Michigan and Pittsburgh Fuzzy Classifier Systems for Learning Mobile Robot Control Rules: An Experimental Comparison

Abstract

We extend our previous work on the artificial evolution of Fuzzy Classifier Systems as reactive controllers for mobile robots, to encompass more versatile genotypic representations and more powerful genetic operators. The results are an improvement on our earlier work; in general, better controllers are evolved in fewer generations. However, the more global evolutionary characteristics of the Pittsburgh approach still bias the overall results heavily in its favour. A major weakness in both approaches is the lack of robustness in retaining crucial, but seldom-active rules in the evolutionary population.