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Acquisition of General Adaptive Features by Evolution

Submitted to EP98

### Daniel Ashlock and John Mayfield

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.