Self-adaptation in classifier system controllers

Abstract

The use and benefits of self-adaptive mutation operators are well known within evolutionary computing. In this paper, we begin by examining the use of self-adaptive mutation in learning classifier systems with the aim of improving their performance as controllers for autonomous mobile robots. We implement the operator in a zeroth level classifier system, and examine its performance in two animat environments. It is shown that although no significant increase in performance is seen over results presented in the literature using a fixed rate of mutation, the operator adapts to an appropriate rateregadless of the initial range. The same concept is then applied to the learning rate parameter, but results show that a modification must be made to produce stable/effective controllers. Finally, results from a fully self-adaptive system are presented, with marked benefits being found in a nonstationary environment.