Acquisition of General Adaptive Features by Evolution
Submitted to EP98

Daniel Ashlock and John Mayfield

Abstract PDF eprint

We investigate the following question. Do populations of evolving agents adapt only to their recent environment or do general adaptive features appear over time? We find statistically significant appearance of general adaptive features in a spatially distributed population of prisoner's dilemma playing agents in a noisy environment. Multiple populations are evolved in an evolutionary algorithm structured as a cellular automaton with states drawn from a rich set of prisoner's dilemma strategies. Populations are sampled early and at the end of a ten-thousand generation simulation. Modern and archaic populations are then placed in competition. We test the hypothesis that competition between an archaic and modern population yields probability $p=0.5$ of modern populations out-competing archaic ones. The hypothesis is rejected at a confidence level of 99.5\% using a binomial probability model in each of seven variations of our basic experiment.