Evolutionary Programming and Neural Computation             (Computer and Information Science)
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The goal of evolutionary programming is to develop systems              which learn how to perform critical computing tasks through a                   variation and selection algorithm.  The appproach taken in this                 research is based on the recognition that some structural                       organizations are well-suited to learning through evolution,                    while others are not.  A system that achieves evolvability by                   simulating the structure-fashion relations that facilitate                      evolution in biological systems has been developed.  Currently                  the system consists of simulated networks of dynamic neurons                    whose input-output behavior is controlled by membrane proteins.                 The variation and selection learning algorithm acts on the                      distribution of proteins.  The organization is well-suited to                   evolution because improvement can always be achieved through                    single, reasonably likely application of the variation operator.                The continuous dynamics displayed by the neurons also enhances                  the generalization capability of the system. The system is called               a molecular design since it was inspired by biophysical and                     biochemical properties of neurons.  However, the new efforts will               be concerned with virtual implementations that would be useful in               and of themselves and which could provide some insight into                     natural information processing.  Specific objectives are:  to                   incorporate richer nonlinear and cell automation dynamics, to add               a memory manipulation capability, to implement the system as a                  preprocessor for high-level vision and process control tasks, and               to create systems capable of long-term evolutionary improvement                 by applying the variation and selection operators to the                        dynamics.
[{'FirstName': 'Michael', 'LastName': 'Conrad', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Michael Conrad', 'EmailAddress': 'conrad@cs.wayne.edu', 'NSF_ID': '000123711', 'StartDate': '06/15/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}, {'FirstName': 'Roberto', 'LastName': 'Kampfner', 'PI_MID_INIT': 'R', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Roberto R Kampfner', 'EmailAddress': 'rrk@umich.edu', 'NSF_ID': '000318894', 'StartDate': '06/15/1987', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}]
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[{'Code': '6840', 'Text': 'ROBOTICS'}, {'Code': '9120', 'Text': 'CROSS-DIRECTORATE PROGRAMS'}]
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