Step size adaptation in evolution strategies using reinforcement learning

Abstract

We discuss the implementation of a learning algorithm for determining adaptation parameters in evolution strategies. As an initial test case, we consider the application of reinforcement learning for determining the relationship between success rates and the adaptation of step sizes in the (1+1)-evolution strategy. The results from the new adaptive scheme when applied to several test functions are compared with those obtained from the (1+1)-evolution strategy with a priori selected parameters. Our results indicate that assigning good reward measures seems to be crucial to the performance of the combined strategy.