Simple Markov Models of the Genetic Algorithm in Classifier Systems: Accuracy-Based Fitness

Abstract

Michigan-style Classifier Systems use Genetic Algorithms to facilitate rule-discovery, where rule fitness has traditionally been prediction-based. Current research has shifted to the use of accuracy-based fitness. This paper presents a simple Markov model of the algorithm in such systems, allowing comparison between the two forms of rule utility measure. Using a single-step task the previously discussed benefits of accuracy over prediction are clearly shown with regard to overgeneral rules. The effects of a niche-based algorithm (maximal generality) are also briefly examined, as are the effects of mutation under the two fitness schemes.