai, L., Varadi, A. and Patthy, L. (1983). Common evolutionary origin of the fibrin-binding structures of fibronectin and tissue-type plasminogen activator. FEBS Letters, 163(1): 37-41.
Bazan, J. F. (1990). Structural design and molecular evolution of a cytokine receptor superfamily. Proceedings of the National Academy of Sciences of the United States of America, 87(18): 6934-6938.
Blake, C. C. F., Harlos, K. and Holland, S. K. (1987). Exon and Domain Evolution in the Proenzymes of Blood Coagulation and Fibrinolysis. Cold Spring Harbor Symposia on Quantitative Biology: The Evolution of Catalytic Function, LII: 925-932.
Fornace AJ Jr, Cummings DE, Comeau CM, Kant JA, Crabtree GR. The Structure of the human gamma-fibrinogen gene. Alternate mRNA splicing near the 3' end of the gene produces gamma A and gamma B forms of gamma-fibrinogen. J Biol Chem. 1984 Oct 25;259(20):12826-30.
Crabtree, G. R., Comeau, C. M., Fowlkes, D. M., Fornace, A. J., Jr., Malley, J. D. and Kant, J. A. (1985). Evolution and structure of the fibrinogen genes: Random insertion of introns or selective loss? Journal of Molecular Biology, 185(1): 1-20.
Di Cera, E., Dang, Q. D. and Ayala, Y. M. (1997). Molecular mechanisms of thrombin function. Cell Mol Life Sci, 53(9): 701-730.
Doolittle, R. F. (1985). More homologies among the vertebrate plasma proteins. Biosci Rep, 5(10-11): 877-884.
Doolittle, R. F. (1990). The Structure and Evolution of Vertebrate Fibrinogen A Comparison of the Lamprey and Mammalian Proteins, in ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY: FIBRINOGEN, THROMBOSIS, COAGULATION, AND FIBRINOLYSIS. C. Y. Liu and Chien, S. New York, Plenum Press. 281.
Doolittle, R. F. (1992). A detailed consideration of a principal domain of vertebrate fibrinogen and its relatives. Protein Science, 1(12): 1563-1577.
Doolittle, R. F. (1992). Early Evolution of the Vertebrate Fibrinogen Molecule. Biophysical Journal, 61(2 PART 2): A410.
Doolittle, R. F. (1992). Stein and Moore Award address. Reconstructing history with amino acid sequences. Protein Science, 1(2): 191-200.
Doolittle, R. F. (1993). The Evolution of Vertebrate Blood Coagulation - a Case of Yin and Yang. Thrombosis and Haemostasis, V70(N1): 24-28.
Doolittle, R. F. and Feng, D. F. (1987). Reconstructing the Evolution of Vertebrate Blood Coagulation from a Consideration of the Amino Acid Sequences of Clotting Proteins. Cold Spring Harbor Symposia on Quantitative Biology: The Evolution of Catalytic Function, LII: 869-874.
Doolittle, R. F., G., Spraggon and J., Everse S. (1997). Evolution of vertebrate fibrin formation and the process of its dissolution. Ciba Found Symp, 212: 4-17; discussion 17-23.
Doolittle, R. F. and Riley, M. (1990). The amino-terminal sequence of lobster fibrinogen reveals common ancestry with vitellogenins. Biochemical and Biophysical Research Communications, 167(1): 16-19.
Edgington, T. S., Curtiss, L. K. and Plow, E. F. (1985). A linkage between the hemostatic and immune systems embodied in the fibrinolytic release of lymphocyte suppressive peptides. Journal of Immunology, 134(1): 471-477.
Ghidalia, W., Vendrely, R., Montmory, C., Coirault, Y., Samama, M., Lucet, B., Bellay, A. M. and Vergoz, D. (1989). Overall study of the in vitro plasma clotting system in an invertebrate, Liocarcinus puber (Crustacea Decapoda): Considerations on the structure of the Crustacea plasma fibrinogen in relation to evolution. Journal of Invertebrate Pathology, 53(2): 197-205.
Hervio, L. S., Coombs, G. S., Bergstrom, R. C., Trivedi, K., Corey, D. R. and Madison, E. L. (2000). Negative selectivity and the evolution of protease cascades: the specificity of plasmin for peptide and protein substrates. Chemistry & Biology, V7(N6): 443-452.
Hewett-Emmett, D., Czelusniak, J. and Goodman, M. (1981). The evolutionary relationship of the enzymes involved in blood coagulation and hemostasis. Annals of the New York Academy of Sciences, 370(20): 511-527.
Holland, S. K., Harlos, K. and Blake, C. C. F. (1987). Deriving the generic structure of the fibronectin type II domain from the prothrombin Kringle 1 crystal structure. EMBO (European Molecular Biology Organization) Journal, 6(7): 1875-1880.
Jordan, R. E. (1983). Antithrombin in vertebrate species: conservation of the heparin-dependent anticoagulant mechanism. Archives of Biochemistry and Biophysics, 227(2): 587-595.
Kant, J. A., Fornace, A. J., Jr., Saxe, D., Simon, M. J., McBride, O. W. and Crabtree, G. R. (1985). Evolution and organization of the fibrinogen locus on chromosome 4: Gene duplication accompanied by transposition and inversion. Proceedings of the National Academy of Sciences of the United States of America, 82(8): 2344-2348.
Kornblihtt, A. R., Pesce, C. G., Alonso, C. R., Cramer, P., Srebrow, A., Werbajh, S. and Muro, A. F. (1996). The fibronectin gene as a model for splicing and transcription studies. FASEB Journal, 10(2): 248-257.
Laki, K. (1972). Our ancient heritage in blood clotting and some of its consequences. Annals of the New York Academy of Sciences, 202(4): 297-307.
Neurath, H. (1984). Evolution of proteolytic enzymes. Science, 224(4647): 350-357.
Neurath, H. (1986). The Versatility of Proteolytic Enzymes. Journal of Cellular Biochemistry, 32(1): 35-50.
Oldberg, A. and Ruoslahti, E. (1986). Evolution of the fibronectin gene: Exon structure of cell attachment domain. Journal of Biological Chemistry, 261(5): 2113-2116.
Opal, S. M. (2000). Phylogenetic and functional relationships between coagulation and the innate immune response. Critical Care Medicine, V28(N9 SUPPS): S77-S80.
Pan, Y. and Doolittle, R. F. (1991). Distribution of Introns in Lamprey Fibrinogen Genes. Journal of Cellular Biochemistry Supplement(15 PART D): 75.
Pan, Y. and Doolittle, R. F. (1992). cDNA sequence of a second fibrinogen alpha chain in lamprey: an archetypal version alignable with full-length beta and gamma chains. Proceedings of the National Academy of Sciences of the United States of America, 89(6): 2066-2070.
Patthy, L. (1985). Evolution of the Proteases of Blood Coagulation and Fibrinolysis by Assembly from Modules. Cell, 41(3): 657-664.
Patthy, L. (1990). Evolution of blood coagulation and fibrinolysis. Blood Coagulation and Fibrinolysis, 1(2): 153-166.
Patthy, L. (1990). Evolutionary Assembly of Blood Coagulation Proteins. Seminars in Thrombosis and Hemostasis, 16(3): 245-259.
Patthy, L. (1999). Genome evolution and the evolution of exon-shufflinga review. Gene, 238(1): 103-114.
Roberts, Lewis R., Nichols, Lanita A. and Holland, Lene J. (1995). CDNA and amino-acid sequences and organization of the gene encoding the B-beta subunit of fibrinogen from Xenopus laevis. Gene (Amsterdam), 160(2): 223-228.
Sosnoski, D. M., Emanuel, B. S., Hawkins, A. L., Van Tuinen, P., Ledbetter, D. H., Nussbaum, R. L., Kaos, F. T., Schwartz, E., Phillips, D. and et al. (1988). Chromosomal localization of the genes for the vitronectin and fibronectin receptors .alpha. subunits and for platelet glycoproteins IIb and IIIa. Journal of Clinical Investigation, 81(6): 1993-1998.
Wang, Y. Z., Patterson, J., Gray, J. E., Yu, C., Cottrell, B. A., Shimizu, A., Graham, D., Riley, M. and Doolittle, R. F. (1989). Complete sequence of the lamprey fibrinogen .alpha. chain. Biochemistry, 28(25): 9801-9806.
Xu, X. and Doolittle, R. F. (1990). Presence of a vertebrate fibrinogen-like sequence in an echinoderm. Proceedings of the National Academy of Sciences of the United States of America, 87(6): 2097-2101.
Zhang, Y. L., Hervio, L., Strandberg, L. and Madison, E. L. (1999). Distinct contributions of residue 192 to the specificity of coagulation and fibrinolytic serine proteases. Journal of Biological Chemistry, V274(N11): 7153-7156.
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The 2.0-Å crystal structure of tachylectin 5A provides evidence for the common origin of the innate immunity and the blood coagulation systems
Davidson CJ, Tuddenham EG, McVey JH. 450 million years of hemostasis J Thromb Haemost. 2003 Jul;1(7):1487-94.
Reptile -> Mammal evolutionary transition:The above is from 29+ Evidences for Macroevolution, which compiles several hundred transitional fossils, which is itself just a *SMALL* sampling of the ENORMOUS numbers of fine transitional sequences found in the fossil record and well known to anyone who has bothered to CRACK OPEN A BOOK -- or even do a websearch -- in the past 25 years or so... So what's the anti-evolutionists' excuse for remaining abysmally ignorant of such things, and repeatedly making the false claim that there are "no" transitional fossils, etc.?
Example 2: reptile-mammals
Figure 1.4.1. The jaws of three vertebratesmammal, therapsid, and pelycosaur. A side view of three idealized skulls of mammals, therapsids (mammal-like reptiles), and pelycosaurs (early reptiles). The figure shows the differences between mammal and reptilian jaws and ear-bone structures. The jaw joint is shown as a large black dot, the quadrate (mammalian anvil or incus) is in turquoise, the articular (mammalian hammer or malleus) is in yellow, and the angular (mammalian tympanic annulus) is in pink. Note how, in the reptile, the jaw joint is formed between the blue quadrate and the yellow articular (with the pink angular close by), and how, in the mammal, the jaw joint is formed between the squamosal above and the dentary below. In the reptile, the squamosal is just above and contacting the quadrate. Advanced therapsids have two jaw joints: a reptile-like joint and a mammal-like joint (Figure based on Kardong 2002, pp. 275, reproduced with permission from the publisher, Copyright © 2002 McGraw-Hill)
We also have an exquisitely complete series of fossils for the reptile-mammal intermediates, ranging from the pelycosauria, therapsida, cynodonta, up to primitive mammalia (Carroll 1988, pp. 392-396; Futuyma 1998, pp. 146-151; Gould 1990; Kardong 2002, pp. 255-275). As mentioned above, the standard phylogenetic tree indicates that mammals gradually evolved from a reptile-like ancestor, and that transitional species must have existed which were morphologically intermediate between reptiles and mammalseven though none are found living today. However, there are significant morphological differences between modern reptiles and modern mammals. Bones, of course, are what fossilize most readily, and that is where we look for transitional species from the past. Osteologically, two major striking differences exist between reptiles and mammals: (1) reptiles have at least four bones in the lower jaw (e.g. the dentary, articular, angular, surangular, and coronoid), while mammals have only one (the dentary), and (2) reptiles have only one middle ear bone (the stapes), while mammals have three (the hammer, anvil, and stapes) (see Figure 1.4.1).
Early in the 20th century, developmental biologists discovered something that further complicates the picture. In the reptilian fetus, two developing bones from the head eventually form two bones in the reptilian lower jaw, the quadrate and the articular (see the Pelycosaur in Figure 1.4.1). Surprisingly, the corresponding developing bones in the mammalian fetus eventually form the anvil and hammer of the unique mammalian middle ear (also known more formally as the incus and malleus, respectively; see Figure 1.4.2) (Gilbert 1997, pp. 894-896). These facts strongly indicated that the hammer and anvil had evolved from these reptilian jawbonesthat is, if common descent was in fact true. This result was so striking, and the required intermediates so outlandish, that many anatomists had extreme trouble imagining how transitional forms bridging these morphologies could have existed while retaining function. Young-earth creationist Duane Gish stated the problem this way:
"All mammals, living or fossil, have a single bone, the dentary, on each side of the lower jaw, and all mammals, living or fossil, have three auditory ossicles or ear bones, the malleus, incus and stapes. ... Every reptile, living or fossil, however, has at least four bones in the lower jaw and only one auditory ossicle, the stapes. ... There are no transitional fossil forms showing, for instance, three or two jawbones, or two ear bones. No one has explained yet, for that matter, how the transitional form would have managed to chew while his jaw was being unhinged and rearticulated, or how he would hear while dragging two of his jaw bones up into his ear." (Gish 1978, p. 80)
Figure 1.4.2. A comparison of the ears of reptiles and mammals. The reptile ear is shown on the left, the mammal ear on the right. As in Figure 1.4.1, the quadrate (mammalian anvil or incus) is in turquoise and the articular (mammalian hammer or malleus) is in yellow. The stapes is shown in brown. Note how the relative arrangement of these bones is similar in both taxa, in the order of inner ear-stapes-quadrate-articular.
Gish was incorrect in stating that there were no transitional fossil forms, and he has been corrected on this gaff numerous times since he wrote these words. However, Gish's statements nicely delineate the morphological conundrum at hand. Let's review the required evolutionary conclusion. During their evolution, two mammalian middle ear bones (the hammer and anvil, aka malleus and incus) were derived from two reptilian jawbones. Thus there was a major evolutionary transition in which several reptilian jawbones (the quadrate, articular, and angular) were extensively reduced and modified gradually to form the modern mammalian middle ear. At the same time, the dentary bone, a part of the reptilian jaw, was expanded to form the major mammalian lower jawbone. During the course of this change, the bones that form the hinge joint of the jaw changed identity. Importantly, the reptilian jaw joint is formed at the intersection of the quadrate and articular whereas the mammalian jaw joint is formed at the intersection of the squamosal and dentary (see Figure 1.4.1).
How could hearing and jaw articulation be preserved during this transition? As clearly shown from the many transitional fossils that have been found (see Figure 1.4.3), the bones that transfer sound in the reptilian and mammalian ear were in contact with each other throughout the evolution of this transition. In reptiles, the stapes contacts the quadrate, which in turn contacts the articular. In mammals, the stapes contacts the incus, which in turn contacts the malleus (see Figure 1.4.2). Since the quadrate evolved into the incus, and the articular evolved into the malleus, these three bones were in constant contact during this impressive evolutionary change. Furthermore, a functional jaw joint was maintained by redundancyseveral of the intermediate fossils have both a reptilian jaw joint (from the quadrate and articular) and a mammalian jaw joint (from the dentary and squamosal). Several late cynodonts and Morganucodon clearly have a double-jointed jaw. In this way, the reptilian-style jaw joint was freed to evolve a new specialized function in the middle ear. It is worthy of note that some modern species of snakes have a double-jointed jaw involving different bones, so such a mechanical arrangement is certainly possible and functional.
Since Figure 1.4.3 was made, several important intermediate fossils have been discovered that fit between Morganucodon and the earliest mammals. These new discoveries include a complete skull of Hadrocodium wui (Luo et al. 2001) and cranial and jaw material from Repenomamus and Gobiconodon (Wang et al. 2001). These new fossil finds clarify exactly when and how the malleus, incus, and angular completely detached from the lower jaw and became solely auditory ear ossicles.
Recall that Gish stated: "There are no transitional fossil forms showing, for instance, three or two jawbones, or two ear bones" (Gish 1978, p. 80). Gish simply does not understand how gradual transitions happen (something he should understand, obviously, if he intends to criticize evolutionary theory). These fossil intermediates illustrate why Gish's statement is a gross mischaracterization of how a transitional form should look. In several of the known intermediates, the bones have overlapping functions, and one bone can be called both an ear bone and a jaw bone; these bones serve two functions. Thus, there is no reason to expect transitional forms with intermediate numbers of jaw bones or ear bones. For example, in Morganucodon, the quadrate (anvil) and the articular (hammer) serve as mammalian-style ear bones and reptilian jaw bones simultaneously. In fact, even in modern reptiles the quadrate and articular serve to transmit sound to the stapes and the inner ear (see Figure 1.4.2). The relevant transition, then, is a process where the ear bones, initially located in the lower jaw, become specialized in function by eventually detaching from the lower jaw and moving closer to the inner ear.
Figure 1.4.3. A comparison of the jawbones and ear-bones of several transitional forms in the evolution of mammals. Approximate stratigraphic ranges of the various taxa are indicated at the far left (more recent on top). The left column of jawbones shows the view of the left jawbone from the inside of the mouth. The right column is the view of the right jawbone from the right side (outside of the skull). As in Figure 1.4.1, the quadrate (mammalian anvil or incus) is in turquoise, the articular (mammalian hammer or malleus) is in yellow, and the angular (mammalian tympanic annulus) is in pink. For clarity, the teeth are not shown, and the squamosal upper jawbone is omitted (it replaces the quadrate in the mammalian jaw joint, and forms part of the jaw joint in advanced cynodonts and Morganucodon). Q = quadrate, Ar = articular, An = angular, I = incus (anvil), Ma = malleus (hammer), Ty = tympanic annulus, D = dentary. (Reproduced from Kardong 2002, pp. 274, with permission from the publisher, Copyright © 2002 McGraw-Hill)
Here's another look:(The above is from The Fossil Record: Evolution or "Scientific Creation", which is yet ANOTHER source the anti-evolutionists are obviously completely ignorant of -- not that that stops them from spouting off falsehoods about the subject anyway...
Mammal-Like Reptiles
As previously stated, a succession of transitional fossils exists that link reptiles (Class Reptilia) and mammals (Class Mammalia). These particular reptiles are classifie as Subclass Synapsida. Presently, this is the best example of th e transformation of one major higher taxon into another. The morphologic changes that took place are well documented by fossils, beginning with animals essentially 100% reptilian and resulting in animals essentially 100% mammalian. Therefore, I have chosen this as the example to summarize in more detail (Table 1, Fig. 1).
Skulls and jaws of synapsid reptiles and mammals; left column side view of skull; center column top view of skull; right column side view of lower jaw. Hylonomus modified from Carroll (1964, Figs. 2,6; 1968, Figs. 10-2, 10-5; note that Hylonomus is a protorothyrod, not a synapsid). Archaeothyris modified from Reisz (1972, Fig. 2). Haptodus modified from Currie (1977, Figs, 1a, 1b; 1979, Figs. 5a, 5b). Sphenacodo n modified from Romer & Price (1940, Fig. 4f), Allin (1975, p. 3, Fig. 16);note: Dimetrodon substituted for top view; modified from Romer & Price, 1940, pl. 10. Biarmosuchus modified from Ivakhnenko et al. (1997, pl. 65, Figs. 1a, 1B, 2); Alin & Hopson (1992; Fig. 28.4c); Sigogneau & Tchudinov (1972, Figs. 1, 15). Eoarctops modified from Broom (1932, Fig. 35a); Boonstra (1969, Fig. 18). Pristerognathus modified from Broom (1932, Figs 17a, b,c); Boonstra (1963, Fig. 5d). Procynosuchus modified from Allin & Hopson (1992, Fig. 28.4e); Hopson (1987, Fig. 5c); Brink (1963, Fig. 10a); Kemp (1979, Fig. 1); Allin (1975, p. 3, Fig. 14). Thrinaxodon modified from Allin & Hopson (1992, Fig. 28.4f);Parrington (1946, Fig. 1); Allin (1975, p. 3, Fig. 13). Probainognathus modified from Allin & Hopson (1992, Fig. 28.4g); Romer (1970, Fig. 1); Allin (1975, p. 3, Fig. 12). Morga nucodon modified from Kermack, Mussett, & Rigney (1981, Figs. 95, 99a; 1973, Fig. 7a); Allin (1975, p. 3, Fig. 11). Asioryctes modified from Carroll (1988, Fig. 20-3b). Abbreviations: ag = angular; ar = articular; cp = coronoid process; d = dentary; f = lateral temporal fenestra; j = jugal; mm = attachment site for mammalian jaw muscles; o = eye socket; po = post orbital; q = quadrate; rl = reflected lamina; sq = squamosal; ty = tympanic.
TAXONOMY LATERAL TEMPORAL FENESTRA LOWER JAW DENTARY TEETH LOWER JAW: POST- DENTARY BONES MIDDLE EAR & JAW ARTICULATION M: Early Placental mammals
Upper Cretaceous
Merged with eye socket; cheek arch bowed out laterally 100% of jaw length is the den- tary; condylar process in contact with squamosal Fully differentiated teeth; incisors, canines, premolars; one tooth replacement No post-dentary bones 3 middle ear bones (stapes, incus, malleus) + tympanic; squamosal-dentary jaw joint L: "Pantothere" mammals
Middle/Upper Jurassic
X 100% of jaw length is the den- tary; condylar process contacts squamosal Fully differentiated teeth; incisors, canines, premolars; one tooth replacement Post-dentary bones migrated to middle ear Probably 3 middle ear bones (stapes, incus, malleus) + tympanic; squamosal-dentary jaw joint K: Morganucodontid mammals
Morganucodon Upper Triassic & Lower Jurassic
Merged with eye socket; cheeck arch bowed out laterally 100% of jaw length is the den- tary; condylar process expanded posteriorly to make contact with squamosal Fully differentiated teeth; incisors, canines, premolars; one tooth replacement 20% of jaw length; reflected lamina decreased to narrow ribbon-like horseshoe Stapes extends from inner ear capsule to quadrate; quadrate tiny; both quadrate-articular and squamosal-dentary jaw joints J: Chiniquodontid cynodonts
Middle Triassic
Much larger than eye socket; 40- 45% of skull length; expanded posterioirly, medially, & laterally; midline of skull narrow sagittal crest; chek arch bowed out laterally 95% of jaw length is the dentary; large coronoid process expanded posteriorly; condylar process expanded posteriorly Large single canine; cheek teeth multicusped; tooth replacement reduced 20% of jaw length; angular notch widened ventrally; width of main part of angular decreased; reflec - ted lamina decreased to narrow ribbon-like horseshoe Stapes extends from inner ear capsule to quadrate; quadrate tiny; quadrate-articular joint I:Galesaurid cynodonts
Lower Triassic
Much larger than eye socket; 40% of skull length; expanded pos- terioirly, medially, & laterally; midline of skull narrow sagittal crest; chek arch bowed out laterally 85% of jaw length is the dentary; large coronoid process expanded to top of eye socket and pos- teriorly; jaw muscles attached to most of coronoid process Large single canine; cheek teeth multicusped; tooth replacement reduced 25% of jaw length; angular notch widened ventrally; width of reflec- ted lamina decreased; width of main part of angular decreased Stapes extends from inner ear capsule to quadrate; quadrate small; quadrate-articular jaw joint H: Procynosuchid cynodonts
upper Upper Permian
Much larger than eye socket; 40% of skull length; expanded pos- terioirly, medially, & laterally; midline of skull narrow sagittal crest; chek arch bowed out laterally 75-80% of jaw length is the den- tary; coronoid process expanded to near top of eye socket and posteriorly; jaw muscles attached to dorsal part of coronoid process Large single canine; cheek teeth multicusped 30% of jaw length; angular notch widened ventrally; width of reflected lamina decreased Stapes extends from inner ear capsule to quadrate; quadrate small; quadrate-articular jaw joint G: Early Therocephalians
lower Upper Permian
Larger than eye socket; expanded posteriorly and medially; 30% of skull length 75-80% of jaw length is the den- tary; posterior end of dentary expanded posteriorly and dorsally into narrow blade-like coronoid process; rises to middle of eye socket Large single canine; other teeth simple cones. 35% of jaw length; angular notch deepened into a cleft; reflected lamina large, broad, blade-like Stapes extends from inner ear capsule to quadrate; quadrate small; quadrate-articular jaw joint F: Early Gorgonopsians
lower Upper Permian
Slightly larger than eye socket; expanded posteriorly and medially (minimal); 20-25% of skull length 65-75% of jaw length is the den- tary; posterior end of dentary slightly expanded posteriorly and dorsally as incipient coronoid process Large single canine; other teeth simple cones. 40% of jaw length; angular notch deepened into a cleft; reflected lamina large, broad, blade-like Stapes extends from inner ear capsule to quadrate; quadrate- articular jaw joint E: Eotitanosuchians
Lower Permian
Small; slightly smaller than eye socket; slightly expanded posteriorly and medially 65-75% of jaw length is the den- tary; posterodorsal edge rises broadly but slightly above tooth row Large single canine; other teeth simple cones. 40% of jaw length; angular notch deepened into a cleft; reflected lamina large, broad, blade-like Stapes extends from inner ear capsule to quadrate; quadrate- articular jaw joint D: Late sphenacodonts
Upper Pennsylvanian
Small; smaller than eye socket; confined to one side of skull 65% of jaw length is the dentary; posterodorsal edge rises broadly but slightly above the tooth row Enlarged incipient canines; other teeth simple cones 60% of jaw length; venntral edge of angular notched ("angular notch") offsetting a short pro- tusion (reflected lamina) Stapes extends from inner ear capsule to quadrate; quadrate large and plate-like; quadrate- articular jaw joint C: Early spenacodonts
Upper Pennsylvanian
Tiny; smaller than eye socket; confined to one side of skull 65-75% of jaw length is the den- tary; posterodorsal edge rises broadly but slightly above tooth row Undifferentiated; slightly enlarged incipient canines just behind nares 70% of jaw length; ventral edge of angular with shallow indentation Stapes extends from inner ear capsule to quadrate; quadrate- articular jaw joint B: Early ophiacodonts
upper Middle Pennsylvanian
Tiny; smaller than eye socket; confined to one side of skull x Undifferentiated; slightly enlarged incipient canines just behind nares x Stapes extends from inner ear capsule to quadrate; quadrate- articular jaw joint A: Protorothyrids
lower Middle Pennsylvanian
Absent 65-75% of jaw length is the den- tary; posterodorsal edge rises broadly but slightly above tooth row Undifferentiated; slightly enlarged incipient canines just behind nares 70% of jaw length; ventral edge of angular continuous Stapes extends from inner ear capsule to quadrate; quadrate- articular jaw joint
Table 1: Morphology of synapsid reptiles and mammals (Note that Hylonomus is a protothyrid, not a synapsid). Data from references cited in text.
Modern reptiles and mammals are very distinctive, easily diagnosable, and do not intergrade. Reptiles are covered by scales, mammals by hair; reptiles are cold-blooded, mammals warm-blooded; reptiles do not suckle their young, mammals have mammary glands; reptiles have sprawling posture, mammals have upright posture. Most of these features are soft part anatomy or physiology that very rarely fossilize (although dinosaur skin impressions are known from Cretaceous sediments, and imprints of mammal hair are known from Eocene bats from Germany; Franzen, 1990). In the fossil record, we must look to skeletal features.
There are many skeletal features which allow us to distinguish the reptiles from the mammals (Carroll, 1988; Table 1, rows A, M). The single most important defining characteristic is the nature of the articulation of the lower jaw to the skull (Simpson, 1959). In reptiles, multiple bones comprise the lower jaw. A small bone at the posterior end of the lower jaw, the articular, articulates with the quadrate bone of the skull (Simpson, 1959; Carroll, 1988). In mammals, one large bone, the dentary, comprises the lower jaw. It articulates with the squamosal bone of the skull (Simpson, 1959; Carroll, 1988).
From comparative anatomy studies, it is certain that most of the bones of the reptiles and mammals are homologous (Crompton & Parker, 1978; Carroll, 1988). Of greatest importance, the middle ear bones of mammals (stapes, incus, malleus, and tympanic) are homologous with several of the skull and jaw bones of reptiles (stapes, quadrate, articular, and angular, respectively; Romer, 1956, p. 33-38, 1970a; Allin, 1975, 1986; Allin & Hopson, 1992; Crompton & Parker, 1978; Hopso n, 1987, 1994; Carroll, 1988). One group of reptiles, the synapsids (Subclass Synapsida), share with the mammals an additional homologous structure: the lateral temporal fenestra, which is an opening in the skull behind the eye socket at the triple junction between the squamosal, jugal , and post orbital bones (Broom, 1932; Frazetta, 1968; Kemp, 1982; Carroll, 1988). A band of bone composed of the jugal and the squamosal is adjacent to the lateral temporal fenestra (Broom, 1932; Kemp, 1982; Carroll, 1988). This is the cheek arch so characteristic of mammal skulls (Broom, 1932; Kemp, 1982; Carroll, 1988). Therefore, synapsids are commonly named the mammal-like reptiles.
The presence of diagnosable morphologic differences between reptiles (including the oldest reptiles and the oldest synapsids) and mammals distinguishes them as distinct taxa. This allows us to test evolution by looking for transitional forms between the two. Because many of the bones are homologous, we should find evidence illustrating how these bones were modified over time to become the new bones. Furthermore, these morphologic changes should happen in parallel and in geochronologic succession.
Synapsid reptiles inhabited Pangea from the Middle Pennsylvanian through the Early Jurassic (Kemp, 1982, 1985; Sloan, 1983; Carroll, 1988; Hopson, 1969, 1987, 1994; Hopson & Crompton, 1969; Hotton, et al., 1986; Crompton & Jenkins, 1973; Sidor & Hopson, 1998; Romer & Price, 1940; Broom, 1932; Boonstra, 1963, 1969, 1971; Tchudinov, 1983; Olson, 1944; Tatarinov, 1974; Vyushkov, 1955; Efremov, 1954). From the Early Permian through the Early Triassic, they were the largest and most abundant land animals (Sloan, 1983; Colbert, 1965). Though much less well known to the general public than dinosaurs, one of the cereal box dinosaurs, Dimetrodon (the sail-backed reptile), is a synapsid, not a dinosaur (Romer & Price, 1940; Carroll, 1988). The oldest mammals are Late Triassic (Kemp, 1982; Carroll, 1988). Below is a discussion of the geochronologic succession linking synapsids and mammals. The oldest reptiles (named protorothyrids; Carroll, 1964, 1988, p. 192-199) are from the lower Middle Pennsylvanian, and the oldest synapsids (Reisz, 1972) are from the upper Middle Pennsylvanian, both of Nova Scotia. Upper Pennsylvanian and Lower Permian forms are known primarily from the midcontinent and Permian Basin region of the United States (Romer & Price, 1940; Currie, 1977, 1979; Kemp, 1982; Sloan, 1983). The basal Upper Permian forms are known from Russia (Tchudinov, 1960, 1983; Efremov, 1954; Olson, 1962; Sigogneau & Tchudinov, 1972; Ivakhnenko et al., 1997). Most of the Upper Permian and Lower Triassic succession is known from southern Africa, especially the Great Karoo of South Africa (Broom, 1932; Boonstra, 1963, 1969, 1971; Hopson & Kitching, 1972; Kemp, 1982; Sloan, 1983). The Middle Triassic forms are from South America (Romer, 1969a, 1969b, 1970b, 1973; Romer & Lewis, 1973; Bonaparte & Barbarena, 1975), and the Upper Triassic and Lower Jurassic mammals are known from Eurasia (Kermack, Mussett, & Rigney, 1973, 1981; Kemp, 1982). Subsequent Mesozoic mammals are known from all over the world (Simpson, 1928; Lillegraven et al., 1979).
When placed in proper geochronologic succession, the synapsids naturally form a succession of taxa (genera and families) that progressively become more mammal-like and less reptile-like (Kemp, 1982, 1985; Sloan, 1983; Sidor & Hopson, 1998; Hopson, 1987, 1994). Morphologic changes, summarized in Table 1 and Figure 1, affect the entire skeletal anatomy of these animals, but are most clearly displayed in their skulls.
The lateral temporal fenestra increased in size from a tiny opening smaller than the eye socket to a giant opening occupying nearly half the length of the skull. Ultimately, it merged with the eye socket, thus producing the full development of the cheek arch so characteristic of mammals (Broom, 1932; Frazetta, 1968; Kemp, 1982; Sloan, 1983; Hopson, 1987, 1994; Carroll, 1988).
Successively, the relative proportion of the lower jaw comprised of the dentary bone (teeth-bearing bone) gradually increased until the entire lower jaw consisted of the dentary (Kemp, 1982; Sloan, 1983; Carroll, 1988; Hopson, 1987, 1994). In Pennsylvanian and Lower and basal Upper Permian synapsids, the postero-dorsal edge of the lower jaw rose broadly but only slightly above the level of the tooth row (Romer & Price, 1940; Currie, 1977, 1979; Ivakhnenko et al., 1997; Tchudinov, 1960, 1983; Efremov, 1954; Olson, 1962; Sigogneau & Tchudinov, 1972; Hopson, 1987, 1994). In succeeding forms, the posterior part of the dentary expanded dorsally and posteriorly as a blade-like process, and progressively became larger (Broom, 1932; Boonstra, 1963, 1969, 1971; Sigogneau, 1970; Brink, 1963; Kemp, 1979; Hopson, 1987, 1994), forming the coronoid process (Parrington, 1946; Fourie, 1974; Romer, 1969b, 1970b, 1973; Hopson, 1987, 1994) to which the mammalian-type jaw musculature is attached (Barghusen, 1968; Bramble, 1978; Crompton, 1972; Crompton & Parker, 1978; Kemp, 1982; Sloan, 1983; Carroll, 1988). Concomitantly, the post-dentary bones progressively reduced in size (Allin, 1975; Crompton, 1972; Crompton & Parker, 1978; Kemp, 1982; Sloan, 1983; Carroll, 1988; Hopson, 1987, 1994).
Beginning with the Upper Pennsylvanian sphenacodonts, a notch developed in the angular bone that offsets a projection, the reflected lamina (Allin, 1975; Allin & Hopson, 1992; Hopson, 1987, 1994; Romer & Price, 1940; Currie, 1977, 1979; Kemp, 1982; Sloan, 1983; Carroll, 1988). The reflected lamina first became a large blade-like flange (Allin, 1975; Allin & Hopson, 1992; Hopson, 1987, 1994; Ivakhnenko et al., 1997; Tchudinov, 1960, 1983; Efremov, 1954; Olson, 1962; Sigogneau & Tchudinov, 1972; Broom, 1932; Sigogneau, 1970; Boonstra, 1963, 1969, 1971), and then was progressively reduced to a delicate horseshoe-shaped bone (Allin, 1975; Allin & Hopson, 1992; Hopson, 1987, 1994; Brink, 1963; Parrington, 1946; Fourie, 1974; Romer, 1969b, 1970b, 1973; Kermack, Mussett, & Rigney, 1973, 1981; Kemp, 1979, 1982; Sloan, 1983; Carroll, 1988).
Simultaneously, the quadrate progressively decreased in size (Allin, 1975; Allin & Hopson, 1992; Hopson, 1987, 1994; Kemp, 1982; Sloan, 1983; Carroll, 1988). The articular did not decrease in size much, being small initially, but developed a downward-pointing prong (Allin, 1975; Allin & Hopson, 1992; Hopson, 1987, 1994; Kemp, 1982; Sloan, 1983; Carroll, 1988). In the synapsids, the lower jaw was hinged to the skull by the articular and quadrate bones (Crompton, 1972; Crompton & Parker, 1978; Allin, 1975; Allin & Hopson, 1992; Hopson, 1987, 1994). Thus they are classified as reptiles (Simpson, 1959; Kemp, 1982; Sloan, 1983; Carroll, 1988). As the quadrate and articular became smaller, they were relieved of their solid suture to the dentary and skull (Crompton, 1972; Allin, 1975, 1986; Allin & Hopson, 1992; Hopson, 1987, 1994; Crompton & Parker, 1978; Kemp, 1982; Sloan, 1983; Carroll, 1988). A projection of the dentary extended posteriorly and made contact with the squamosal. Morganucodon possessed the mammalian dentary-squamosal jaw joint adjacent to the reptilian articular-quadrate jaw joint (Kermack, Mussett, & Rigney, 1973, 1981; Carroll, 1988). It is classified as the first mammal, but it is a perfect intermediate. Now that a new jaw joint was established, the quadrate and articular were subsequently relieved of that function (Crompton, 1972; Allin, 1975, 1986; Allin & Hopson, 1992; Hopson, 1987, 1994; Crompton & Parker, 1978; Kemp, 1982; Sloan, 1983; Carroll, 1988). Ultimately, in Middle and Upper Jurassic mammals, the tiny quadrate, articular, and ring-like angular migrated as a unit to the middle ear where they joined the stapes and became the incus, malleus, and tympanic bones (Allin, 197 5, 1986; Allin & Hopson, 1992; Hopson, 1987, 1994; Kemp, 1982; Sloan, 1983; Carroll, 1988).
Progressively, the teeth became differentiated. The large canines developed first, followed by the development of multicusped cheek teeth, reduced tooth replacement (Osborn & Crompton, 1973; Crompton & Parker, 1978), and finally full y differentiated incisors, canines, premolars, and molars with one tooth replacement during life (Kemp, 1982; Hopson, 1994).
Many other morphologic changes are documented in the fossil record. These demonstrate the morphologic and geochronologic succession from sprawling limb posture to upright limb posture of mammals (Jenkins, 1971; Romer & Lewis, 197 3; Kemp, 1982; Carroll, 1988; Hopson, 1994). As Jenkins (1971, p. 210) stated, In details of morphology and function, the cynodont post-cranial skeleton should be regarded as neither reptilian nor mammalian but as transitional between the two classes . Other changes have been adequately summarized elsewhere (Kemp, 1982; Sloan, 1983; Carroll, 1988; Hopson, 1994). Obviously, fundamental physiologic changes must have taken place as well, many of which are not directly preserved in the fossil record, though some can be inferred from the skeletal anatomy (Findlay, 1968; Kemp, 1982; Sloan, 1983, Carroll, 1988; Hopson, 1994).
This is well documented in the fossil record by a massive volume of incontrovertible data that cannot be explained away. Such large-scale, progressive, continuous, gradual, and geochronologically successive morphologic change (Sidor & Hopson, 1998) is descent with modification, and provides compelling evidence for evolution on a grand scale.
Thanks - I'll expect a bonus check from Darwin Central after the usual bureaucratic hemming and hawing
Okay then, put forth the PROOF! Not rhetorical rants that border on zealous fanaticism...
Aside from "evolutionary" secular fundamentalism, help me understand what SPECIFIC tests have shown that order simply "accidentally" happens from disorder to the magnitude, or to the degree to create ONE SINGLE LIVING CELL! Forget about the trillions that make up a single human being, I'm looking for the "accidents" that are able to cause a cell to divide that doesn't even KNOW it needs to divide.
By the way, how many "accidental" cells DIED in the millenia and MILLIONS of years it took for the FIRST CELL to "figure out" that it NEEDED to divide to continue to exist?
And if so many cells were created so easily to be available for millions of years of "accidents" before they figured out they needed to divide to reproduce, then why is it so hard to create them from rocks, water, and a few chemicals today? Or did the "magical" evolutionary cells have a "magical" lifespan of millions of years until they just "accidentally figured out" how to divide?
You see, this is just ONE SILLY EXAMPLE OF HOW RIDICULOUS EVOLUTION IS! Biological systems contain MILLIONS of these "silly examples"...
So there evolutionists, please demonstrate what TESTS have been done to PROVE that all of these "systems" came about by "accident"...
Are they up to the challenge or is it simply more secular fundamentalism?? YOU DECIDE!!!
not even everclear could mess me up enough for me to hallucinate any sense into this article
Maybe you're BOTH right. Who is to say that the original spark of life came from a Divine intervention, but today's biodiversity came about from evolution, an ongoing process?
2 Peter 3:8: "But you must not forget, dear friends, that a day is like a thousand years to the Lord, and a thousand years is like a day."
If life just miraculously appeared from inanimate objects and chemicals by chance... then it should be really easy to duplicate that feat in a lab....
oh you can't ?
didn't think so.
That's not allowed. Don't ask me why, but that's not allowed for some reason.
If I thought it would do any good, I'd explain the various ways in which the open system in question violates the strictures of a proper application of the 2nd Law of Thermodynamics, then go on to explain the various patterns naturally imposed on the system's energy input...
but, as such an educational and helpful effort would be either ignored or boneheadedly misunderstood by the author, I shall conserve my limited energies to simple mockery.
Evolution defies the Second Law of Thermodynamics. In plain terms, it expects people to accept, on blind, unverifiable faith, that out of disorder, and through a bunch of accidents, order is created--, disorder becomes order. Another way of looking at that would be to think of a deck of cards, carefully shuffled and thrown high in the air. With the expectation that eventually an accident would happen which would cause all 52 cards in the deck, to fall in perfect order, and perfectly aligned.
Working from my admittedly hazy recollection of high school statistics and an Excel spreadsheet, I think the chances of throwing a deck of cards in the air and having it come down in order is 1 in (52x51x50x49....x1) or 8.07^67. Seems like a small chance.
But then compare it to the number of chances there are in the natural world. Say you have a bacterium that divides once a day. Each division is like throwing the genetic deck of cards in the air. So first you have 1 bacterium, then you have 2, then 4, then 8 and so on. After a year, you have 3.8^109 bacteria -- that's way bigger than the 1 in 8.07^67 chance of getting the cards in order. In other words, even though the chances of getting the cards in order is so small, you will have so many bacteria that one of them is almost guaranteed to have the genetic cards in order. Even if you assume mutations only occur once in every trillion tosses of the genetic cards, it still seems like the chances of getting the generic cards in order is almost 100%.
Can someone with better math skills confirm this?
Easy for YOU to say that.
How many experiments are left undisturbed for a few million years?
Also, my understanding is that the 2nd Law applies to closed systems that do not receive energy from an outside source, such as a gigantic unshielded fusion reactor 93 million miles away.
Now, to return to your statement which you made earlier:
"... a belief system based on Darwinian mechanisms that are both mindless and stupid."If you knew even the most basic things about it, you'd understand that "Darwinian mechanisms" are anything but "stupid". They're very creative and capable of astoundingly complex innovations which, if they were the product of human work, no one would have trouble labeling as "brilliant". Despite your ignorant declaration that Darwinian mechanisms are capable only of "stupidity", anyone familiar with the field already knows that when actually applied to hard problems -- even those beyond human analysis -- Darwinian mechanisms are more than smart enough to produce fantastic, practical results, including solutions whose performance exceeds that of the best human designers, and which often work in ways which are beyond human understanding. To call that sort of proven ability "stupid" is, well, not too bright.
I'm speaking, of course, of the field of "genetic algorithms", which is the harnessing of the power of evolution in order to achieve real results on real-world problems. And this is not just a "simulation" of evolution -- applying reproduction, variation, and selection on a population of potential solutions (including beginning from totally random "starter solutions") results in real, actual evolution. Evolution is a generative information process which takes place any time there is variable reproduction and selection within a population, whether that population happens to be microbes in a test tube, birds on a mountainside, or cybernetic genomes in a computer.
Read the *whole* document (yes, I know, you might actually learn something by accident, but be not afraid). Pay special attention to some of the more astounding documented results of letting evolution loose on hard real-world problems, such as the evolved FPGA circuit which requires only 37 logic gates in order to recognize spoken words, "a task that would have been considered impossible for any human engineer", and which works in a manner that can't even be understood by human engineers -- it contains no clock circuit, which all human-designed circuits for speech recognition are built upon, and even more shockingly:
In fact, out of the 37 logic gates the final product uses, five of them are not even connected to the rest of the circuit in any way - yet if their power supply is removed, the circuit stops working. It seems that evolution has exploited some subtle electromagnetic effect of these cells to come up with its solution, yet the exact workings of the complex and intricate evolved structure remain a mystery.If a human designer managed to produce such a circuit -- using far fewer components than all previous attempts, working in a manner that appears to be nothing short of magic and based on previously unsuspected principles -- would you be so bold as to call him "stupid"? Or would "genius" be a better label? And yet, this amazing circuit is the direct product of the "Darwinian mechanisms" you so frequently (and ignorantly) disparage. Stupid? Or brilliant?
Or consider the case of the circuit evolved to produce an oscillating sine wave. When a student is given a problem and "thinks outside the box" in order to come up with an unorthodox solution (but one which still meets the problem requirements), it's considered not "stupid", but a brilliant application of creative problem-solving. The classic case is the Barometer Exam Question. So what, then, are we to make of the case of the evolutionary process that was tasked with using evolution to come up with an electronic circuit that produced an oscillating sine wave -- and instead of building some form of classic oscillator circuit, it built a radio receiver and amplifier which fed out the oscillating signal it detected and was picking up from a nearby piece of unshielded electronic equipment?
For another example, how about the antenna configuration -- produced by evolution -- which is wildly asymmetrical and like nothing any human engineer would think to build (or even know how -- human antenna designs are based on symmetries) which "excellently" met the specifications of the application is was needed for (a circularly polarized seven-segment antenna with hemispherical coverage): "It had a nearly uniform radiation pattern with high bandwidth both in simulation and in experimental testing." Stupid? I think not.
How about the evolutionary process which produced battle plans for a complex wargame that exceeded the performance of several sets of plans developed by experienced military experts? Stupid? Hardly.
How about the supersonic wing design produced by evolution, which outperformed -- on *all* required parameters -- the design produced by Japan's National Aerospace Laboratory for the same application? "Stupid"? Don't make me laugh.
The following is an article which corrects all of your (many) misconceptions about evolutionary processes, gives dozens of examples of the real-world problems they've solved in non-stupid ways, as well as responses to all of the common hand-waving creationist "rebuttals" to the inarguable successes of evolutionary processes (which creationists keep asserting "can't actually work"...). If you repeat any of the discredited creationist rebuttals, without taking into account the refutations contained therein, prepare to be laughed at.
Try reading it and learning something about this topic for a change, before you repeat (yet again) your same old tired and false misconceptions:
Short Cliff-notes version for the lazy: Contrary to the strident claims of creationists, the process evolution *works*, and produces "specified complexity" (and new information) without difficulty.
Genetic Algorithms and Evolutionary Computationby Adam Marczyk
Other Links:
- Algoritmos Genéticos y Computación Evolutiva
- A Spanish translation of this article.
- Introduction
- What is a genetic algorithm?
- A brief history of genetic algorithms
- What are the strengths of genetic algorithms?
- What are the limitations of genetic algorithms?
- Some specific examples of genetic algorithms
- Creationist arguments
- Conclusion
- References and resources
reationists occasionally charge that evolution is useless as a scientific theory because it produces no practical benefits and has no relevance to daily life. However, the evidence of biology alone shows that this claim is untrue. There are numerous natural phenomena for which evolution gives us a sound theoretical underpinning. To name just one, the observed development of resistance - to insecticides in crop pests, to antibiotics in bacteria, to chemotherapy in cancer cells, and to anti-retroviral drugs in viruses such as HIV - is a straightforward consequence of the laws of mutation and selection, and understanding these principles has helped us to craft strategies for dealing with these harmful organisms. The evolutionary postulate of common descent has aided the development of new medical drugs and techniques by giving researchers a good idea of which organisms they should experiment on to obtain results that are most likely to be relevant to humans. Finally, the principle of selective breeding has been used to great effect by humans to create customized organisms unlike anything found in nature for their own benefit. The canonical example, of course, is the many varieties of domesticated dogs (breeds as diverse as bulldogs, chihuahuas and dachshunds have been produced from wolves in only a few thousand years), but less well-known examples include cultivated maize (very different from its wild relatives, none of which have the familiar "ears" of human-grown corn), goldfish (like dogs, we have bred varieties that look dramatically different from the wild type), and dairy cows (with immense udders far larger than would be required just for nourishing offspring).
Critics might charge that creationists can explain these things without recourse to evolution. For example, creationists often explain the development of resistance to antibiotic agents in bacteria, or the changes wrought in domesticated animals by artificial selection, by presuming that God decided to create organisms in fixed groups, called "kinds" or baramin. Though natural microevolution or human-guided artificial selection can bring about different varieties within the originally created "dog-kind," or "cow-kind," or "bacteria-kind" (!), no amount of time or genetic change can transform one "kind" into another. However, exactly how the creationists determine what a "kind" is, or what mechanism prevents living things from evolving beyond its boundaries, is invariably never explained.
But in the last few decades, the continuing advance of modern technology has brought about something new. Evolution is now producing practical benefits in a very different field, and this time, the creationists cannot claim that their explanation fits the facts just as well. This field is computer science, and the benefits come from a programming strategy called genetic algorithms. This essay will explain what genetic algorithms are and will show how they are relevant to the evolution/creationism debate.
What is a genetic algorithm?
Concisely stated, a genetic algorithm (or GA for short) is a programming technique that mimics biological evolution as a problem-solving strategy. Given a specific problem to solve, the input to the GA is a set of potential solutions to that problem, encoded in some fashion, and a metric called a fitness function that allows each candidate to be quantitatively evaluated. These candidates may be solutions already known to work, with the aim of the GA being to improve them, but more often they are generated at random.
The GA then evaluates each candidate according to the fitness function. In a pool of randomly generated candidates, of course, most will not work at all, and these will be deleted. However, purely by chance, a few may hold promise - they may show activity, even if only weak and imperfect activity, toward solving the problem.
These promising candidates are kept and allowed to reproduce. Multiple copies are made of them, but the copies are not perfect; random changes are introduced during the copying process. These digital offspring then go on to the next generation, forming a new pool of candidate solutions, and are subjected to a second round of fitness evaluation. Those candidate solutions which were worsened, or made no better, by the changes to their code are again deleted; but again, purely by chance, the random variations introduced into the population may have improved some individuals, making them into better, more complete or more efficient solutions to the problem at hand. Again these winning individuals are selected and copied over into the next generation with random changes, and the process repeats. The expectation is that the average fitness of the population will increase each round, and so by repeating this process for hundreds or thousands of rounds, very good solutions to the problem can be discovered.
As astonishing and counterintuitive as it may seem to some, genetic algorithms have proven to be an enormously powerful and successful problem-solving strategy, dramatically demonstrating the power of evolutionary principles. Genetic algorithms have been used in a wide variety of fields to evolve solutions to problems as difficult as or more difficult than those faced by human designers. Moreover, the solutions they come up with are often more efficient, more elegant, or more complex than anything comparable a human engineer would produce. In some cases, genetic algorithms have come up with solutions that baffle the programmers who wrote the algorithms in the first place!
Methods of representation
Before a genetic algorithm can be put to work on any problem, a method is needed to encode potential solutions to that problem in a form that a computer can process. One common approach is to encode solutions as binary strings: sequences of 1's and 0's, where the digit at each position represents the value of some aspect of the solution. Another, similar approach is to encode solutions as arrays of integers or decimal numbers, with each position again representing some particular aspect of the solution. This approach allows for greater precision and complexity than the comparatively restricted method of using binary numbers only and often "is intuitively closer to the problem space" (Fleming and Purshouse 2002, p. 1228).
This technique was used, for example, in the work of Steffen Schulze-Kremer, who wrote a genetic algorithm to predict the three-dimensional structure of a protein based on the sequence of amino acids that go into it (Mitchell 1996, p. 62). Schulze-Kremer's GA used real-valued numbers to represent the so-called "torsion angles" between the peptide bonds that connect amino acids. (A protein is made up of a sequence of basic building blocks called amino acids, which are joined together like the links in a chain. Once all the amino acids are linked, the protein folds up into a complex three-dimensional shape based on which amino acids attract each other and which ones repel each other. The shape of a protein determines its function.) Genetic algorithms for training neural networks often use this method of encoding also.
A third approach is to represent individuals in a GA as strings of letters, where each letter again stands for a specific aspect of the solution. One example of this technique is Hiroaki Kitano's "grammatical encoding" approach, where a GA was put to the task of evolving a simple set of rules called a context-free grammar that was in turn used to generate neural networks for a variety of problems (Mitchell 1996, p. 74).
The virtue of all three of these methods is that they make it easy to define operators that cause the random changes in the selected candidates: flip a 0 to a 1 or vice versa, add or subtract from the value of a number by a randomly chosen amount, or change one letter to another. (See the section on Methods of change for more detail about the genetic operators.) Another strategy, developed principally by John Koza of Stanford University and called genetic programming, represents programs as branching data structures called trees (Koza et al. 2003, p. 35). In this approach, random changes can be brought about by changing the operator or altering the value at a given node in the tree, or replacing one subtree with another.
Figure 1: Three simple program trees of the kind normally used in genetic programming. The mathematical expression that each one represents is given underneath.
It is important to note that evolutionary algorithms do not need to represent candidate solutions as data strings of fixed length. Some do represent them in this way, but others do not; for example, Kitano's grammatical encoding discussed above can be efficiently scaled to create large and complex neural networks, and Koza's genetic programming trees can grow arbitrarily large as necessary to solve whatever problem they are applied to.
Methods of selection
There are many different techniques which a genetic algorithm can use to select the individuals to be copied over into the next generation, but listed below are some of the most common methods. Some of these methods are mutually exclusive, but others can be and often are used in combination.
Elitist selection: The most fit members of each generation are guaranteed to be selected. (Most GAs do not use pure elitism, but instead use a modified form where the single best, or a few of the best, individuals from each generation are copied into the next generation just in case nothing better turns up.)
Fitness-proportionate selection: More fit individuals are more likely, but not certain, to be selected.
Roulette-wheel selection: A form of fitness-proportionate selection in which the chance of an individual's being selected is proportional to the amount by which its fitness is greater or less than its competitors' fitness. (Conceptually, this can be represented as a game of roulette - each individual gets a slice of the wheel, but more fit ones get larger slices than less fit ones. The wheel is then spun, and whichever individual "owns" the section on which it lands each time is chosen.)
Scaling selection: As the average fitness of the population increases, the strength of the selective pressure also increases and the fitness function becomes more discriminating. This method can be helpful in making the best selection later on when all individuals have relatively high fitness and only small differences in fitness distinguish one from another.
Tournament selection: Subgroups of individuals are chosen from the larger population, and members of each subgroup compete against each other. Only one individual from each subgroup is chosen to reproduce.
Rank selection: Each individual in the population is assigned a numerical rank based on fitness, and selection is based on this ranking rather than absolute differences in fitness. The advantage of this method is that it can prevent very fit individuals from gaining dominance early at the expense of less fit ones, which would reduce the population's genetic diversity and might hinder attempts to find an acceptable solution.
Generational selection: The offspring of the individuals selected from each generation become the entire next generation. No individuals are retained between generations.
Steady-state selection: The offspring of the individuals selected from each generation go back into the pre-existing gene pool, replacing some of the less fit members of the previous generation. Some individuals are retained between generations.
Hierarchical selection: Individuals go through multiple rounds of selection each generation. Lower-level evaluations are faster and less discriminating, while those that survive to higher levels are evaluated more rigorously. The advantage of this method is that it reduces overall computation time by using faster, less selective evaluation to weed out the majority of individuals that show little or no promise, and only subjecting those who survive this initial test to more rigorous and more computationally expensive fitness evaluation.
Methods of change
Once selection has chosen fit individuals, they must be randomly altered in hopes of improving their fitness for the next generation. There are two basic strategies to accomplish this. The first and simplest is called mutation. Just as mutation in living things changes one gene to another, so mutation in a genetic algorithm causes small alterations at single points in an individual's code.
The second method is called crossover, and entails choosing two individuals to swap segments of their code, producing artificial "offspring" that are combinations of their parents. This process is intended to simulate the analogous process of recombination that occurs to chromosomes during sexual reproduction. Common forms of crossover include single-point crossover, in which a point of exchange is set at a random location in the two individuals' genomes, and one individual contributes all its code from before that point and the other contributes all its code from after that point to produce an offspring, and uniform crossover, in which the value at any given location in the offspring's genome is either the value of one parent's genome at that location or the value of the other parent's genome at that location, chosen with 50/50 probability.
Figure 2: Crossover and mutation. The above diagrams illustrate the effect of each of these genetic operators on individuals in a population of 8-bit strings. The upper diagram shows two individuals undergoing single-point crossover; the point of exchange is set between the fifth and sixth positions in the genome, producing a new individual that is a hybrid of its progenitors. The second diagram shows an individual undergoing mutation at position 4, changing the 0 at that position in its genome to a 1.
Other problem-solving techniques
With the rise of artificial life computing and the development of heuristic methods, other computerized problem-solving techniques have emerged that are in some ways similar to genetic algorithms. This section explains some of these techniques, in what ways they resemble GAs and in what ways they differ.
- Neural networks
A neural network, or neural net for short, is a problem-solving method based on a computer model of how neurons are connected in the brain. A neural network consists of layers of processing units called nodes joined by directional links: one input layer, one output layer, and zero or more hidden layers in between. An initial pattern of input is presented to the input layer of the neural network, and nodes that are stimulated then transmit a signal to the nodes of the next layer to which they are connected. If the sum of all the inputs entering one of these virtual neurons is higher than that neuron's so-called activation threshold, that neuron itself activates, and passes on its own signal to neurons in the next layer. The pattern of activation therefore spreads forward until it reaches the output layer and is there returned as a solution to the presented input. Just as in the nervous system of biological organisms, neural networks learn and fine-tune their performance over time via repeated rounds of adjusting their thresholds until the actual output matches the desired output for any given input. This process can be supervised by a human experimenter or may run automatically using a learning algorithm (Mitchell 1996, p. 52). Genetic algorithms have been used both to build and to train neural networks.
Figure 3: A simple feedforward neural network, with one input layer consisting of four neurons, one hidden layer consisting of three neurons, and one output layer consisting of four neurons. The number on each neuron represents its activation threshold: it will only fire if it receives at least that many inputs. The diagram shows the neural network being presented with an input string and shows how activation spreads forward through the network to produce an output.
- Hill-climbing
Similar to genetic algorithms, though more systematic and less random, a hill-climbing algorithm begins with one initial solution to the problem at hand, usually chosen at random. The string is then mutated, and if the mutation results in higher fitness for the new solution than for the previous one, the new solution is kept; otherwise, the current solution is retained. The algorithm is then repeated until no mutation can be found that causes an increase in the current solution's fitness, and this solution is returned as the result (Koza et al. 2003, p. 59). (To understand where the name of this technique comes from, imagine that the space of all possible solutions to a given problem is represented as a three-dimensional contour landscape. A given set of coordinates on that landscape represents one particular solution. Those solutions that are better are higher in altitude, forming hills and peaks; those that are worse are lower in altitude, forming valleys. A "hill-climber" is then an algorithm that starts out at a given point on the landscape and moves inexorably uphill.) Hill-climbing is what is known as a greedy algorithm, meaning it always makes the best choice available at each step in the hope that the overall best result can be achieved this way. By contrast, methods such as genetic algorithms and simulated annealing, discussed below, are not greedy; these methods sometimes make suboptimal choices in the hopes that they will lead to better solutions later on.
- Simulated annealing
Another optimization technique similar to evolutionary algorithms is known as simulated annealing. The idea borrows its name from the industrial process of annealing in which a material is heated to above its melting point, then gradually cooled in order to erase defects in its crystalline structure, producing a more stable and regular lattice arrangement of atoms (Haupt and Haupt 1998, p. 16). In simulated annealing, as in genetic algorithms, there is a fitness function that defines a fitness landscape; however, rather than a population of candidates as in GAs, there is only one candidate solution. Simulated annealing also adds the concept of "temperature", a global numerical quantity which gradually decreases over time. At each step of the algorithm, the solution mutates (which is equivalent to moving to an adjacent point of the fitness landscape). The fitness of the new solution is then compared to the fitness of the previous solution; if it is higher, the new solution is kept. Otherwise, the algorithm makes a decision whether to keep or discard it based on temperature. If the temperature is high, as it is initially, even changes that cause significant decreases in fitness may be kept and used as the basis for the next round of the algorithm, but as temperature decreases, the algorithm becomes more and more inclined to only accept fitness-increasing changes. Finally, the temperature reaches zero and the system "freezes"; whatever configuration it is in at that point becomes the solution. Simulated annealing is often used for engineering design applications such as determining the physical layout of components on a computer chip (Kirkpatrick, Gelatt and Vecchi 1983).
A brief history of GAs
The earliest instances of what might today be called genetic algorithms appeared in the late 1950s and early 1960s, programmed on computers by evolutionary biologists who were explicitly seeking to model aspects of natural evolution. It did not occur to any of them that this strategy might be more generally applicable to artificial problems, but that recognition was not long in coming: "Evolutionary computation was definitely in the air in the formative days of the electronic computer" (Mitchell 1996, p.2). By 1962, researchers such as G.E.P. Box, G.J. Friedman, W.W. Bledsoe and H.J. Bremermann had all independently developed evolution-inspired algorithms for function optimization and machine learning, but their work attracted little followup. A more successful development in this area came in 1965, when Ingo Rechenberg, then of the Technical University of Berlin, introduced a technique he called evolution strategy, though it was more similar to hill-climbers than to genetic algorithms. In this technique, there was no population or crossover; one parent was mutated to produce one offspring, and the better of the two was kept and became the parent for the next round of mutation (Haupt and Haupt 1998, p.146). Later versions introduced the idea of a population. Evolution strategies are still employed today by engineers and scientists, especially in Germany.
The next important development in the field came in 1966, when L.J. Fogel, A.J. Owens and M.J. Walsh introduced in America a technique they called evolutionary programming. In this method, candidate solutions to problems were represented as simple finite-state machines; like Rechenberg's evolution strategy, their algorithm worked by randomly mutating one of these simulated machines and keeping the better of the two (Mitchell 1996, p.2; Goldberg 1989, p.105). Also like evolution strategies, a broader formulation of the evolutionary programming technique is still an area of ongoing research today. However, what was still lacking in both these methodologies was recognition of the importance of crossover.
As early as 1962, John Holland's work on adaptive systems laid the foundation for later developments; most notably, Holland was also the first to explicitly propose crossover and other recombination operators. However, the seminal work in the field of genetic algorithms came in 1975, with the publication of the book Adaptation in Natural and Artificial Systems. Building on earlier research and papers both by Holland himself and by colleagues at the University of Michigan, this book was the first to systematically and rigorously present the concept of adaptive digital systems using mutation, selection and crossover, simulating processes of biological evolution, as a problem-solving strategy. The book also attempted to put genetic algorithms on a firm theoretical footing by introducing the notion of schemata (Mitchell 1996, p.3; Haupt and Haupt 1998, p.147). That same year, Kenneth De Jong's important dissertation established the potential of GAs by showing that they could perform well on a wide variety of test functions, including noisy, discontinuous, and multimodal search landscapes (Goldberg 1989, p.107).
These foundational works established more widespread interest in evolutionary computation. By the early to mid-1980s, genetic algorithms were being applied to a broad range of subjects, from abstract mathematical problems like bin-packing and graph coloring to tangible engineering issues such as pipeline flow control, pattern recognition and classification, and structural optimization (Goldberg 1989, p. 128).
At first, these applications were mainly theoretical. However, as research continued to proliferate, genetic algorithms migrated into the commercial sector, their rise fueled by the exponential growth of computing power and the development of the Internet. Today, evolutionary computation is a thriving field, and genetic algorithms are "solving problems of everyday interest" (Haupt and Haupt 1998, p.147) in areas of study as diverse as stock market prediction and portfolio planning, aerospace engineering, microchip design, biochemistry and molecular biology, and scheduling at airports and assembly lines. The power of evolution has touched virtually any field one cares to name, shaping the world around us invisibly in countless ways, and new uses continue to be discovered as research is ongoing. And at the heart of it all lies nothing more than Charles Darwin's simple, powerful insight: that the random chance of variation, coupled with the law of selection, is a problem-solving technique of immense power and nearly unlimited application.
What are the strengths of GAs?
- The first and most important point is that genetic algorithms are intrinsically parallel. Most other algorithms are serial and can only explore the solution space to a problem in one direction at a time, and if the solution they discover turns out to be suboptimal, there is nothing to do but abandon all work previously completed and start over. However, since GAs have multiple offspring, they can explore the solution space in multiple directions at once. If one path turns out to be a dead end, they can easily eliminate it and continue work on more promising avenues, giving them a greater chance each run of finding the optimal solution.
However, the advantage of parallelism goes beyond this. Consider the following: All the 8-digit binary strings (strings of 0's and 1's) form a search space, which can be represented as ******** (where the * stands for "either 0 or 1"). The string 01101010 is one member of this space. However, it is also a member of the space 0*******, the space 01******, the space 0******0, the space 0*1*1*1*, the space 01*01**0, and so on. By evaluating the fitness of this one particular string, a genetic algorithm would be sampling each of these many spaces to which it belongs. Over many such evaluations, it would build up an increasingly accurate value for the average fitness of each of these spaces, each of which has many members. Therefore, a GA that explicitly evaluates a small number of individuals is implicitly evaluating a much larger group of individuals - just as a pollster who asks questions of a certain member of an ethnic, religious or social group hopes to learn something about the opinions of all members of that group, and therefore can reliably predict national opinion while sampling only a small percentage of the population. In the same way, the GA can "home in" on the space with the highest-fitness individuals and find the overall best one from that group. In the context of evolutionary algorithms, this is known as the Schema Theorem, and is the "central advantage" of a GA over other problem-solving methods (Holland 1992, p. 68; Mitchell 1996, p.28-29; Goldberg 1989, p.20).
- Due to the parallelism that allows them to implicitly evaluate many schema at once, genetic algorithms are particularly well-suited to solving problems where the space of all potential solutions is truly huge - too vast to search exhaustively in any reasonable amount of time. Most problems that fall into this category are known as "nonlinear". In a linear problem, the fitness of each component is independent, so any improvement to any one part will result in an improvement of the system as a whole. Needless to say, few real-world problems are like this. Nonlinearity is the norm, where changing one component may have ripple effects on the entire system, and where multiple changes that individually are detrimental may lead to much greater improvements in fitness when combined. Nonlinearity results in a combinatorial explosion: the space of 1,000-digit binary strings can be exhaustively searched by evaluating only 2,000 possibilities if the problem is linear, whereas if it is nonlinear, an exhaustive search requires evaluating 21000 possibilities - a number that would take over 300 digits to write out in full.
Fortunately, the implicit parallelism of a GA allows it to surmount even this enormous number of possibilities, successfully finding optimal or very good results in a short period of time after directly sampling only small regions of the vast fitness landscape (Forrest 1993, p. 877). For example, a genetic algorithm developed jointly by engineers from General Electric and Rensselaer Polytechnic Institute produced a high-performance jet engine turbine design that was three times better than a human-designed configuration and 50% better than a configuration designed by an expert system by successfully navigating a solution space containing more than 10387 possibilities. Conventional methods for designing such turbines are a central part of engineering projects that can take up to five years and cost over $2 billion; the genetic algorithm discovered this solution after two days on a typical engineering desktop workstation (Holland 1992, p.72).
- Another notable strength of genetic algorithms is that they perform well in problems for which the fitness landscape is complex - ones where the fitness function is discontinuous, noisy, changes over time, or has many local optima. Most practical problems have a vast solution space, impossible to search exhaustively; the challenge then becomes how to avoid the local optima - solutions that are better than all the others that are similar to them, but that are not as good as different ones elsewhere in the solution space. Many search algorithms can become trapped by local optima: if they reach the top of a hill on the fitness landscape, they will discover that no better solutions exist nearby and conclude that they have reached the best one, even though higher peaks exist elsewhere on the map.
Evolutionary algorithms, on the other hand, have proven to be effective at escaping local optima and discovering the global optimum in even a very rugged and complex fitness landscape. (It should be noted that, in reality, there is usually no way to tell whether a given solution to a problem is the one global optimum or just a very high local optimum. However, even if a GA does not always deliver a provably perfect solution to a problem, it can almost always deliver at least a very good solution.) All four of a GA's major components - parallelism, selection, mutation, and crossover - work together to accomplish this. In the beginning, the GA generates a diverse initial population, casting a "net" over the fitness landscape. (Koza (2003, p. 506) compares this to an army of parachutists dropping onto the landscape of a problem's search space, with each one being given orders to find the highest peak.) Small mutations enable each individual to explore its immediate neighborhood, while selection focuses progress, guiding the algorithm's offspring uphill to more promising parts of the solution space (Holland 1992, p. 68).
However, crossover is the key element that distinguishes genetic algorithms from other methods such as hill-climbers and simulated annealing. Without crossover, each individual solution is on its own, exploring the search space in its immediate vicinity without reference to what other individuals may have discovered. However, with crossover in place, there is a transfer of information between successful candidates - individuals can benefit from what others have learned, and schemata can be mixed and combined, with the potential to produce an offspring that has the strengths of both its parents and the weaknesses of neither. This point is illustrated in Koza et al. 1999, p.486, where the authors discuss a problem of synthesizing a lowpass filter using genetic programming. In one generation, two parent circuits were selected to undergo crossover; one parent had good topology (components such as inductors and capacitors in the right places) but bad sizing (values of inductance and capacitance for its components that were far too low). The other parent had bad topology, but good sizing. The result of mating the two through crossover was an offspring with the good topology of one parent and the good sizing of the other, resulting in a substantial improvement in fitness over both its parents.
The problem of finding the global optimum in a space with many local optima is also known as the dilemma of exploration vs. exploitation, "a classic problem for all systems that can adapt and learn" (Holland 1992, p. 69). Once an algorithm (or a human designer) has found a problem-solving strategy that seems to work satisfactorily, should it concentrate on making the best use of that strategy, or should it search for others? Abandoning a proven strategy to look for new ones is almost guaranteed to involve losses and degradation of performance, at least in the short term. But if one sticks with a particular strategy to the exclusion of all others, one runs the risk of not discovering better strategies that exist but have not yet been found. Again, genetic algorithms have shown themselves to be very good at striking this balance and discovering good solutions with a reasonable amount of time and computational effort.
- Another area in which genetic algorithms excel is their ability to manipulate many parameters simultaneously (Forrest 1993, p. 874). Many real-world problems cannot be stated in terms of a single value to be minimized or maximized, but must be expressed in terms of multiple objectives, usually with tradeoffs involved: one can only be improved at the expense of another. GAs are very good at solving such problems: in particular, their use of parallelism enables them to produce multiple equally good solutions to the same problem, possibly with one candidate solution optimizing one parameter and another candidate optimizing a different one (Haupt and Haupt 1998, p.17), and a human overseer can then select one of these candidates to use. If a particular solution to a multiobjective problem optimizes one parameter to a degree such that that parameter cannot be further improved without causing a corresponding decrease in the quality of some other parameter, that solution is called Pareto optimal or non-dominated (Coello 2000, p. 112).
- Finally, one of the qualities of genetic algorithms which might at first appear to be a liability turns out to be one of their strengths: namely, GAs know nothing about the problems they are deployed to solve. Instead of using previously known domain-specific information to guide each step and making changes with a specific eye towards improvement, as human designers do, they are "blind watchmakers" (Dawkins 1996); they make random changes to their candidate solutions and then use the fitness function to determine whether those changes produce an improvement.
The virtue of this technique is that it allows genetic algorithms to start out with an open mind, so to speak. Since its decisions are based on randomness, all possible search pathways are theoretically open to a GA; by contrast, any problem-solving strategy that relies on prior knowledge must inevitably begin by ruling out many pathways a priori, therefore missing any novel solutions that may exist there (Koza et al. 1999, p. 547). Lacking preconceptions based on established beliefs of "how things should be done" or what "couldn't possibly work", GAs do not have this problem. Similarly, any technique that relies on prior knowledge will break down when such knowledge is not available, but again, GAs are not adversely affected by ignorance (Goldberg 1989, p. 23). Through their components of parallelism, crossover and mutation, they can range widely over the fitness landscape, exploring regions which intelligently produced algorithms might have overlooked, and potentially uncovering solutions of startling and unexpected creativity that might never have occurred to human designers. One vivid illustration of this is the rediscovery, by genetic programming, of the concept of negative feedback - a principle crucial to many important electronic components today, but one that, when it was first discovered, was denied a patent for nine years because the concept was so contrary to established beliefs (Koza et al. 2003, p. 413). Evolutionary algorithms, of course, are neither aware nor concerned whether a solution runs counter to established beliefs - only whether it works.
What are the limitations of GAs?
Although genetic algorithms have proven to be an efficient and powerful problem-solving strategy, they are not a panacea. GAs do have certain limitations; however, it will be shown that all of these can be overcome and none of them bear on the validity of biological evolution.
- The first, and most important, consideration in creating a genetic algorithm is defining a representation for the problem. The language used to specify candidate solutions must be robust; i.e., it must be able to tolerate random changes such that fatal errors or nonsense do not consistently result.
There are two main ways of achieving this. The first, which is used by most genetic algorithms, is to define individuals as lists of numbers - binary-valued, integer-valued, or real-valued - where each number represents some aspect of a candidate solution. If the individuals are binary strings, 0 or 1 could stand for the absence or presence of a given feature. If they are lists of numbers, these numbers could represent many different things: the weights of the links in a neural network, the order of the cities visited in a given tour, the spatial placement of electronic components, the values fed into a controller, the torsion angles of peptide bonds in a protein, and so on. Mutation then entails changing these numbers, flipping bits or adding or subtracting random values. In this case, the actual program code does not change; the code is what manages the simulation and keeps track of the individuals, evaluating their fitness and perhaps ensuring that only values realistic and possible for the given problem result.
In another method, genetic programming, the actual program code does change. As discussed in the section Methods of representation, GP represents individuals as executable trees of code that can be mutated by changing or swapping subtrees. Both of these methods produce representations that are robust against mutation and can represent many different kinds of problems, and as discussed in the section Some specific examples, both have had considerable success.
This issue of representing candidate solutions in a robust way does not arise in nature, because the method of representation used by evolution, namely the genetic code, is inherently robust: with only a very few exceptions, such as a string of stop codons, there is no such thing as a sequence of DNA bases that cannot be translated into a protein. Therefore, virtually any change to an individual's genes will still produce an intelligible result, and so mutations in evolution have a higher chance of producing an improvement. This is in contrast to human-created languages such as English, where the number of meaningful words is small compared to the total number of ways one can combine letters of the alphabet, and therefore random changes to an English sentence are likely to produce nonsense.
- The problem of how to write the fitness function must be carefully considered so that higher fitness is attainable and actually does equate to a better solution for the given problem. If the fitness function is chosen poorly or defined imprecisely, the genetic algorithm may be unable to find a solution to the problem, or may end up solving the wrong problem. (This latter situation is sometimes described as the tendency of a GA to "cheat", although in reality all that is happening is that the GA is doing what it was told to do, not what its creators intended it to do.) An example of this can be found in Graham-Rowe 2002, in which researchers used an evolutionary algorithm in conjunction with a reprogrammable hardware array, setting up the fitness function to reward the evolving circuit for outputting an oscillating signal. At the end of the experiment, an oscillating signal was indeed being produced - but instead of the circuit itself acting as an oscillator, as the researchers had intended, they discovered that it had become a radio receiver that was picking up and relaying an oscillating signal from a nearby piece of electronic equipment!
This is not a problem in nature, however. In the laboratory of biological evolution there is only one fitness function, which is the same for all living things - the drive to survive and reproduce, no matter what adaptations make this possible. Those organisms which reproduce more abundantly compared to their competitors are more fit; those which fail to reproduce are unfit.
- In addition to making a good choice of fitness function, the other parameters of a GA - the size of the population, the rate of mutation and crossover, the type and strength of selection - must be also chosen with care. If the population size is too small, the genetic algorithm may not explore enough of the solution space to consistently find good solutions. If the rate of genetic change is too high or the selection scheme is chosen poorly, beneficial schema may be disrupted and the population may enter error catastrophe, changing too fast for selection to ever bring about convergence.
Living things do face similar difficulties, and evolution has dealt with them. It is true that if a population size falls too low, mutation rates are too high, or the selection pressure is too strong (such a situation might be caused by drastic environmental change), then the species may go extinct. The solution has been "the evolution of evolvability" - adaptations that alter a species' ability to adapt. For example, most living things have evolved elaborate molecular machinery that checks for and corrects errors during the process of DNA replication, keeping their mutation rate down to acceptably low levels; conversely, in times of severe environmental stress, some bacterial species enter a state of hypermutation where the rate of DNA replication errors rises sharply, increasing the chance that a compensating mutation will be discovered. Of course, not all catastrophes can be evaded, but the enormous diversity and highly complex adaptations of living things today show that, in general, evolution is a successful strategy. Likewise, the diverse applications of and impressive results produced by genetic algorithms show them to be a powerful and worthwhile field of study.
- One type of problem that genetic algorithms have difficulty dealing with are problems with "deceptive" fitness functions (Mitchell 1996, p.125), those where the locations of improved points give misleading information about where the global optimum is likely to be found. For example, imagine a problem where the search space consisted of all eight-character binary strings, and the fitness of an individual was directly proportional to the number of 1s in it - i.e., 00000001 would be less fit than 00000011, which would be less fit than 00000111, and so on - with two exceptions: the string 11111111 turned out to have very low fitness, and the string 00000000 turned out to have very high fitness. In such a problem, a GA (as well as most other algorithms) would be no more likely to find the global optimum than random search.
The resolution to this problem is the same for both genetic algorithms and biological evolution: evolution is not a process that has to find the single global optimum every time. It can do almost as well by reaching the top of a high local optimum, and for most situations, this will suffice, even if the global optimum cannot easily be reached from that point. Evolution is very much a "satisficer" - an algorithm that delivers a "good enough" solution, though not necessarily the best possible solution, given a reasonable amount of time and effort invested in the search. The Evidence for Jury-Rigged Design in Nature FAQ gives examples of this very outcome appearing in nature. (It is also worth noting that few, if any, real-world problems are as fully deceptive as the somewhat contrived example given above. Usually, the location of local improvements gives at least some information about the location of the global optimum.)
- One well-known problem that can occur with a GA is known as premature convergence. If an individual that is more fit than most of its competitors emerges early on in the course of the run, it may reproduce so abundantly that it drives down the population's diversity too soon, leading the algorithm to converge on the local optimum that that individual represents rather than searching the fitness landscape thoroughly enough to find the global optimum (Forrest 1993, p. 876; Mitchell 1996, p. 167). This is an especially common problem in small populations, where even chance variations in reproduction rate may cause one genotype to become dominant over others.
The most common methods implemented by GA researchers to deal with this problem all involve controlling the strength of selection, so as not to give excessively fit individuals too great of an advantage. Rank, scaling and tournament selection, discussed earlier, are three major means for accomplishing this; some methods of scaling selection include sigma scaling, in which reproduction is based on a statistical comparison to the population's average fitness, and Boltzmann selection, in which the strength of selection increases over the course of a run in a manner similar to the "temperature" variable in simulated annealing (Mitchell 1996, p. 168).
Premature convergence does occur in nature (where it is called genetic drift by biologists). This should not be surprising; as discussed above, evolution as a problem-solving strategy is under no obligation to find the single best solution, merely one that is good enough. However, premature convergence in nature is less common since most beneficial mutations in living things produce only small, incremental fitness improvements; mutations that produce such a large fitness gain as to give their possessors dramatic reproductive advantage are rare.
- Finally, several researchers (Holland 1992, p.72; Forrest 1993, p.875; Haupt and Haupt 1998, p.18) advise against using genetic algorithms on analytically solvable problems. It is not that genetic algorithms cannot find good solutions to such problems; it is merely that traditional analytic methods take much less time and computational effort than GAs and, unlike GAs, are usually mathematically guaranteed to deliver the one exact solution. Of course, since there is no such thing as a mathematically perfect solution to any problem of biological adaptation, this issue does not arise in nature.
Some specific examples of GAs
As the power of evolution gains increasingly widespread recognition, genetic algorithms have been used to tackle a broad variety of problems in an extremely diverse array of fields, clearly showing their power and their potential. This section will discuss some of the more noteworthy uses to which they have been put.
- Acoustics
- Aerospace engineering
- Astronomy and astrophysics
- Chemistry
- Electrical engineering
- Financial markets
- Game playing
- Geophysics
- Materials engineering
- Mathematics and algorithmics
- Military and law enforcement
- Molecular biology
- Pattern recognition and data mining
- Robotics
- Routing and scheduling
- Systems engineering
- Acoustics
Sato et al. 2002 used genetic algorithms to design a concert hall with optimal acoustic properties, maximizing the sound quality for the audience, for the conductor, and for the musicians on stage. This task involves the simultaneous optimization of multiple variables. Beginning with a shoebox-shaped hall, the authors' GA produced two non-dominated solutions, both of which were described as "leaf-shaped" (p.526). The authors state that these solutions have proportions similar to Vienna's Grosser Musikvereinsaal, which is widely agreed to be one of the best - if not the best - concert hall in the world in terms of acoustic properties.
Porto, Fogel and Fogel 1995 used evolutionary programming to train neural networks to distinguish between sonar reflections from different types of objects: man-made metal spheres, sea mounts, fish and plant life, and random background noise. After 500 generations, the best evolved neural network had a probability of correct classification ranging between 94% and 98% and a probability of misclassification between 7.4% and 1.5%, which are "reasonable probabilities of detection and false alarm" (p.21). The evolved network matched the performance of another network developed by simulated annealing and consistently outperformed networks trained by back propagation, which "repeatedly stalled at suboptimal weight sets that did not yield satisfactory results" (p.21). By contrast, both stochastic methods showed themselves able to overcome these local optima and produce smaller, effective and more robust networks; but the authors suggest that the evolutionary algorithm, unlike simulated annealing, operates on a population and so takes advantage of global information about the search space, potentially leading to better performance in the long run.
Tang et al. 1996 survey the uses of genetic algorithms within the field of acoustics and signal processing. One area of particular interest involves the use of GAs to design Active Noise Control (ANC) systems, which cancel out undesired sound by producing sound waves that destructively interfere with the unwanted noise. This is a multiple-objective problem requiring the precise placement and control of multiple loudspeakers; GAs have been used both to design the controllers and find the optimal placement of the loudspeakers for such systems, resulting in the "effective attenuation of noise" (p.33) in experimental tests.
- Aerospace engineering
Obayashi et al. 2000 used a multiple-objective genetic algorithm to design the wing shape for a supersonic aircraft. Three major considerations govern the wing's configuration - minimizing aerodynamic drag at supersonic cruising speeds, minimizing drag at subsonic speeds, and minimizing aerodynamic load (the bending force on the wing). These objectives are mutually exclusive, and optimizing them all simultaneously requires tradeoffs to be made.
The chromosome in this problem is a string of 66 real-valued numbers, each of which corresponds to a specific aspect of the wing: its shape, its thickness, its twist, and so on. Evolution with elitist rank selection was simulated for 70 generations, with a population size of 64 individuals. At the termination of this process, there were several Pareto-optimal individuals, each one representing a single non-dominated solution to the problem. The paper notes that these best-of-run individuals have "physically reasonable" characteristics, indicating the validity of the optimization technique (p.186). To further evaluate the quality of the solutions, six of the best were compared to a supersonic wing design produced by the SST Design Team of Japan's National Aerospace Laboratory. All six were competitive, having drag and load values approximately equal to or less than the human-designed wing; one of the evolved solutions in particular outperformed the NAL's design in all three objectives. The authors note that the GA's solutions are similar to a design called the "arrow wing" which was first suggested in the late 1950s, but ultimately abandoned in favor of the more conventional delta-wing design.
In a follow-up paper (Sasaki et al. 2001), the authors repeat their experiment while adding a fourth objective, namely minimizing the twisting moment of the wing (a known potential problem for arrow-wing SST designs). Additional control points for thickness are also added to the array of design variables. After 75 generations of evolution, two of the best Pareto-optimal solutions were again compared to the Japanese National Aerospace Laboratory's wing design for the NEXST-1 experimental supersonic airplane. It was found that both of these designs (as well as one optimal design from the previous simulation, discussed above) were physically reasonable and superior to the NAL's design in all four objectives.
Williams, Crossley and Lang 2001 applied genetic algorithms to the task of spacing satellite orbits to minimize coverage blackouts. As telecommunications technology continues to improve, humans are increasingly dependent on Earth-orbiting satellites to perform many vital functions, and one of the problems engineers face is designing their orbital trajectories. Satellites in high Earth orbit, around 22,000 miles up, can see large sections of the planet at once and be in constant contact with ground stations, but these are far more expensive to launch and more vulnerable to cosmic radiation. It is more economical to put satellites in low orbits, as low as a few hundred miles in some cases, but because of the curvature of the Earth it is inevitable that these satellites will at times lose line-of-sight access to surface receivers and thus be useless. Even constellations of several satellites experience unavoidable blackouts and losses of coverage for this reason. The challenge is to arrange the satellites' orbits to minimize this downtime. This is a multi-objective problem, involving the minimization of both the average blackout time for all locations and the maximum blackout time for any one location; in practice, these goals turn out to be mutually exclusive.
When the GA was applied to this problem, the evolved results for three, four and five-satellite constellations were unusual, highly asymmetric orbit configurations, with the satellites spaced by alternating large and small gaps rather than equal-sized gaps as conventional techniques would produce. However, this solution significantly reduced both average and maximum revisit times, in some cases by up to 90 minutes. In a news article about the results, Dr. William Crossley noted that "engineers with years of aerospace experience were surprised by the higher performance offered by the unconventional design".
Keane and Brown 1996 used a GA to evolve a new design for a load-bearing truss or boom that could be assembled in orbit and used for satellites, space stations and other aerospace construction projects. The result, a twisted, organic-looking structure that has been compared to a human leg bone, uses no more material than the standard truss design but is lightweight, strong and far superior at damping out damaging vibrations, as was confirmed by real-world tests of the final product. And yet "No intelligence made the designs. They just evolved" (Petit 1998). The authors of the paper further note that their GA only ran for 10 generations due to the computationally intensive nature of the simulation, and the population had not become stagnant. Continuing the run for more generations would undoubtedly have produced further improvements in performance.
Figure 4: A genetically optimized three-dimensional truss with improved frequency response. (Adapted from [1].)
Finally, as reported in Gibbs 1996, Lockheed Martin has used a genetic algorithm to evolve a series of maneuvers to shift a spacecraft from one orientation to another within 2% of the theoretical minimum time for such maneuvers. The evolved solution was 10% faster than a solution hand-crafted by an expert for the same problem.
- Astronomy and astrophysics
Charbonneau 1995 suggests the usefulness of GAs for problems in astrophysics by applying them to three example problems: fitting the rotation curve of a galaxy based on observed rotational velocities of its components, determining the pulsation period of a variable star based on time-series data, and solving for the critical parameters in a magnetohydrodynamic model of the solar wind. All three of these are hard multi-dimensional nonlinear problems.
Charbonneau's genetic algorithm, PIKAIA, uses generational, fitness-proportionate ranking selection coupled with elitism, ensuring that the single best individual is copied over once into the next generation without modification. PIKAIA has a crossover rate of 0.65 and a variable mutation rate that is set to 0.003 initially and gradually increases later on, as the population approaches convergence, to maintain variability in the gene pool.
In the galactic rotation-curve problem, the GA produced two curves, both of which were very good fits to the data (a common result in this type of problem, in which there is little contrast between neighboring hilltops); further observations can then distinguish which one is to be preferred. In the time-series problem, the GA was impressively successful in autonomously generating a high-quality fit for the data, but harder problems were not fitted as well (although, Charbonneau points out, these problems are equally difficult to solve with conventional techniques). The paper suggests that a hybrid GA employing both artificial evolution and standard analytic techniques might perform better. Finally, in solving for the six critical parameters of the solar wind, the GA successfully determined the value of three of them to an accuracy of within 0.1% and the remaining three to accuracies of within 1 to 10%. (Though lower experimental error for these three would always be preferable, Charbonneau notes that there are no other robust, efficient methods for experimentally solving a six-dimensional nonlinear problem of this type; a conjugate gradient method works "as long as a very good starting guess can be provided" (p.323). By contrast, GAs do not require such finely tuned domain-specific knowledge.)
Based on the results obtained so far, Charbonneau suggests that GAs can and should find use in other difficult problems in astrophysics, in particular inverse problems such as Doppler imaging and helioseismic inversions. In closing, Charbonneau argues that GAs are a "strong and promising contender" (p.324) in this field, one that can be expected to complement rather than replace traditional optimization techniques, and concludes that "the bottom line, if there is to be one, is that genetic algorithms work, and often frightfully well" (p.325).
- Chemistry
High-powered, ultrashort pulses of laser energy can split apart complex molecules into simpler molecules, a process with important applications to organic chemistry and microelectronics. The specific end products of such a reaction can be controlled by modulating the phase of the laser pulse. However, for large molecules, solving for the desired pulse shape analytically is too difficult: the calculations are too complex and the relevant characteristics (the potential energy surfaces of the molecules) are not known precisely enough.
Assion et al. 1998 solved this problem by using an evolutionary algorithm to design the pulse shape. Instead of inputting complex, problem-specific knowledge about the quantum characteristics of the input molecules to design the pulse to specifications, the EA fires a pulse, measures the proportions of the resulting product molecules, randomly mutates the beam characteristics with the hope of getting these proportions closer to the desired output, and the process repeats. (Rather than fine-tune any characteristics of the laser beam directly, the authors' GA represents individuals as a set of 128 numbers, each of which is a voltage value that controls the refractive index of one of the pixels in the laser light modulator. Again, no problem-specific knowledge about the properties of either the laser or the reaction products is needed.) The authors state that their algorithm, when applied to two sample substances, "automatically finds the best configuration... no matter how complicated the molecular response may be" (p.920), demonstrating "automated coherent control on products that are chemically different from each other and from the parent molecule" (p.921).
In the early to mid-1990s, the widespread adoption of a novel drug design technique called combinatorial chemistry revolutionized the pharmaceutical industry. In this method, rather than the painstaking, precise synthesis of a single compound at a time, biochemists deliberately mix a wide variety of reactants to produce an even wider variety of products - hundreds, thousands or millions of different compounds per batch - which can then be rapidly screened for biochemical activity. In designing libraries of reactants for this technique, there are two main approaches: reactant-based design, which chooses optimized groups of reactants without considering what products will result, and product-based design, which selects reactants most likely to produce products with the desired properties. Product-based design is more difficult and complex, but has been shown to result in better and more diverse combinatorial libraries and a greater likelihood of getting a usable result.
In a paper funded by GlaxoSmithKline's research and development department, Gillet 2002 discusses the use of a multiobjective genetic algorithm for the product-based design of combinatorial libraries. In choosing the compounds that go into a particular library, qualities such as molecular diversity and weight, cost of supplies, toxicity, absorption, distribution, and metabolism must all be considered. If the aim is to find molecules similar to an existing molecule of known function (a common method of new drug design), structural similarity can also be taken into account. This paper presents a multiobjective approach where a set of Pareto-optimal results that maximize or minimize each of these objectives can be developed. The author concludes that the GA was able to simultaneously satisfy the criteria of molecular diversity and maximum synthetic efficiency, and was able to find molecules that were drug-like as well as "very similar to given target molecules after exploring a very small fraction of the total search space" (p.378).
In a related paper, Glen and Payne 1995 discuss the use of genetic algorithms to automatically design new molecules from scratch to fit a given set of specifications. Given an initial population either generated randomly or using the simple molecule ethane as a seed, the GA randomly adds, removes and alters atoms and molecular fragments with the aim of generating molecules that fit the given constraints. The GA can simultaneously optimize a large number of objectives, including molecular weight, molecular volume, number of bonds, number of chiral centers, number of atoms, number of rotatable bonds, polarizability, dipole moment, and more in order to produce candidate molecules with the desired properties. Based on experimental tests, including one difficult optimization problem that involved generating molecules with properties similar to ribose (a sugar compound frequently mimicked in antiviral drugs), the authors conclude that the GA is an "excellent idea generator" (p.199) that offers "fast and powerful optimisation properties" and can generate "a diverse set of possible structures" (p.182). They go on to state, "Of particular note is the powerful optimising ability of the genetic algorithm, even with relatively small population sizes" (p.200). In a sign that these results are not merely theoretical, Lemley 2001 reports that the Unilever corporation has used genetic algorithms to design new antimicrobial compounds for use in cleansers, which it has patented.
- Electrical engineering
A field-programmable gate array, or FPGA for short, is a special type of circuit board with an array of logic cells, each of which can act as any type of logic gate, connected by flexible interlinks which can connect cells. Both of these functions are controlled by software, so merely by loading a special program into the board, it can be altered on the fly to perform the functions of any one of a vast variety of hardware devices.
Dr. Adrian Thompson has exploited this device, in conjunction with the principles of evolution, to produce a prototype voice-recognition circuit that can distinguish between and respond to spoken commands using only 37 logic gates - a task that would have been considered impossible for any human engineer. He generated random bit strings of 0s and 1s and used them as configurations for the FPGA, selecting the fittest individuals from each generation, reproducing and randomly mutating them, swapping sections of their code and passing them on to another round of selection. His goal was to evolve a device that could at first discriminate between tones of different frequencies (1 and 10 kilohertz), then distinguish between the spoken words "go" and "stop".
This aim was achieved within 3000 generations, but the success was even greater than had been anticipated. The evolved system uses far fewer cells than anything a human engineer could have designed, and it does not even need the most critical component of human-built systems - a clock. How does it work? Thompson has no idea, though he has traced the input signal through a complex arrangement of feedback loops within the evolved circuit. In fact, out of the 37 logic gates the final product uses, five of them are not even connected to the rest of the circuit in any way - yet if their power supply is removed, the circuit stops working. It seems that evolution has exploited some subtle electromagnetic effect of these cells to come up with its solution, yet the exact workings of the complex and intricate evolved structure remain a mystery (Davidson 1997).
Altshuler and Linden 1997 used a genetic algorithm to evolve wire antennas with pre-specified properties. The authors note that the design of such antennas is an imprecise process, starting with the desired properties and then determining the antenna's shape through "guesses.... intuition, experience, approximate equations or empirical studies" (p.50). This technique is time-consuming, often does not produce optimal results, and tends to work well only for relatively simple, symmetric designs. By contrast, in the genetic algorithm approach, the engineer specifies the antenna's electromagnetic properties, and the GA automatically synthesizes a matching configuration.
Figure 5: A crooked-wire genetic antenna
(after Altshuler and Linden 1997, figure 1).
Altshuler and Linden used their GA to design a circularly polarized seven-segment antenna with hemispherical coverage; the result is shown to the left. Each individual in the GA consisted of a binary chromosome specifying the three-dimensional coordinates of each end of each wire. Fitness was evaluated by simulating each candidate according to an electromagnetic wiring code, and the best-of-run individual was then built and tested. The authors describe the shape of this antenna, which does not resemble traditional antennas and has no obvious symmetry, as "unusually weird" and "counter-intuitive" (p.52), yet it had a nearly uniform radiation pattern with high bandwidth both in simulation and in experimental testing, excellently matching the prior specification. The authors conclude that a genetic algorithm-based method for antenna design shows "remarkable promise". "...this new design procedure is capable of finding genetic antennas able to effectively solve difficult antenna problems, and it will be particularly useful in situations where existing designs are not adequate" (p.52).
- Financial markets
Mahfoud and Mani 1996 used a genetic algorithm to predict the future performance of 1600 publicly traded stocks. Specifically, the GA was tasked with forecasting the relative return of each stock, defined as that stock's return minus the average return of all 1600 stocks over the time period in question, 12 weeks (one calendar quarter) into the future. As input, the GA was given historical data about each stock in the form of a list of 15 attributes, such as price-to-earnings ratio and growth rate, measured at various past points in time; the GA was asked to evolve a set of if/then rules to classify each stock and to provide, as output, both a recommendation on what to do with regards to that stock (buy, sell, or no prediction) and a numerical forecast of the relative return. The GA's results were compared to those of an established neural net-based system which the authors had been using to forecast stock prices and manage portfolios for three years previously. Of course, the stock market is an extremely noisy and nonlinear system, and no predictive mechanism can be correct 100% of the time; the challenge is to find a predictor that is accurate more often than not.
In the experiment, the GA and the neural net each made forecasts at the end of the week for each one of the 1600 stocks, for twelve consecutive weeks. Twelve weeks after each prediction, the actual performance was compared with the predicted relative return. Overall, the GA significantly outperformed the neural network: in one trial run, the GA correctly predicted the direction of one stock 47.6% of the time, made no prediction 45.8% of the time, and made an incorrect prediction only 6.6% of the time, for an overall predictive accuracy of 87.8%. Although the neural network made definite predictions more often, it was also wrong in its predictions more often (in fact, the authors speculate that the GA's greater ability to make no prediction when the data were uncertain was a factor in its success; the neural net always produces a prediction unless explicitly restricted by the programmer). In the 1600-stock experiment, the GA produced a relative return of +5.47%, versus +4.40% for the neural net - a statistically significant difference. In fact, the GA also significantly outperformed three major stock market indices - the S&P 500, the S&P 400, and the Russell 2000 - over this period; chance was excluded as the cause of this result at the 95% confidence level. The authors attribute this compelling success to the ability of the genetic algorithm to learn nonlinear relationships not readily apparent to human observers, as well as the fact that it lacks a human expert's "a priori bias against counterintuitive or contrarian rules" (p.562).
Similar success was achieved by Andreou, Georgopoulos and Likothanassis 2002, who used hybrid genetic algorithms to evolve neural networks that predicted the exchange rates of foreign currencies up to one month ahead. As opposed to the last example, where GAs and neural nets were in competition, here the two worked in concert, with the GA evolving the architecture (number of input units, number of hidden units, and the arrangement of the links between them) of the network which was then trained by a filter algorithm.
As historical information, the algorithm was given 1300 previous raw daily values of five currencies - the American dollar, the German deutsche mark, the French franc, the British pound, and the Greek drachma - and asked to predict their future values 1, 2, 5, and 20 days ahead. The hybrid GA's performance, in general, showed a "remarkable level of accuracy" (p.200) in all cases tested, outperforming several other methods including neural networks alone. Correlations for the one-day case ranged from 92 to 99%, and though accuracy decreased over increasingly greater time lags, the GA continued to be "quite successful" (p.206) and clearly outperformed the other methods. The authors conclude that "remarkable prediction success has been achieved in both a one-step ahead and a multistep predicting horizon" (p.208) - in fact, they state that their results are better by far than any related predictive strategies attempted on this data series or other currencies.
The uses of GAs on the financial markets have begun to spread into real-world brokerage firms. Naik 1996 reports that LBS Capital Management, an American firm headquartered in Florida, uses genetic algorithms to pick stocks for a pension fund it manages. Coale 1997 and Begley and Beals 1995 report that First Quadrant, an investment firm in California that manages over $2.2 billion, uses GAs to make investment decisions for all of their financial services. Their evolved model earns, on average, $255 for every $100 invested over six years, as opposed to $205 for other types of modeling systems.
- Game playing
One of the most novel and compelling demonstrations of the power of genetic algorithms was presented by Chellapilla and Fogel 2001, who used a GA to evolve neural networks that could play the game of checkers. The authors state that one of the major difficulties in these sorts of strategy-related problems is the credit assignment problem - in other words, how does one write a fitness function? It has been widely believed that the mere criterion of win, lose or draw does not provide sufficient information for an evolutionary algorithm to figure out what constitutes good play.
In this paper, Chellapilla and Fogel overturn that assumption. Given only the spatial positions of pieces on the checkerboard and the total number of pieces possessed by each side, they were able to evolve a checkers program that plays at a level competitive with human experts, without any intelligent input as to what constitutes good play - indeed, the individuals in the evolutionary algorithm were not even told what the criteria for a win were, nor were they told the result of any one game.
In Chellapilla and Fogel's representation, the game state was represented by a numeric list of 32 elements, with each position in the list corresponding to an available position on the board. The value at each position was either 0 for an unoccupied square, -1 if that square was occupied by an enemy checker, +1 if that square was occupied by one of the program's checkers, and -K or +K for a square occupied by an enemy or friendly king. (The value of K was not pre-specified, but again was determined by evolution over the course of the algorithm.) Accompanying this was a neural network with multiple processing layers and one input layer with a node for each of the possible 4x4, 5x5, 6x6, 7x7 and 8x8 squares on the board. The output of the neural net for any given arrangement of pieces was a value from -1 to +1 indicating how good it felt that position was for it. For each move, the neural network was presented with a game tree listing all possible moves up to four turns into the future, and a move decision was made based on which branch of the tree produced the best results for it.
The evolutionary algorithm began with a population of 15 neural networks with randomly generated weights and biases assigned to each node and link; each individual then reproduced once, generating an offspring with variations in the values of the network. These 30 individuals then competed for survival by playing against each other, with each individual competing against 5 randomly chosen opponents per turn. 1 point was awarded for each win and 2 points were deducted for each loss. The 15 best performers, based on total score, were selected to produce offspring for the next generation, and the process repeated. Evolution was continued for 840 generations (approximately six months of computer time).
Class Rating Senior Master 2400+ Master 2200-2399 Expert 2000-2199 Class A 1800-1999 Class B 1600-1799 Class C 1400-1599 Class J < 200 The single best individual that emerged from this selection was entered as a competitor on the gaming website http://www.zone.com/. Over a period of two months, it played against 165 human opponents comprising a range of high skill levels, from class C to master, according to the ranking system of the United States Chess Federation (shown at left, some ranks omitted for clarity). Of these games, the neural net won 94, lost 39 and drew 32; based on the rankings of the opponents in these games, the evolved neural net was equivalent to a player with a mean rating of 2045.85, placing it at the expert level - a higher ranking than 99.61% of over 80,000 players registered at the website. One of the neural net's most significant victories was when it defeated a player ranked 98th out of all registered players, whose rating was just 27 points below master level.
Tests conducted with a simple piece-differential program (which bases moves solely on the difference between the number of checkers remaining to each side) with an eight-move look-ahead showed the neural net to be significantly superior, with a rating over 400 points higher. "A program that relies only on the piece count and an eight-ply search will defeat a lot of people, but it is not an expert. The best evolved neural network is" (p.425). Even when it was searching positions two further moves ahead than the neural net, the piece-differential program lost decisively in eight out of ten games. This conclusively demonstrates that the evolved neural net is not merely counting pieces, but is somehow processing spatial characteristics of the board to decide its moves. The authors point out that opponents on zone.com often commented that the neural net's moves were "strange", but its overall level of play was described as "very tough" or with similar complimentary terms.
To further test the evolved neural network (which the authors named "Anaconda" since it often won by restricting its opponents' mobility), it was played against a commercial checkers program, Hoyle's Classic Games, distributed by Sierra Online (Chellapilla and Fogel 2000). This program comes with a variety of built-in characters, each of whom plays at a different skill level. Anaconda was tested against three characters ("Beatrice", "Natasha" and "Leopold") designated as expert-level players, playing one game as red and one game as white against each of them with a six-ply look-ahead. Though the authors doubted that this depth of look-ahead would give Anaconda the ability to play at the expert skill level it had previously shown, it won six straight victories out of all six games played. Based on this outcome, the authors expressed skepticism over whether the Hoyle software played at the skill level advertised, though it should be noted that they reached this conclusion based solely on the ease with which Anaconda defeated it!
The ultimate test of Anaconda was given in Chellapilla and Fogel 2002, where the evolved neural net was matched against the best checkers player in the world: Chinook, a program designed principally by Dr. Jonathan Schaeffer of the University of Alberta. Rated at 2814 in 1996 (with its closest human competitors rated in the 2600s), Chinook incorporates a book of opening moves provided by human grandmasters, a sophisticated set of middle-game algorithms, and a complete database of all possible moves with ten pieces on the board or less, so it never makes a mistake in the endgame. An enormous amount of human intelligence and expertise went into the design of this program.
Chellapilla and Fogel pitted Anaconda against Chinook in a 10-game tournament, with Chinook playing at a 5-ply skill level, making it roughly approximate to master level. Chinook won this contest, four wins to two with four draws. (Interestingly, the authors note, in two of the games that ended as draws, Anaconda held the lead with four kings to Chinook's three. Furthermore, one of Chinook's wins came from a 10-ply series of movies drawn from its endgame database, which Anaconda with an 8-ply look-ahead could not have anticipated. If Anaconda had had access to an endgame database of the same quality as Chinook's, the outcome of the tournament might well have been a victory for Anaconda, four wins to three.) These results "provide good support for the expert-level rating that Anaconda earned on www.zone.com" (p.76), with an overall rating of 2030-2055, comparable to the 2045 rating it earned by playing against humans. While Anaconda is not an invulnerable player, it is able to play competitively at the expert level and hold its own against a variety of extremely skilled human checkers players. When one considers the very simple fitness criterion under which these results were obtained, the emergence of Anaconda provides dramatic corroboration of the power of evolution.
- Geophysics
Sambridge and Gallagher 1993 used a genetic algorithm to locate earthquake hypocenters based on seismological data. (The hypocenter is the point beneath the Earth's surface at which an earthquake begins. The epicenter is the point on the surface directly above the hypocenter.) This is an exceedingly complex task, since the properties of seismic waves depend on the properties of the rock layers through which they travel. The traditional method for locating the hypocenter relies upon what is known as a seismic inversion algorithm, which starts with a best guess of the location, calculates the derivatives of wave travel time with respect to source position, and performs a matrix operation to provide an updated location. This process is repeated until an acceptable solution is reached. (This Post of the Month, from November 2003, provides more information.) However, this method requires derivative information and is prone to becoming trapped on local optima.
A location algorithm that does not depend on derivative information or velocity models can avoid these shortfalls by calculating only the forward problem - the difference between observed and predicted wave arrival times for different hypocenter locations. However, an exhaustive search based on this method would be far too computationally expensive. This, of course, is precisely the type of optimization problem at which genetic algorithms excel. Like all GAs, the one proposed by the cited paper is parallel in nature - rather than progressively perturbing a single hypocenter closer and closer to the solution, it begins with a cloud of potential hypocenters which shrinks over time to converge on a single solution. The authors state that their approach "can rapidly locate near optimal solutions without an exhaustive search of the parameter space" (p.1467), displays "highly organized behavior resulting in efficient search" and is "a compromise between the efficiency of derivative based methods and the robustness of a fully nonlinear exhaustive search" (p.1469). The authors conclude that their genetic algorithm is "efficient for truly global optimization" (p.1488) and "a powerful new tool for performing robust hypocenter location" (p.1489).
- Materials engineering
Giro, Cyrillo and Galvão 2002 used genetic algorithms to design electrically conductive carbon-based polymers known as polyanilines. These polymers, a recently invented class of synthetic materials, have "large technological potential applications" and may open up windows onto "new fundamental physical phenomena" (p.170). However, due to their high reactivity, carbon atoms can form a virtually infinite number of structures, making a systematic search for new molecules with interesting properties all but impossible. In this paper, the authors apply a GA-based approach to the task of designing new molecules with pre-specified properties, starting with a randomly generated population of initial candidates. They conclude that their methodology can be a "very effective tool" (p.174) to guide experimentalists in the search for new compounds and is general enough to be extended to the design of novel materials belonging to virtually any class of molecules.
Weismann, Hammel and Bäck 1998 applied evolutionary algorithms to a "nontrivial" (p.162) industrial problem: the design of multilayer optical coatings used for filters that reflect, transmit or absorb light of specified frequencies. These coatings are used in the manufacture of sunglasses, for example, or compact discs. Their manufacture is a precise task: the layers must be laid down in a particular sequence and particular thicknesses to produce the desired result, and uncontrollable environmental variations in the manufacturing environment such as temperature, pollution and humidity may affect the performance of the finished product. Many local optima are not robust against such variations, meaning that maximum product quality must be paid for with higher rates of undesirable deviation. The particular problem considered in this paper also had multiple criteria: in addition to the reflectance, the spectral composition (color) of the reflected light was also considered.
The EA operated by varying the number of coating layers and the thickness of each, and produced designs that were "substantially more robust to parameter variation" (p.166) and had higher average performance than traditional methods. The authors conclude that "evolutionary algorithms can compete with or even outperform traditional methods" (p.167) of multilayer optical coating design, without having to incorporate domain-specific knowledge into the search function and without having to seed the population with good initial designs.
One more use of GAs in the field of materials engineering merits mention: Robin et al. 2003 used GAs to design exposure patterns for an electron lithography beam, used to etch submicrometer-scale structures onto integrated circuits. Designing these patterns is a highly difficult task; it is cumbersome and wasteful to determine them experimentally, but the high dimensionality of the search space defeats most search algorithms. As many as 100 parameters must be optimized simultaneously to control the electron beam and prevent scattering and proximity effects that would otherwise ruin the fine structures being sculpted. The forward problem - determining the resulting structure as a function of the electron dose - is straightforward and easy to simulate, but the inverse problem of determining the electron dose to produce a given structure, which is what is being solved here, is far harder and no deterministic solution exists.
Genetic algorithms, which are "known to be able to find good solutions to very complex problems of high dimensionality" (p.75) without needing to be supplied with domain-specific information on the topology of the search landscape, were applied successfully to this problem. The paper's authors employed a steady-state GA with roulette-wheel selection in a computer simulation, which yielded "very good optimized" (p.77) exposure patterns. By contrast, a type of hill-climber known as a simplex-downhill algorithm was applied to the same problem, without success; the SD method quickly became trapped in local optima which it could not escape, yielding solutions of poor quality. A hybrid approach of the GA and SD methods also could not improve on the results delivered by the GA alone.
- Mathematics and algorithmics
Although some of the most promising applications and compelling demonstrations of GAs' power are in the field of engineering design, they are also relevant to "pure" mathematical problems. Haupt and Haupt 1998 (p.140) discuss the use of GAs to solve high-order nonlinear partial differential equations, typically by finding the values for which the equations equal zero, and give as an example a near-perfect GA solution for the coefficients of the fifth-order Super Korteweg-de Vries equation.
Sorting a list of items into order is an important task in computer science, and a sorting network is an efficient way to accomplish this. A sorting network is a fixed list of comparisons performed on a set of a given size; in each comparison, two elements are compared and exchanged if not in order. Koza et al. 1999, p. 952 used genetic programming to evolve minimal sorting networks for 7-item sets (16 comparisons), 8-item sets (19 comparisons), and 9-item sets (25 comparisons). Mitchell 1996, p.21, discusses the use of genetic algorithms by W. Daniel Hillis to find a 61-comparison sorting network for a 16-item set, only one step more than the smallest known. This latter example is particularly interesting for two innovations it used: diploid chromosomes, and more notably, host-parasite coevolution. Both the sorting networks and the test cases evolved alongside each other; sorting networks were given higher fitness based on how many test cases they sorted correctly, while test cases were given higher fitness based on how many sorting networks they could "trick" into sorting incorrectly. The GA with coevolution performed significantly better than the same GA without it.
One final, noteworthy example of GAs in the field of algorithmics can be found in Koza et al. 1999, who used genetic programming to discover a rule for the majority classification problem in one-dimensional cellular automata that is better than all known rules written by humans. A one-dimensional cellular automaton can be thought of as a finite tape with a given number of positions (cells) on it, each of which can hold either the state 0 or the state 1. The automaton runs for a given number of time steps; at each step, every cell acquires a new value based on its previous value and the value of its nearest neighbors. (The Game of Life is a two-dimensional cellular automaton.) The majority classification problem entails finding a table of rules such that, if more than half the cells on the tape are 1 initially, all the cells go to 1; otherwise all the cells go to 0. The challenge lies in the fact that any individual cell can only access information about its nearest neighbors; therefore, good rule sets must somehow find a way to transmit information about distant regions of the tape.
It is known that a perfect solution to this problem does not exist - no rule set can accurately classify all possible initial configurations - but over the past twenty years, there has been a long succession of increasingly better solutions. In 1978, three researchers developed the so-called GKL rule, which correctly classifies 81.6% of the possible initial states. In 1993, a better rule with an accuracy of 81.8% was discovered; in 1995, another rule with accuracy of 82.178% was found. All of these rules required significant work by intelligent, creative humans to develop. By contrast, the best rule discovered by a run of genetic programming, given in Koza et al. 1999, p.973, has an overall accuracy of 82.326% - better than any of the human-created solutions that have been developed over the last two decades. The authors note that their new rules are qualitatively different from previously published rules, employing fine-grained internal representations of state density and intricate sets of signals for communicating information over long distance.
- Military and law enforcement
Kewley and Embrechts 2002 used genetic algorithms to evolve tactical plans for military battles. The authors note that "[p]lanning for a tactical military battle is a complex, high-dimensional task which often bedevils experienced professionals" (p.163), not only because such decisions are usually made under high-stress conditions, but also because even simple plans require a great number of conflicting variables and outcomes to take into account: minimizing friendly casualties, maximizing enemy casualties, controlling desired terrain, conserving resources, and so on. Human planners have difficulty dealing with the complexities of this task and often must resort to "quick and dirty" approaches, such as doing whatever worked last time.
To overcome these difficulties, the authors of the cited paper developed a genetic algorithm to automate the creation of battle plans, in conjunction with a graphical battle simulator program. The commander enters the preferred outcome, and the GA automatically evolves a battle plan; in the simulation used, factors such as the topography of the land, vegetative cover, troop movement speed, and firing accuracy were taken into account. In this experiment, co-evolution was also used to improve the quality of the solutions: battle plans for the enemy forces evolved concurrently with friendly plans, forcing the GA to correct any weaknesses in its own plan that an enemy could exploit. To measure the quality of solutions produced by the GA, they were compared to battle plans for the same scenario produced by a group of "experienced military experts... considered to be very capable of developing tactical courses of action for the size forces used in this experiment" (p.166). These seasoned experts both developed their own plan and, when the GA's solution was complete, were given a chance to examine it and modify it as they saw fit. Finally, all the sets of plans were run multiple times on the simulator to determine their average quality.
The results speak for themselves: the evolved solution outperformed both the military experts' own plan and the plan produced by their modifications to the GA's solution. "...[T]he plans produced by automated algorithms had a significantly higher mean performance than those generated by experienced military experts" (p.161). Furthermore, the authors note that the GA's plan made good tactical sense. (It involved a two-pronged attack on the enemy position by mechanized infantry platoons supported by attack helicopters and ground scouts, in conjunction with unmanned aerial vehicles conducting reconnaissance to direct artillery fire.) In addition, the evolved plan included individual friendly units performing doctrinal missions - an emergent property that appeared during the course of the run, rather than being specified by the experimenter. In increasingly networked modern battlefields, the attractive potential of an evolutionary algorithm that can automate the production of high-quality tactical plans should be obvious.
An interesting use of GAs in law enforcement was reported in Naik 1996, which described the "FacePrints" software, a project to help witnesses identify and describe criminal suspects. The cliched image of the police sketch artist drawing a picture of the suspect's face in response to witnesses' promptings is a difficult and inefficient method: most people are not good at describing individual aspects of a person's face, such as the size of the nose or shape of the jaw, but instead are better at recognizing whole faces. FacePrints takes advantage of this by using a genetic algorithm that evolves pictures of faces based on databases of hundreds of individual features that can be combined in a vast number of ways. The program shows randomly generated face images to witnesses, who pick the ones that most resemble the person they saw; the selected faces are then mutated and bred together to generate new combinations of features, and the process repeats until an accurate portrait of the suspect's face emerges. In one real-life robbery case, the final portraits created by the three witnesses were strikingly similar, and the resulting picture was printed in the local paper.
- Molecular biology
In living things, transmembrane proteins are proteins that protrude through a cellular membrane. Transmembrane proteins often perform important functions such as sensing the presence of certain substances outside the cell or transporting them into the cell. Understanding the behavior of a transmembrane protein requires identifying the segment of that protein that is actually embedded within the membrane, which is called the transmembrane domain. Over the last two decades, molecular biologists have published a succession of increasingly accurate algorithms for this purpose.
All proteins used by living things are made up of the same 20 amino acids. Some of these amino acids are hydrophobic, meaning they are repelled by water, and some are hydrophilic, meaning they are attracted to water. Amino acid sequences that are part of a transmembrane domain are more likely to be hydrophobic. However, hydrophobicity is not a precisely defined characteristic, and there is no one agreed-upon scale for measuring it.
Koza et al. 1999, chapter 16, used genetic programming to design an algorithm to identify transmembrane domains of a protein. Genetic programming was given a set of standard mathematical operators to work with, as well as a set of boolean amino-acid-detecting functions that return +1 if the amino acid at a given position is the amino acid they detect and otherwise return -1. (For example, the A? function takes as an argument one number corresponding to a position within the protein, and returns +1 if the amino acid at that position is alanine, which is denoted by the letter A; otherwise it returns -1). A single shared memory variable kept a running count of the overall sum, and when the algorithm completed, the protein segment was identified as a transmembrane domain if its value was positive. Given only these tools, would it entail the creation of new information for a human designer to produce an efficient solution to this problem?
The solutions produced by genetic programming were evaluated for fitness by testing them on 246 protein segments whose transmembrane status was known. The best-of-run individual was then evaluated on 250 additional, out-of-sample, test cases and compared to the performance of the four best known human-written algorithms for the same purpose. The result: Genetic programming produced a transmembrane segment-identifying algorithm with an overall error rate of 1.6% - significantly lower than all four human-written algorithms, the best of which had an error rate of 2.5%. The genetically designed algorithm, which the authors dubbed the 0-2-4 rule, operates as follows:
- Increment the running sum by 4 for each instance of glutamic acid (an electrically charged and highly hydrophilic) amino acid in the protein segment.
- Increment the running sum by 0 for each instance of alanine, phenylalanine, isoleucine, leucine, methionine, or valine (all highly hydrophobic amino acids) in the protein segment.
- Increment the running sum by 2 for each instance of all other amino acids.
- If [(SUM - 3.1544)/0.9357] is less than the length of the protein segment, classify that segment as a transmembrane domain; otherwise, classify it as a nontransmembrane domain.
- Pattern recognition and data mining
Competition in the telecommunications industry today is fierce, and a new term - "churn" - has been coined to describe the rapid rate at which subscribers switch from one service provider to another. Churn costs telecom carriers a large amount of money each year, and reducing churn is an important factor in increasing profitability. If carriers can contact customers who are likely to switch and offer them special incentives to stay, churn rates can be reduced; but no carrier has the resources to contact more than a small percent of its customers. The problem is therefore how to identify customers who are more likely to churn. All carriers have extensive databases of customer information that can theoretically be used for this purpose; but what method works best for sifting through this vast amount of data to identify the subtle patterns and trends that signify a customer's likelihood of churning?
Au, Chan and Yao 2003 applied genetic algorithms to this problem to generate a set of if-then rules that predict the churning probability of different groups of customers. In their GA, the first generation of rules, all of which had one condition, was generated using a probabilistic induction technique. Subsequent generations then refine these, combining simple, single-condition rules into more complex, multi-condition rules. The fitness measure used an objective "interestingness" measure of correlation which requires no subjective input. The evolutionary data-mining algorithm was tested on a real-world database of 100,000 subscribers provided by a Malaysian carrier, and its performance was compared against two alternative methods: a multilayer neural network and a widely used decision-tree-based algorithm, C4.5. The authors state that their EA was able to discover hidden regularities in the database and was "able to make accurate churn prediction under different churn rates" (p.542), outperforming C4.5 under all circumstances, outperforming the neural network under low monthly churn rates and matching the neural network under larger churn rates, and reaching conclusions more quickly in both cases. Some further advantages of the evolutionary approach are that it can operate efficiently even when some data fields are missing and that it can express its findings in easily understood rule sets, unlike the neural net.
Among some of the more interesting rules discovered by the EA are as follows: subscribers are more likely to churn if they are personally subscribed to the service plan and have not been admitted to any bonus scheme (a potential solution is to admit all such subscribers to bonus schemes); subscribers are more likely to churn if they live in Kuala Lumpur, are between 36 and 44 in age, and pay their bills with cash (presumably because it is easier for subscribers who pay cash, rather than those whose accounts are automatically debited, to switch providers); and subscribers living in Penang who signed up through a certain dealer are more likely to churn (this dealer may be providing poor customer service and should be investigated).
Rizki, Zmuda and Tamburino 2002 used evolutionary algorithms to evolve a complex pattern recognition system with a wide variety of potential uses. The authors note that the task of pattern recognition is increasingly being performed by machine learning algorithms, evolutionary algorithms in particular. Most such approaches begin with a pool of predefined features, from which an EA can select appropriate combinations for the task at hand; by contrast, this approach began from the ground up, first evolving individual feature detectors in the form of expression trees, then evolving cooperative combinations of those detectors to produce a complete pattern recognition system. The evolutionary process automatically selects the number of feature detectors, the complexity of the detectors, and the specific aspects of the data each detector responds to.
To test their system, the authors gave it the task of classifying aircraft based on their radar reflections. The same kind of aircraft can return quite different signals depending on the angle and elevation at which it is viewed, and different kinds of aircraft can return very similar signals, so this is a non-trivial task. The evolved pattern recognition system correctly classified 97.2% of the targets, a higher net percentage than any of the three other techniques - a perceptron neural network, a nearest-neighbor classifier algorithm, and a radial basis algorithm - against which it was tested. (The radial basis network's accuracy was only 0.5% less than the evolved classifier, which is not a statistically significant difference, but the radial basis network required 256 feature detectors while the evolved recognition system used only 17.) As the authors state, "The recognition systems that evolve use fewer features than systems formed using conventional techniques, yet achieve comparable or superior recognition accuracy" (p.607). Various aspects of their system have also been applied to problems including optical character recognition, industrial inspection and medical image analysis.
Hughes and Leyland 2000 also applied multiple-objective GAs to the task of classifying targets based on their radar reflections. High-resolution radar cross section data requires massive amounts of disk storage space, and it is very computationally intensive to produce an actual model of the source from the data. By contrast, the authors' GA-based approach proved very successful, producing a model as good as the traditional iterative approach while reducing the computational overhead and storage requirements to the point where it was feasible to generate good models on a desktop computer. By contrast, the traditional iterative approach requires ten times the resolution and 560,000 times as many accesses of image data to produce models of similar quality. The authors conclude that their results "clearly demonstrate" (p.160) the ability of the GA to process both two- and three-dimensional radar data of any level of resolution with far fewer calculations than traditional methods, while retaining acceptably high accuracy.
- Robotics
The international RoboCup tournament is a project to promote advances in robotics, artificial intelligence, and related fields by providing a standard problem where new technologies can be tried out - specifically, it is an annual soccer tournament between teams of autonomous robots. (The stated goal is to develop a team of humanoid robots that can win against the world-champion human soccer team by 2050; at the present time, most of the competing robot teams are wheeled.) The programs that control the robotic team members must display complex behavior, deciding when to block, when to kick, how to move, when to pass the ball to teammates, how to coordinate defense and offense, and so on. In the simulator league of the 1997 competition, David Andre and Astro Teller entered a team named Darwin United whose control programs had been developed automatically from the ground up by genetic programming, a challenge to the conventional wisdom that "this problem is just too difficult for such a technique" (Andre and Teller 1999, p. 346).
To solve this difficult problem, Andre and Teller provided the genetic programming algorithm with a set of primitive control functions such as turning, moving, kicking, and so on. (These functions were themselves subject to change and refinement during the course of evolution.) Their fitness function, written to reward good play in general rather than scoring specifically, provided a list of increasingly important objectives: getting near the ball, kicking the ball, keeping the ball on the opponent's side of the field, moving in the correct direction, scoring goals, and winning the game. It should be noted that no code was provided to teach the team specifically how to achieve these complex objectives. The evolved programs were then evaluated using a hierarchical selection model: first, the candidate teams were tested on an empty field and rejected if they did not score a goal within 30 seconds. Next, they were evaluated against a team of stationary "kicking posts" that kick the ball toward the opposite side of the field. Thirdly, the team played a game against the winning team from the RoboCup 1997 competition. Finally, teams that scored at least one goal against this team were played off against each other to determine which was best.
Out of 34 teams in its division, Darwin United ultimately came in 17th, placing squarely in the middle of the field and outranking half of the human-written entries. While a tournament victory would undoubtedly have been more impressive, this result is competitive and significant in its own right, and appears even more so in the light of history. About 25 years ago, chess-playing computer programs were in their infancy; a computer had only recently entered even a regional competition for the first time, although it did not win (Sagan 1979, p.286). But "[a] machine that plays chess in the middle range of human expertise is a very capable machine" (ibid.), and it might be said that the same is true of robot soccer. Just as chess-playing machines compete at world grandmaster levels today, what types of systems will genetic programming be producing 20 or 30 years from now?
- Routing and scheduling
Burke and Newall 1999 used genetic algorithms to schedule exams among university students. The timetable problem in general is known to be NP-complete, meaning that no method is known to find a guaranteed-optimal solution in a reasonable amount of time. In such a problem, there are both hard constraints - two exams may not be assigned to the same room at the same time - and soft constraints - students should not be assigned to multiple exams in succession, if possible, to minimize fatigue. Hard constraints must be satisfied, while soft constraints should be satisfied as far as possible. The authors dub their hybrid approach for solving this problem a "memetic algorithm": an evolutionary algorithm with rank-based, fitness-proportionate selection, combined with a local hill-climber to optimize solutions found by the EA. The EA was applied to data sets from four real universities (the smallest of which had an enrollment of 25,000 students), and its results were compared to results produced by a heuristic backtracking method, a well-established algorithm that is among the best known for this problem and that is used at several real universities. Compared to this method, the EA produced a result with a quite uniform 40% reduction in penalty.
He and Mort 2000 applied genetic algorithms to the problem of finding optimal routing paths in telecommunications networks (such as phone networks and the Internet) which are used to relay data from senders to recipients. This is an NP-hard optimization problem, a type of problem for which GAs are "extremely well suited... and have found an enormous range of successful applications in such areas" (p.42). It is also a multiobjective problem, balancing conflicting objectives such as maximizing data throughput, minimizing transmission delay and data loss, finding low-cost paths, and distributing the load evenly among routers or switches in the network. Any successful real-world algorithm must also be able to re-route around primary paths that fail or become congested.
In the authors' hybrid GA, a shortest-path-first algorithm, which minimizes the number of "hops" a given data packet must pass through, is used to generate the seed for the initial population. However, this solution does not take into account link congestion or failure, which are inevitable conditions in real networks, and so the GA takes over, swapping and exchanging sections of paths. When tested on a data set derived from a real Oracle network database, the GA was found to be able to efficiently route around broken or congested links, balancing traffic load and maximizing the total network throughput. The authors state that these results demonstrate the "effectiveness and scalability" of the GA and show that "optimal or near-optimal solutions can be achieved" (p.49).
This technique has found real-world applications for similar purposes, as reported in Begley and Beals 1995. The telecommunications company U.S. West (now merged with Qwest) was faced with the task of laying a network of fiber-optic cable. Until recently, the problem of designing the network to minimize the total length of cable laid was solved by an experienced engineer; now the company uses a genetic algorithm to perform the task automatically. The results: "Design time for new networks has fallen from two months to two days and saves US West $1 million to $10 million each" (p.70).
Jensen 2003 and Chryssolouris and Subramaniam 2001 applied genetic algorithms to the task of generating schedules for job shops. This is an NP-hard optimization problem with multiple criteria: factors such as cost, tardiness, and throughput must all be taken into account, and job schedules may have to be rearranged on the fly due to machine breakdowns, employee absences, delays in delivery of parts, and other complications, making robustness in a schedule an important consideration. Both papers concluded that GAs are significantly superior to commonly used dispatching rules, producing efficient schedules that can more easily handle delays and breakdowns. These results are not merely theoretical, but have been applied to real-world situations:
As reported in Naik 1996, organizers of the 1992 Paralympic Games used a GA to schedule events. As reported in Petzinger 1995, John Deere & Co. has used GAs to generate schedules for a Moline, Illinois plant that manufactures planters and other heavy agricultural equipment. Like luxury cars, these can be built in a wide variety of configurations with many different parts and options, and the vast number of possible ways to build them made efficient scheduling a seemingly intractable problem. Productivity was hampered by scheduling bottlenecks, worker teams were bickering, and money was being lost. Finally, in 1993, Deere turned to Bill Fulkerson, a staff analyst and engineer who conceived of using a genetic algorithm to produce schedules for the plant. Overcoming initial skepticism, the GA quickly proved itself: monthly output has risen by 50 percent, overtime has nearly vanished, and other Deere plants are incorporating GAs into their own scheduling.
As reported in Rao 1998, Volvo has used an evolutionary program called OptiFlex to schedule its million-square-foot factory in Dublin, Virginia, a task that requires handling hundreds of constraints and millions of possible permutations for each vehicle. Like all genetic algorithms, OptiFlex works by randomly combining different scheduling possibilities and variables, determines their fitness by ranking them according to costs, benefits and constraints, then causes the best solutions to swap genes and sends them back into the population for another trial. Until recently, this daunting task was handled by a human engineer who took up to four days to produce the schedule for each week; now, thanks to GAs, this task can be completed in one day with minimal human intervention.
As reported in Lemley 2001, United Distillers and Vintners, a Scottish company that is the largest and most profitable spirits distributor in the world and accounts for over one-third of global grain whiskey production, uses a genetic algorithm to manage its inventory and supply. This is a daunting task, requiring the efficient storage and distribution of over 7 million barrels containing 60 distinct recipes among a vast system of warehouses and distilleries, depending on a multitude of factors such as age, malt number, wood type and market conditions. Previously, coordinating this complex flow of supply and demand required five full-time employees. Today, a few keystrokes on a computer instruct a genetic algorithm to generate a new schedule each week, and warehouse efficiency has nearly doubled.
Beasley, Sonander and Havelock 2001 used a GA to schedule airport landings at London Heathrow, the United Kingdom's busiest airport. This is a multiobjective problem that involves, among other things, minimizing delays and maximizing number of flights while maintaining adequate separation distances between planes (air vortices that form in a plane's wake can be dangerous to another flying too closely behind). When compared to actual schedules from a busy period at the airport, the GA was able to reduce average wait time by 2-5%, equating to one to three extra flights taking off and landing per hour - a significant improvement. However, even greater improvements have been achieved: as reported in Wired 2002, major international airports and airlines such as Heathrow, Toronto, Sydney, Las Vegas, San Francisco, America West Airlines, AeroMexico, and Delta Airlines are using genetic algorithms to schedule takeoffs, landings, maintenance and other tasks, in the form of Ascent Technology's SmartAirport Operations Center software (see http://www.ascent.com/faq.html). Breeding and mutating solutions in the form of schedules that incorporate thousands of variables, "Ascent beats humans hands-down, raising productivity by up to 30 percent at every airport where it's been implemented."
- Systems engineering
Benini and Toffolo 2002 applied a genetic algorithm to the multi-objective task of designing wind turbines used to generate electric power. This design "is a complex procedure characterized by several trade-off decisions... The decision-making process is very difficult and the design trends are not uniquely established" (p.357); as a result, there are a number of different turbine types in existence today and no agreement on which, if any, is optimal. Mutually exclusive objectives such as maximum annual energy production and minimal cost of energy must be taken into account. In this paper, a multi-objective evolutionary algorithm was used to find the best trade-offs between these goals, constructing turbine blades with the optimal configuration of characteristics such as tip speed, hub/tip ratio, and chord and twist distribution. In the end, the GA was able to find solutions competitive with commercial designs, as well as more clearly elucidate the margins by which annual energy production can be improved without producing overly expensive designs.
Haas, Burnham and Mills 1997 used a multiobjective genetic algorithm to optimize the beam shape, orientation and intensity of X-ray emitters used in targeted radiotherapy to destroy cancerous tumors while sparing healthy tissue. (X-ray photons aimed at a tumor tend to be partially scattered by structures within the body, unintentionally damaging internal organs. The challenge is to minimize this effect while maximizing the radiation dose delivered to the tumor.) Using a rank-based fitness model, the researchers began with the solution produced by the conventional method, an iterative least-squares approach, and then used the GA to modify and improve it. By constructing a model of a human body and exposing it to the beam configuration evolved by the GA, they found good agreement between the predicted and actual distributions of radiation. The authors conclude that their results "show a sparing of [healthy organs] that could not be achieved using conventional techniques" (p.1745).
Lee and Zak 2002 used a genetic algorithm to evolve a set of rules to control an automotive anti-lock braking system. While the ability of antilock brake systems to reduce stopping distance and improve maneuverability has saved many lives, the performance of an ABS is dependent on road surface conditions: for example, an ABS controller that is optimized for dry asphalt will not work as well on wet or icy roads, and vice versa. In this paper, the authors propose a GA to fine-tune an ABS controller that can identify the road surface properties (by monitoring wheel slip and acceleration) and respond accordingly, delivering the appropriate amount of braking force to maximize the wheels' traction. In testing, the genetically tuned ABS "exhibits excellent tracking properties" (p.206) and was "far superior" (p.209) to two other methods of braking maneuvers, quickly finding new optimal values for wheel slip when the type of terrain changes beneath a moving car and reducing total stopping distance. "The lesson we learned from our experiment... is that a GA can help to fine-tune even a well-designed controller. In our case, we already had a good solution to the problem; yet, with the help of a GA, we were able to improve the control strategy significantly. In summary, it seems that it is worthwhile to try to apply a GA, even to a well-designed controller, because there is a good chance that one can find a better set of the controller settings using GAs" (p.211).
As cited in Schechter 2000, Dr. Peter Senecal of the University of Wisconsin used small-population genetic algorithms to improve the efficiency of diesel engines. These engines work by injecting fuel into a combustion chamber which is filled with extremely compressed and therefore extremely hot air, hot enough to cause the fuel to explode and drive a piston that produces the vehicle's motive force. This basic design has changed little since it was invented by Rudolf Diesel in 1893; although vast amounts of effort have been put into making improvements, this is a very difficult task to perform analytically because it requires precise knowledge of the turbulent behavior displayed by the fuel-air mixture and simultaneous variation of many interdependent parameters. Senecal's approach, however, eschewed the use of such problem-specific knowledge and instead worked by evolving parameters such as the pressure of the combustion chamber, the timing of the fuel injections and the amount of fuel in each injection. The result: the simulation produced an improved engine that consumed 15% less fuel than a normal diesel engine and produced one-third as much nitric oxide exhaust and half as much soot. Senecal's team then built a real diesel engine according to the specifications of the evolved solution and got the same results. Senecal is now moving on to evolving the geometry of the engine itself, hopefully producing even greater improvements.
As cited in Begley and Beals 1995, Texas Instruments used a genetic algorithm to optimize the layout of components on a computer chip, placing structures so as to minimize the overall area and create the smallest chip possible. Using a connection strategy that no human had thought of, the GA came up with a design that took 18% less space.
Finally, as cited in Ashley 1992, a proprietary software system known as Engineous that employs genetic algorithms is being used by companies in the aerospace, automotive, manufacturing, turbomachinery and electronics industries to design and improve engines, motors, turbines and other industrial devices. In the words of its creator, Dr. Siu Shing Tong, Engineous is "a master 'tweaker,' tirelessly trying out scores of 'what-if' scenarios until the best possible design emerges" (p.49). In one trial of the system, Engineous was able to produce a 0.92 percent increase in the efficiency of an experimental turbine in only one week, while ten weeks of work by a human designer produced only a 0.5 percent improvement.
Granted, Engineous does not rely solely on genetic algorithms; it also employs numerical optimization techniques and expert systems which use logical if-then rules to mimic the decision-making process of a human engineer. However, these techniques are heavily dependent on domain-specific knowledge, lack general applicability, and are prone to becoming trapped on local optima. By contrast, the use of genetic algorithms allows Engineous to explore regions of the search space that other methods miss.
Engineous has found widespread use in a variety of industries and problems. Most famously, it was used to improve the turbine power plant of the Boeing 777 airliner; as reported in Begley and Beals 1995, the genetically optimized design was almost 1% more fuel-efficient than previous engines, which in a field such as this is "a windfall". Engineous has also been used to optimize the configuration of industrial DC motors, hydroelectric generators and steam turbines, to plan out power grids, and to design superconducting generators and nuclear power plants for orbiting satellites. Rao 1998 also reports that NASA has used Engineous to optimize the design of a high-altitude airplane for sampling ozone depletion, which must be both light and efficient.
Creationist arguments
As one might expect, the real-world demonstration of the power of evolution that GAs represent has proven surprising and disconcerting for creationists, who have always claimed that only intelligent design, not random variation and selection, could have produced the information content and complexity of living things. They have therefore argued that the success of genetic algorithms does not allow us to infer anything about biological evolution. The criticisms of two anti-evolutionists, representing two different viewpoints, will be addressed: young-earth creationist Dr. Don Batten of Answers in Genesis, who has written an article entitled " Genetic algorithms -- do they show that evolution works?", and old-earth creationist and intelligent-design advocate Dr. William Dembski, whose recent book No Free Lunch (Dembski 2002) discusses this topic.
Don Batten
- Some traits in living things are qualitative, whereas GAs are always quantitative
Batten states that GAs must be quantitative, so that any improvement can be selected for. This is true. He then goes on to say, "Many biological traits are qualitative--it either works or it does not, so there is no step-wise means of getting from no function to the function." This assertion has not been demonstrated, however, and is not supported by evidence. Batten does not even attempt to give an example of a biological trait that either "works or does not" and thus cannot be built up in a stepwise fashion.
But even if he did offer such a trait, how could he possibly prove that there is no stepwise path to it? Even if we do not know of such a path, does it follow that there is none? Of course not. Batten is effectively claiming that if we do not understand how certain traits evolved, then it is impossible for those traits to have evolved - a classic example of the elementary logical fallacy of argument from ignorance. The search space of all possible variants of any given biological trait is enormous, and in most cases our knowledge subsumes only an infinitesimal fraction of the possibilities. There may well be numerous paths to a structure which we do not yet know about; there is no reason whatsoever to believe our current ignorance sets limits on our future progress. Indeed, history gives us reason to be confident: scientists have made enormous progress in explaining the evolution of many complex biological structures and systems, both macroscopic and microscopic (for example, see these pages on the evolution of complex molecular systems, "clock" genes, the woodpecker's tongue or the bombardier beetle). We are justified in believing it likely that the ones that have so far eluded us will also be made clear in the future.
In fact, GAs themselves give us an excellent reason to believe this. Many of the problems to which they have been applied are complex engineering and design issues where the solution was not known ahead of time and therefore the problem could not be "rigged" to aid the algorithm's success. If the creationists were correct, it would have been entirely reasonable to expect genetic algorithms to fail dismally time after time when applied to these problems, but instead, just the opposite has occurred: GAs have discovered powerful, high-quality solutions to difficult problems in a diverse variety of fields. This calls into serious question whether there even are any problems such as Batten describes, whose solutions are inaccessible to an evolutionary process.
- GAs select for one trait at a time, whereas living things are multidimensional
Batten states that in GAs, "A single trait is selected for, whereas any living thing is multidimensional", and asserts that in living things with hundreds of traits, "selection has to operate on all traits that affect survival", whereas "[a] GA will not work with three or four different objectives, or I dare say even just two."
This argument reveals Batten's profound ignorance of the relevant literature. Even a cursory survey of the work done on evolutionary algorithms (or a look at an earlier section of this essay) would have revealed that multiobjective genetic algorithms are a major, thriving area of research within the broader field of evolutionary computation and prevented him from making such an embarrassingly incorrect claim. There are journal articles, entire issues of prominent journals on evolutionary computation, entire conferences, and entire books on the topic of multiobjective GAs. Coello 2000 provides a very extensive survey, with five pages of references to papers on the use of multiobjective genetic algorithms in a broad range of fields; see also Fleming and Purshouse 2002; Hanne 2000; Zitzler and Thiele 1999; Fonseca and Fleming 1995; Srinivas and Deb 1994; Goldberg 1989, p.197. For some books and papers discussing the use of multiobjective GAs to solve specific problems, see: Obayashi et al. 2000; Sasaki et al. 2001; Benini and Toffolo 2002; Haas, Burnham and Mills 1997; Chryssolouris and Subramaniam 2001; Hughes and Leyland 2000; He and Mort 2000; Kewley and Embrechts 2002; Beasley, Sonander and Havelock 2001; Sato et al. 2002; Tang et al. 1996; Williams, Crossley and Lang 2001; Koza et al. 1999; Koza et al. 2003. For a comprehensive repository of citations on multiobjective GAs, see http://www.lania.mx/~ccoello/EMOO/.
- GAs do not allow the possibility of extinction or error catastrophe
Batten claims that, in GAs, "Something always survives to carry on the process", while this is not necessarily true in the real world - in short, GAs do not allow the possibility of extinction.
However, this is not true; extinction can occur. For example, some GAs use a model of selection called thresholding, in which individuals must have a fitness higher than some predetermined level to survive and reproduce (Haupt and Haupt 1998, p. 37). If no individual meets this standard in such a GA, the population can indeed go extinct. But even in GAs that do not use thresholding, states analogous to extinction can occur. If mutation rates are too high or selective pressures too strong, then a GA will never find a feasible solution. The population may become hopelessly scrambled as deleterious mutations building up faster than selection can remove them disrupt fitter candidates (error catastrophe), or it may thrash around helplessly, unable to achieve any gain in fitness large enough to be selected for. Just as in nature, there must be a balance or a solution will never be reached. The one advantage a programmer has in this respect is that, if this does happen, he can reload the program with different values - for population size, for mutation rate, for selection pressure - and start over again. Obviously this is not an option for living things. Batten says, "There is no rule in evolution that says that some organism(s) in the evolving population will remain viable no matter what mutations occur," but there is no such rule in genetic algorithms either.
Batten also states that "the GAs that I have looked at artificially preserve the best of the previous generation and protect it from mutations or recombination in case nothing better is produced in the next iteration". This criticism will be addressed in the next point.
- GAs ignore the cost of substitution
Batten's next claim is that GAs neglect "Haldane's Dilemma", which states that an allele which contributes less to an organism's fitness will take a correspondingly longer time to become fixated in a population. Obviously, what he is referring to is the elitist selection technique, which automatically selects the best candidate at each generation no matter how small its advantage over its competitors is. He is right to suggest that, in nature, very slight competitive advantages might take much longer to propagate. Genetic algorithms are not an exact model of biological evolution in this respect.
However, this is beside the point. Elitist selection is an idealization of biological evolution - a model of what would happen in nature if chance did not intervene from time to time. As Batten acknowledges, Haldane's dilemma does not state that a slightly advantageous mutation will never become fixed in a population; it states that it will take more time for it to do so. However, when computation time is at a premium or a GA researcher wishes to obtain a solution more quickly, it may be desirable to skip this process by implementing elitism. An important point is that elitism does not affect which mutations arise, merely makes certain the selection of the best ones that do arise. It would not matter what the strength of selection was if information-increasing mutations did not occur. In other words, elitism speeds up convergence once a good solution has been discovered - it does not bring about an outcome which would not otherwise have occurred. Therefore, if genetic algorithms with elitism can produce new information, then so can evolution in the wild.
Furthermore, not all GAs use elitist selection. Many do not, instead relying only on roulette-wheel selection and other stochastic sampling techniques, and yet these have been no less successful. For instance, Koza et al. 2003, p.8-9, gives examples of 36 instances where genetic programming has produced human-competitive results, including the automated recreation of 21 previously patented inventions (six of which were patented during or after 2000), 10 of which duplicate the functionality of the patent in a new way, and also including two patentable new inventions and five new algorithms that outperform any human-written algorithms for the same purpose. As Dr. Koza states in an earlier reference to the same work (1999, p.1070): "The elitist strategy is not used." Some other papers cited in this essay in which elitism is not used include: Robin et al. 2003; Rizki, Zmuda and Tamburino 2002; Chryssolouris and Subramaniam 2001; Burke and Newall 1999; Glen and Payne 1995; Au, Chan and Yao 2003; Jensen 2003; Kewley and Embrechts 2002; Williams, Crossley and Lang 2001; Mahfoud and Mani 1996. In each of these cases, without any mechanism to ensure that the best individuals were selected at each generation, without exempting these individuals from potentially detrimental random change, genetic algorithms still produce powerful, efficient, human-competitive results. This fact may be surprising to creationists such as Batten, but it is wholly expected by advocates of evolution.
- GAs ignore generation time constraints
This criticism is puzzling. Batten claims that a single generation in a GA can take microseconds, whereas a single generation in any living organism can take anywhere from minutes to years. This is true, but how it is supposed to bear on the validity of GAs as evidence for evolution is not explained. If a GA can generate new information, regardless of how long it takes, then surely evolution in the wild can do so as well; that GAs can indeed do so is all this essay intends to demonstrate. The only remaining issue would then be whether biological evolution has actually had enough time to cause significant change, and the answer to this question would be one for biologists, geologists and physicists, not computer programmers.
The answer these scientists have provided is fully in accord with evolutionary timescales, however. Numerous lines of independent evidence, including radiometric isochron dating, the cooling rates of white dwarfs, the nonexistence of isotopes with short halflives in nature, the recession rates of distant galaxies, and analysis of the cosmic microwave background radiation all converge on the same conclusion: an Earth and a universe many billions of years old, easily long enough for evolution to produce all the diversity of life we see today by all reasonable estimates.
- GAs employ unrealistically high rates of mutation and reproduction
Batten asserts, without providing any supporting evidence or citations, that GAs "commonly produce 100s or 1000s of 'offspring' per generation", a rate even bacteria, the fastest-reproducing biological organisms, cannot match.
This criticism misses the mark in several ways. First of all, if the metric being used is (as it should be) number of offspring per generation, rather than number of offspring per unit of absolute time, then there clearly are biological organisms that can reproduce at rates faster than that of bacteria and roughly equal to the rates Batten claims are unrealistic. For example, a single frog can lay thousands of eggs at a time, each of which has the potential to develop into an adult. Granted, most of these usually will not survive due to resource limitations and predation, but then most of the "offspring" in each generation of a GA will not go on either.
Secondly, and more importantly, a genetic algorithm working on solving a problem is not meant to represent a single organism. Instead, a genetic algorithm is more analogous to an entire population of organisms - after all, it is populations, not individuals, that evolve. Of course, it is eminently plausible for a whole population to collectively have hundreds or thousands of offspring per generation. (Creationist Walter ReMine makes this same mistake with regards to Dr. Richard Dawkins' "weasel" program. See this Post of the Month for more.)
Additionally, Batten says, the mutation rate is artificially high in GAs, whereas living organisms have error-checking machinery designed to limit the mutation rate to about 1 in 10 billion base pairs (though this is too small - the actual figure is closer to 1 in 1 billion. See Dawkins 1996, p.124). Now of course this is true. If GAs mutated at this rate, they would take far too long to solve real-world problems. Clearly, what should be considered relevant is the rate of mutation relative to the size of the genome. The mutation rate should be high enough to promote a sufficient amount of diversity in the population without overwhelming the individuals. An average human will possess between one and five mutations; this is not at all unrealistic for the offspring in a GA.
- GAs have artificially small genomes
Batten's argument that the genome of a genetic algorithm "is artificially small and only does one thing" is badly misguided. In the first place, as we have seen, it is not true that a GA only does one thing; there are many examples of genetic algorithms specifically designed to optimize many parameters simultaneously, often far more parameters simultaneously than a human designer ever could.
And how exactly does Batten quantify "artificially small"? Many evolutionary algorithms, such as John Koza's genetic programming, use variable-length encodings where the size of candidate solutions can grow arbitrarily large. Batten claims that even the simplest living organism has far more information in its genome than a GA has ever produced, but while organisms living today may have relatively large genomes, that is because much complexity has been gained over the course of billions of years of evolution. As the Probability of Abiogenesis article points out, there is good reason to believe that the earliest living organisms were very much simpler than any species currently extant - self-replicating molecules probably no longer than 30 or 40 subunits, which could easily be specified by the 1800 bits of information that Batten apparently concedes at least one GA has generated. Genetic algorithms are similarly a very new technique whose full potential has not yet been tapped; digital computers themselves are only a few decades old, and as Koza (2003, p. 25) points out, evolutionary computing techniques have been generating increasingly more substantial and complex results over the last 15 years, in synchrony with the ongoing rapid increase in computing power often referred to as "Moore's Law". Just as early life was very simple compared to what came after, today's genetic algorithms, despite the impressive results they have already produced, are likely to give rise to far greater things in the future.
- GAs ignore the possibility of mutation occurring throughout the genome
Batten apparently does not understand how genetic algorithms work, and he shows it by making this argument. He states that in real life, "mutations occur throughout the genome, not just in a gene or section that specifies a given trait". This is true, but when he says that the same is not true of GAs, he is wrong. Exactly like in living organisms, GAs permit mutation and recombination to occur anywhere in the genomes of their candidate solutions; exactly like in living organisms, GAs must weed out the deleterious changes while simultaneously selecting for the beneficial ones.
Batten goes on to claim that "the program itself is protected from mutations; only target sequences are mutated", and if the program itself were mutated it would soon crash. This criticism, however, is irrelevant. There is no reason why the governing program of a GA should be mutated. The program is not part of the genetic algorithm; the program is what supervises the genetic algorithm and mutates the candidate solutions, which are what the programmer is seeking to improve. The program running the GA is not analogous to the reproductive machinery of an organism, a comparison which Batten tries to make. Rather, it is analogous to the invariant natural laws that govern the environments in which living organisms live and reproduce, and these are neither expected to change nor need to be "protected" from it.
- GAs ignore problems of irreducible complexity
Using old-earth creationist Michael Behe's argument of "irreducible complexity", Batten argues, "Many biological traits require many different components to be present, functioning together, for the trait to exist at all," whereas this does not happen in GAs.
However, it is trivial to show that such a claim is false, as genetic algorithms have produced irreducibly complex systems. For example, the voice-recognition circuit Dr. Adrian Thompson evolved (Davidson 1997) is composed of 37 core logic gates. Five of them are not even connected to the rest of the circuit, yet all 37 are required for the circuit to work; if any of them are disconnected from their power supply, the entire system ceases to function. This fits Behe's definition of an irreducibly complex system and shows that an evolutionary process can produce such things.
It should be noted that this is the same argument as the first one, merely presented in different language, and thus the refutation is the same. Irreducible complexity is not a problem for evolution, whether that evolution is occurring in living beings in the wild or in silicon on a computer's processor chip.
- GAs ignore polygeny, pleiotropy, and other genetic complexity
Batten argues that GAs ignore issues of polygeny (the determination of one trait by multiple genes), pleiotropy (one gene affecting multiple traits), and dominant and recessive genes.
However, none of these claims are true. GAs do not ignore polygeny and pleiotropy: these properties are merely allowed to arise naturally rather than being deliberately coded in. It is obvious that in any complex interdependent system (i.e., a nonlinear system), the alteration or removal of one part will cause a ripple effect of changes throughout; thus GAs naturally incorporate polygeny and pleiotropy. "In the genetic algorithm literature, parameter interaction is called epistasis (a biological term for gene interaction). When there is little to no epistasis, minimum seeking algorithms [i.e., hill-climbers --A.M.] perform best. Genetic algorithms shine when the epistasis is medium to high..." (Haupt and Haupt 1998, p. 31, original emphasis).
Likewise, there are some genetic algorithm implementations that do have diploid chromosomes and dominant and recessive genes (Goldberg 1989, p.150; Mitchell 1996, p.22). However, those that do not are simply more like haploid organisms, such as bacteria, than they are like diploid organisms, such as human beings. Since (by certain measures) bacteria are among the most successful organisms on this planet, such GAs remain a good model of evolution.
- GAs do not have multiple reading frames
Batten discusses the existence of multiple reading frames in the genomes of some living things, in which the DNA sequences code for different functional proteins when read in different directions or with different starting offsets. He asserts that "Creating a GA to generate such information-dense coding would seem to be out of the question".
Such a challenge begs for an answer, and here it is: Soule and Ball 2001. In this paper, the authors present a genetic algorithm with multiple reading frames and dense coding, enabling it to store more information than the total length of its genome. Like the three-nucleotide codons that specify amino acids in the genomes of living organisms, this GA's codons were five-digit binary strings. Since the codons were five digits long, there were five different possible reading frames. The sequence 11111 serves as a "start" codon and 00000 as a "stop" codon; because the start and stop codons could occur anywhere in the genome, the length of each individual was variable. Regions of the chromosome which did not fall between start-stop pairs were ignored.
The GA was tested on four classic function maximization problems. "Initially, the majority of the bits do not participate in any gene, i.e., most of a chromosome is non-coding. Again this is because in the initial random individuals there are relatively few start-stop codon pairs. However, the number of bits that do not participate decreases extremely rapidly." During the course of the run, the GA can increase the effective length of its genome by introducing new start codons in different reading frames. By the end of the run, "the amount of overlap is quite high. Many bits are participating in several (and often all five) genes." On all test problems, the GA started, on average, with 5 variables specified; by the end of the run, that number had increased to an average of around 25.
In the test problems, the GA with multiple reading frames produced significantly better solutions than a standard GA on two out of the four problems and better average solutions on the remaining two. In one problem, the GA successfully compressed 625 total bits of information into a chromosome only 250 bits long by using alternative reading frames. The authors label this behavior "extremely sophisticated" and conclude that "These data show that a GA can successfully use reading frames despite the added complexity" and "It is clear that a GA can introduce new 'genes' as necessary to solve a given problem, even with the difficulties imposed by using start and stop codons and overlapping genes".
- GAs have preordained goals
Like several others, this objection shows that Batten does not fully understand what a genetic algorithm is and how it works. He argues that GAs, unlike evolution, have goals predetermined and specified at the outset, and as an example of this, offers Dr. Richard Dawkins' "weasel" program.
However, the weasel program is not a true genetic algorithm, and is not typical of genetic algorithms, for precisely that reason. It was not intended to demonstrate the problem-solving power of evolution. Instead, its only intent was to show the difference between single-step selection (the infamous "tornado blowing through a junkyard producing a 747") and cumulative, multi-step selection. It did have a specific goal predetermined at the outset. True genetic algorithms, however, do not.
In a broadly general sense, GAs do have a goal: namely, to find an acceptable solution to a given problem. In this same sense, evolution also has a goal: to produce organisms that are better adapted to their environment and thus experience greater reproductive success. But just as evolution is a process without specific goals, GAs do not specify at the outset how a given problem should be solved. The fitness function is merely set up to evaluate how well a candidate solution performs, without specifying any particular way it should work and without passing judgment on whatever way it does invent. The solution itself then emerges through a process of mutation and selection.
Batten's next statement shows clearly that he does not understand what a genetic algorithm is. He asserts that "Perhaps if the programmer could come up with a program that allowed anything to happen and then measured the survivability of the 'organisms', it might be getting closer to what evolution is supposed to do" - but that is exactly how genetic algorithms work. They randomly generate candidate solutions and randomly mutate them over many generations. No configuration is specified in advance; as Batten puts it, anything is allowed to happen. As John Koza (2003, p. 37) writes, uncannily echoing Batten's words: "An important feature... is that the selection [in genetic programming] is not greedy. Individuals that are known to be inferior will be selected to a certain degree. The best individual in the population is not guaranteed to be selected. Moreover, the worst individual in the population will not necessarily be excluded. Anything can happen and nothing is guaranteed." (An earlier section discussed this very point as one of a GA's strengths.) And yet, by applying a selective filter to these randomly mutating candidates, efficient, complex and powerful solutions to difficult problems arise, solutions that were not designed by any intelligence and that can often equal or outperform solutions that were designed by humans. Batten's blithe assertion that "Of course that is impossible" is squarely contradicted by reality.
- GAs do not actually generate new information
Batten's final criticism runs: "With a particular GA, we need to ask how much of the 'information' generated by the program is actually specified in the program, rather than being generated de novo." He charges that GAs often do nothing more than find the best way for certain modules to interact when both the modules themselves and the ways they can interact are specified ahead of time.
It is difficult to know what to make of this argument. Any imaginable problem - terms in a calculus equation, molecules in a cell, components of an engine, stocks on a financial market - can be expressed in terms of modules that interact in given ways. If all one has is unspecified modules that interact in unspecified ways, there is no problem to be solved. Does this mean that the solution to no problem requires the generation of new information?
In regards to Batten's criticism about information contained in the solution being prespecified in the problem, the best way to assuage his concerns is to point out the many examples in which GAs begin with randomly generated initial populations that are not in any way designed to help the GA solve the problem. Some such examples include: Graham-Rowe 2004; Davidson 1997; Assion et al. 1998; Giro, Cyrillo and Galvão 2002; Glen and Payne 1995; Chryssolouris and Subramaniam 2001; Williams, Crossley and Lang 2001; Robin et al. 2003; Andreou, Georgopoulos and Likothanassis 2002; Kewley and Embrechts 2002; Rizki, Zmuda and Tamburino 2002; and especially Koza et al. 1999 and Koza et al. 2003, which discuss the use of genetic programming to generate 36 human-competitive inventions in analog circuit design, molecular biology, algorithmics, industrial controller design, and other fields, all starting from populations of randomly generated initial candidates.
Granted, some GAs do begin with intelligently generated solutions which they then seek to improve, but this is irrelevant: in such cases the aim is not just to return the initially input solution, but to improve it by the production of new information. In any case, even if the initial situation is as Batten describes, finding the most efficient way a number of modules can interact under a given set of constraints can be a far from trivial task, and one whose solution involves a considerable amount of new information: scheduling at international airports, for example, or factory assembly lines, or distributing casks among warehouses and distilleries. Again, GAs have proven themselves effective at solving problems whose complexity would swamp any human. In light of the multiple innovations and unexpectedly effective solutions arising from GAs in many fields, Batten's claim that "The amount of new information generated (by a GA) is usually quite trivial" rings hollow indeed.
William Dembski
Old-earth creationist Dr. William Dembski's recent book, No Free Lunch: Why Specified Complexity Cannot Be Purchased Without Intelligence, is largely devoted to the topic of evolutionary algorithms and how they relate to biological evolution. In particular, Dembski's book is concerned with an elusive quality he calls "specified complexity," which he asserts is contained in abundance in living things, and which he further asserts evolutionary processes are incapable of generating, leaving "design" through unspecified mechanisms by an unidentified "intelligent designer" the only alternative. To bolster his case, Dembski appeals to a class of mathematical theorems known as the No Free Lunch theorems, which he claims prove that evolutionary algorithms, on the average, do no better than blind search.
Richard Wein has written an excellent and comprehensive rebuttal to Dembski, entitled Not a Free Lunch But a Box of Chocolates, and its points will not be reproduced here. I will instead focus on chapter 4 of Dembski's book, which deals in detail with genetic algorithms.
Dembski has one main argument against GAs, which is developed at length throughout this chapter. While he does not deny that they can produce impressive results - indeed, he says that there is something "oddly compelling and almost magical" (p.221) about the way GAs can find solutions that are unlike anything designed by human beings - he argues that their success is due to the specified complexity that is "smuggled into" them by their human designers and subsequently embodied in the solutions they produce. "In other words, all the specified complexity we get out of an evolutionary algorithm has first to be put into its construction and into the information that guides the algorithm. Evolutionary algorithms therefore do not generate or create specified complexity, but merely harness already existing specified complexity" (p.207).
The first problem evident in Dembski's argument is this. Although his chapter on evolutionary algorithms runs for approximately 50 pages, the first 30 of those pages discuss nothing but Dr. Richard Dawkins' "weasel" algorithm, which, as already discussed, is not a true genetic algorithm and is not representative of genetic algorithms. Dembski's other two examples - the crooked wire genetic antennas of Edward Altshuler and Derek Linden and the checkers-playing neural nets of Kumar Chellapilla and David Fogel - are only introduced within the last 10 pages of the chapter and are discussed for three pages, combined. This is a serious deficiency, considering that the "weasel" program is not representative of most work being done in the field of evolutionary computation; nevertheless, Dembski's arguments relating to it will be analyzed.
In regard to the weasel program, Dembski states that "Dawkins and fellow Darwinists use this example to illustrate the power of evolutionary algorithms" (p.182), and, again, "Darwinists... are quite taken with the METHINKS IT IS LIKE A WEASEL example and see it as illustrating the power of evolutionary algorithms to generate specified complexity" (p.183). This is a straw man of Dembski's creation (not least because Dawkins' book was written long before Dembski ever coined that term!). Here is what Dawkins really says about the purpose of his program:
"What matters is the difference between the time taken by cumulative selection, and the time which the same computer, working flat out at the same rate, would take to reach the target phrase if it were forced to use the other procedure of single-step selection: about a million million million million million years." (Dawkins 1996, p.49, emphasis original)
In other words, the weasel program was intended to demonstrate the difference between two different kinds of selection: single-step selection, where a complex result is produced by pure chance in a single leap, and cumulative selection, where a complex result is built up bit by bit via a filtering process that preferentially preserves improvements. It was never intended to be a simulation of evolution as a whole.
Single-step selection is the absurdly improbable process frequently attacked in creationist literature by comparing it to a tornado blowing through a junkyard producing a 747 airliner, or an explosion in a print shop producing a dictionary. Cumulative selection is what evolution actually uses. Using single-step selection to achieve a functional result of any significant complexity, one would have to wait, on average, many times the current age of the universe. Using cumulative selection, however, that same result can be reached in a comparatively very short length of time. Demonstrating this difference was the point of Dawkins' weasel program, and that was the only point of that program. In a footnote to this chapter, Dembski writes, "It is remarkable how Dawkins' example gets recycled without any indication of the fundamental difficulties that attend it" (p.230), but it is only misconceptions in the minds of creationists such as Dembski and Batten, who attack the weasel program for not demonstrating something it was never intended to demonstrate, that give rise to these "difficulties".
Unlike every example of evolutionary algorithms discussed in this essay, the weasel program does indeed have a single, prespecified outcome, and the quality of the solutions it generates is judged by explicitly comparing them to that prespecified outcome. Therefore, Dembski is quite correct when he says that the weasel program does not generate new information. However, he then makes a gigantic and completely unjustified leap when he extrapolates this conclusion to all evolutionary algorithms: "As the sole possibility that Dawkins' evolutionary algorithm can attain, the target sequence in fact has minimal complexity.... Evolutionary algorithms are therefore incapable of generating true complexity" (p.182). Even Dembski seems to recognize this when he writes: "...most evolutionary algorithms in the literature are programmed to search a space of possible solutions to a problem until they find an answer - not, as Dawkins does here, by explicitly programming the answer into them in advance" (p.182). But then, having given a perfectly good reason why the weasel program is not representative of GAs as a whole, he inexplicably goes on to make precisely that fallacious generalization!
In reality, the weasel program is significantly different from most genetic algorithms, and therefore Dembski's argument from analogy does not hold up. True evolutionary algorithms, such as the examples discussed in this essay, do not simply find their way back to solutions already discovered by other methods - instead, they are presented with problems where the optimal solution is not known in advance, and are asked to discover that solution on their own. Indeed, if genetic algorithms could do nothing more than rediscover solutions already programmed into them, what would be the point of using them? It would be an exercise in redundancy to do so. However, the widespread scientific (and commercial) interest in GAs shows that there is far more substance to them than the rather trivial example Dembski tries to reduce this entire field to.
Having set up and then knocked down this straw man, Dembski moves on to his next line of argument: that the specified complexity exhibited by the outcomes of more representative evolutionary algorithms has, like the weasel program, been "smuggled in" by the designers of the algorithm. "But invariably we find that when specified complexity seems to be generated for free, it has in fact been front-loaded, smuggled in, or hidden from view" (p.204). Dembski suggests that the most common "hiding place" of specified complexity is in the GA's fitness function. "What the [evolutionary algorithm] has done is take advantage of the specified complexity inherent in the fitness function and used it in searching for and then locating the target..." (p.194). Dembski goes on to argue that, before an EA can search a given fitness landscape for a solution, some mechanism must first be employed to select that fitness landscape from what he calls a phase space of all the possible fitness landscapes, and if that mechanism is likewise an evolutionary one, some other mechanism must first be employed to select its fitness function from an even larger phase space, and so on. Dembski concludes that the only way to stop this infinite regress is through intelligence, which he holds to have some irreducible, mysterious ability to select a fitness function from a given phase space without recourse to higher-order phase spaces. "There is only one known generator of specified complexity, and that is intelligence" (p.207).
Dembski is correct when he writes that the fitness function "guid[es] an evolutionary algorithm into the target" (p.192). However, he is incorrect in his claim that selecting the right fitness function is a process that requires the generation of even more specified complexity than the EA itself produces. As Koza (1999, p. 39) writes, the fitness function tells an evolutionary algorithm "what needs to be done", not "how to do it". Unlike the unrepresentative weasel program example, the fitness function of an EA typically does not specify any particular form that the solution should take, and therefore it cannot be said to contribute "specified complexity" to the evolved solution in any meaningful sense.
An example will illustrate the point in greater detail. Dembski claims that in Chellapilla and Fogel's checkers experiment, their choice to hold the winning criterion constant from game to game "inserted an enormous amount of specified complexity" (p.223). It is certainly true that the final product of this process displayed a great deal of specified complexity (however one chooses to define that term). But is it true that the chosen fitness measure contained just as much specified complexity? Here is what Chellapilla and Fogel actually say:
"To appreciate the level of play that has been achieved, it may be useful to consider the following thought experiment. Suppose you are asked to play a game on an eight-by-eight board of squares with alternating colors. There are 12 pieces on each side arranged in a specific manner to begin play. You are told the rules of how the pieces move (i.e., diagonally, forced jumps, kings) and that the piece differential is available as a feature. You are not, however, told whether or not this differential is favorable or unfavorable (there is a version of checkers termed 'suicide checkers,' where the object is to 'lose' as fast as possible) or if it is even valuable information. Most importantly, you are not told the object of the game. You simply make moves and at some point an external observer declares the game over. They do not, however, provide feedback on whether or not you won, lost, or drew. The only data you receive comes after a minimum of five such games and is offered in the form of an overall point score. Thus, you cannot know with certainty which games contributed to the overall result or to what degree. Your challenge is to induce the appropriate moves in each game based only on this coarse level of feedback." (Chellapilla and Fogel 2001, p.427)
It exceeds the bounds of the absurd for Dembski to claim that this fitness measure inserted an "enormous" amount of specified complexity. If a human being who had never heard of checkers was given the same information, and we returned several months later to discover that he had become an internationally ranked checkers expert, should we conclude that specified complexity has been generated?
Dembski states that to overturn his argument, "one must show that finding the information that guides an evolutionary algorithm to a target is substantially easier than finding the target directly through a blind search" (p.204). I contend that this is precisely the case. Intuitively, it should not be surprising that the fitness function contains less information than the evolved solution. This is precisely the reason why GAs have found such widespread use: it is easier (requires less information) to write a fitness function that measures how good a solution is, than to design a good solution from scratch.
In more informal terms, consider Dembski's two examples, the crooked-wire genetic antenna and the evolved checkers-playing neural network named Anaconda. It requires a great deal of detailed information about the game of checkers to come up with a winning strategy (consider Chinook and its enormous library of endgames). However, it does not require equally detailed information to recognize such a strategy when one sees it: all we need observe is that that strategy consistently defeats its opponents. Similarly, a person who knew nothing about how to design an antenna that radiates evenly over a hemispherical region in a given frequency range could still test such an antenna and verify that it works as intended. In both cases, determining what constitutes high fitness is far easier (requires less information) than figuring out how to achieve high fitness.
Granted, even though choosing a fitness function for a given problem requires less information than actually solving the problem defined by that fitness function, it does take some information to specify the fitness function in the first place, and it is a legitimate question to ask where this initial information comes from. Dembski may still ask about the origin of human intelligence that enables us to decide to solve one problem rather than another, or about the origin of the natural laws of the cosmos that make it possible for life to exist and flourish and for evolution to occur. These are valid questions, and Dembski is entitled to wonder about them. However, by this point - seemingly unnoticed by Dembski himself - he has now moved away from his initial argument. He is no longer claiming that evolution cannot happen; instead, he is essentially asking why we live in a universe where evolution can happen. In other words, what Dembski does not seem to realize is that the logical conclusion of his argument is theistic evolution. It is fully compatible with a God who (as many Christians, including evolutionary biologist Kenneth Miller, believe) used evolution as his creative tool, and set up the universe in such a way as to make it not just likely, but certain.
I will conclude by clearing up some additional, minor misconceptions in chapter 4 of No Free Lunch. For starters, although Dembski, unlike Batten, is clearly aware of the field of multiobjective optimization, he erroneously states that "until some form of univalence is achieved, optimization cannot begin" (p.186). This essay's discussion of multiple-objective genetic algorithms shows the error of this. Perhaps other design techniques have this restriction, but one of the virtues of GAs is precisely that they can make trade-offs and optimize several mutually exclusive objectives simultaneously, and the human overseers can then pick whichever solution best achieves their goals from the final group of Pareto-optimal solutions. No method of combining multiple criteria into one is necessary.
Dembski also states that GAs "seem less adept at constructing integrated systems that require multiple parts to achieve novel functions" (p.237). The many examples detailed in this essay (particularly John Koza's use of genetic programming to engineer complex analog circuits) show this claim to be false as well.
Finally, Dembski mentions that INFORMS, the professional organization of the operations research community, pays very little attention to GAs, and this "is reason to be skeptical of the technique's general scope and power" (p.237). However, just because a particular scientific society is not making widespread use of GAs does not mean that such uses are not widespread elsewhere or in general, and this essay has endeavored to show that this is in fact the case. Evolutionary techniques have found a wide variety of uses in virtually any field of science one would care to name, as well as among many companies in the commercial sector. Here is a partial list:
- Lockheed Martin (Gibbs 1996)
- GlaxoSmithKline (Gillet 2002)
- LBS Capital Management (Naik 1996)
- First Quadrant (Begley and Beals 1995)
- Texas Instruments (Begley and Beals 1995)
- U.S. West (Begley and Beals 1995)
- John Deere & Co. (Petzinger 1995)
- Volvo (Rao 1998)
- Ascent Technology (Wired 2002)
- Boeing (Ashley 1992)
- British Petroleum (Lemley 2001)
- Ford Motor Company (Lemley 2001)
- Unilever (Lemley 2001)
- United Distillers and Vintners (Lemley 2001)
By contrast, given the dearth of scientific discoveries and research stimulated by intelligent design, Dembski is in a poor position to complain about lack of practical application. Intelligent design is a vacuous hypothesis, telling us nothing more than "Some designer did something, somehow, at some time, to cause this result." By contrast, this essay has hopefully demonstrated that evolution is a problem-solving strategy rich with practical applications.
Even creationists find it impossible to deny that the combination of mutation and natural selection can produce adaptation. Nevertheless, they still attempt to justify their rejection of evolution by dividing the evolutionary process into two categories - "microevolution" and "macroevolution" - and arguing that only the second is controversial, and that any evolutionary change we observe is only an example of the first.
Now, microevolution and macroevolution are terms that have meaning to biologists; they are defined, respectively, as evolution below the species level and evolution at or above the species level. But the crucial difference between the way creationists use these terms and the way scientists use them is that scientists recognize that these two are fundamentally the same process with the same mechanisms, merely operating at different scales. Creationists, however, are forced to postulate some type of unbridgeable gap separating the two, in order for them to deny that the processes of change and adaptation we see operating in the present can be extrapolated to produce all the diversity observed in the living world.
However, genetic algorithms make this view untenable by demonstrating the fundamental seamlessness of the evolutionary process. Take, for example, a problem that consists of programming a circuit to discriminate between a 1-kilohertz and a 10-kilohertz tone, and respond respectively with steady outputs of 0 and 5 volts. Say we have a candidate solution that can accurately discriminate between the two tones, but its outputs are not quite steady as required; they produce small waveforms rather than the requisite unchanging voltage. Presumably, according to the creationist view, to change this circuit from its present state to the perfect solution would be "microevolution", a small change within the ability of mutation and selection to produce. But surely, a creationist would argue, to arrive at this same final state from a completely random initial arrangement of components would be "macroevolution" and beyond the reach of an evolutionary process. However, genetic algorithms were able to accomplish both, evolving the system from a random arrangement to the near-perfect solution and finally to the perfect, optimal solution. At no step of the way did an insoluble difficulty or a gap that could not be bridged turn up. At no point whatsoever was human intervention required to assemble an irreducibly complex core of components (despite the fact that the finished product does contain such a thing) or to "guide" the evolving system over a difficult peak. The circuit evolved, without any intelligent guidance, from a completely random and non-functional state to a tightly complex, efficient and optimal state. How can this not be a compelling experimental demonstration of the power of evolution?
It has been said that human cultural evolution has superceded the biological kind - that we as a species have reached a point where we are able to consciously control our society, our environment and even our genes to a sufficient degree to make the evolutionary process irrelevant. It has been said that the cultural whims of our rapidly changing society, rather than the comparatively glacially slow pace of genetic mutation and natural selection, is what determines fitness today. In a sense, this may well be true.
But in another sense, nothing could be further from the truth. Evolution is a problem-solving process whose power we are only beginning to understand and exploit; despite this, it is already at work all around us, shaping our technology and improving our lives, and in the future, these uses will only multiply. Without a detailed understanding of the evolutionary process, none of the countless advances we owe to genetic algorithms would have been possible. There is a lesson here to those who deny the power of evolution, as well as those who deny that knowledge of it has any practical benefit. As incredible as it may seem, evolution works. As the poet Lord Byron put it: "'Tis strange but true; for truth is always strange, stranger than fiction."
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additionally, factor in the organizing effects of the following cyclic modulations of that energy input:
1. elliptical orbit, with aphelion and perihelion
2. diurnal cycle due to axial revolution
3. solar activity cycles
4. seasonal cycles due to axial tilt from perpendicualr to plane of ecliptic
of course, later on come other organizing natural phenomena (such as the tidal cycles caused by a masive moon whose orbit is not aligned with the solar ecliptic plane)
the 2nd Law of Thermodynamics simply does not fit the open system under discussion.
"If life just miraculously appeared from inanimate objects and chemicals by chance... then it should be really easy to duplicate that feat in a lab...."
Give me a hundred billion galaxies with a hundred billion solar systems each, and give the planets in each several billion years and I expect I'll be able to show you one where life occurs through natural processes.
It's just this sort of mindless screed that that brings to mind Wolfgang Pauli's famous quote:
"It's so bad it's not even wrong."
see my #40 (?)
order abounds in the open system.
all neo-creo/ID arguments are rooted in argumentum ad incredulum, which is in turn rooted in intransigent, obstinate, deliberate adamantine ignorance.
1st Samuel:For further information: The Flat Earth Society.
2:8 He raiseth up the poor out of the dust, and lifteth up the beggar from the dunghill, to set them among princes, and to make them inherit the throne of glory: for the pillars of the earth are the LORD's, and he hath set the world upon them.
2nd Samuel:
22:8 Then the earth shook and trembled; the foundations of heaven moved and shook, because he was wroth.
22:16 And the channels of the sea appeared, the foundations of the world were discovered, at the rebuking of the LORD, at the blast of the breath of his nostrils.
11:12 And he shall set up an ensign for the nations, and shall assemble the outcasts of Israel, and gather together the dispersed of Judah from the four corners of the earth.
Psalms, 74:17
Thou hast set all the borders of the earth: thou hast made summer and winter.
Psalms, 102:25
Of old hast thou laid the foundation of the earth: and the heavens are the work of thy hands.
Psalms, 104:5
Who laid the foundations of the earth, that it should not be re-moved for ever.
8:29 When he gave to the sea his decree, that the waters should not pass his commandment: when he appointed the foundations of the earth.
Job, 38:4
Where wast thou when I laid the foundations of the earth? declare, if thou hast understanding.
38:5 Who hath laid the measures thereof, if thou knowest? or who hath stretched the line upon it?
38:6 Whereupon are the foundations thereof fastened? or who laid the corner stone thereof;
Matthew 4:8
Again, the devil taketh him up into an exceeding high mountain, and sheweth him all the kingdoms of the world, and the glory of them;
Luke 4:5
And the devil, taking him up into an high mountain, shewed unto him all the kingdoms of the world in a moment of time.
Hasn't happened yet, but they are working on it....
Probability of Abiogenesis FAQs
And for a taste of just how *much* evolution can speed up things over purely random processes like the one you *incorrectly* try to use as an analogy for evolutionary processes, here's an older post of mine:
Or are you one of those who insist that a room full of monkeys with keyboards can write the complete works of Shakespeare?
In theory? Yes they can, if you're willing to wait long enough (where "enough" is an amount of time that boggles the imagination). In practice (by simple random output)? No they can't.
But they can do it pretty quickly and easily if a replication and selection process is involved.
You wanted to see a calculation, so let's do one.
Consider the Shakespeare phrase, "If you can look into the seeds of time, and say which grain will grow and which will not, speak then unto me." That's 109 characters (including spaces and punctuation). Upper and lower case letters, plus digits and puntuaction, make up a pool of about 70 different characters. This means that the odds of producing the Shakespeare phrase in one random trial is 1 out of 70109, or 1 in 1,305,227,939,201,292,014, 528,313,176,276,968,928,001, 249,110,077,400,839,115,038, 451,821,150,802,274,449,576, 205,527,736,070,000,000,000, 000,000,000,000,000,000,000, 000,000,000,000,000,000,000, 000,000,000,000,000,000,000, 000,000,000,000,000,000,000, 000,000,000,000,000.
Needless to say, that's a big number. It's so huge that if every atom in the universe (about 1080 of them) were a computer capable of making a billion (1,000,000,000) random trials per second, the expected time required to produce the above line from Shakespeare would be 2,585,011,097,170,911,314,802,759,827,024,569,612,393, 783,728,161,759,843,736,212,615,624,189,581,658,716,078, 309,043,891,309 times the expected lifespan of the universe. That's close enough to "never" in my book.
But that's for *purely* random production process. How much do you think an evolutionary process could cut down that figure? Knock a few zeros off the end, maybe?
Well let's try it. Using an evolutionary process, which couples random variation with replication and selection and *nothing* else, the above Shakespeare phrase can be produced on a *single* computer (mine), using a breeding population of 1024 character strings in a whopping... 15 seconds (using this applet):Generation: 0Hmm, 15 seconds is a hell of a lot faster than zillions of times the lifespan of the universe, isn't it? Evolution sped things up (compared to a purely random process) by a factor of more than 10195 -- that's a "1" followed by a hundred and ninety-five zeros, or: 1, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000.
Tries <= 1024
Best Critter: "xSeOSEpc3Lm6rnRWnpFYL?QEDY7a67XlfRoJ0e8Len'X'1u'BhdrNqSNaXr7kVjondNozkf2CH9d96SaI?'f43M.CUGJ5XHbqfeR.UJP'tgNP"
Score (0 is best) 101
Generation: 100
Tries <= 26624
Best Critter: "vf,ioV c3RKlooioifBFQXh, PeHTskof!oJ0e,Lrn'X'1u BhkchESNaXr kVjo dNozpanSI div1Qwi8h taQ,jswMkk,us1S'ugYtmm7."
Score (0 is best) 72
Generation: 1115
Tries <= 286464
Best Critter: "If you can look into the seeds of time, and say which grain will grow and which will not, speak then unto me."
Score (0 is best) 0
Checked 286464 critters in 15 seconds == 19097 tries/sec.
Lesson: Even simple evolutionary processes are *incredibly* more efficient and effective than simple randomness alone. Evolution can *easily* accomplish things which would be *impossibly* improbable by purely random means.
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