Learning and Complexity in Genetic Auto-Adaptive Systems
  We describe and investigate the learning capablities displayed by a population of self-replicating segments of computer-code subject to random mutation: the tierra environment. We find that learning is achieved through phase transitions that adapt the population to whichever environment it encounters, with a learning rate characterized by the environmental variables. Our results suggest that most effective learning is achieved close to the edge of chaos. 
Chris Adami (Kellogg Radiation Laboratory, Caltech)