LEARNING AND COMPLEXITY IN GENETIC AUTO-ADAPTIVE SYSTEMS

We describe and investigate the learning capabilities displayed by a p opulation of self-replicating segments of computer code subject to ran dom mutation: the tierra environment. We find that learning is achieve d through phase transitions that adapt the population to the environme nt it encounters, at a rate characterized by external parameters such as mutation rate and population size. Our results suggest that most ef fective learning is achieved close to the transition to disorder, and that learning curves of evolutionary systems are fractal.