LEARNING AND COMPLEXITY IN GENETIC AUTO-ADAPTIVE SYSTEMS

We describe and investigate the learning capabilities 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 the environment it encounters, at a rate characterized by external parameters such as mutation rate and population size. Our results suggest that most effective learning is achieved close to the transition to disorder, and that learning curves of evolutionary systems are fractal.