Revisit of Rule-Deletion Strategy for XCSAM Classifier System on Classification

Abstract

The XCSAM classifier system is an approach of evolutionary rule-based machine learning, which evolves rules advocating the highest-return actions at state, resulting in best classification. This paper starts with claiming a limitation that XCSAM still fails to evolutionary generate adequate rules advocating the highest-return actions. Then, under our hypothesis that this limitation is caused from the rule-deletion mechanism of XCSAM, we revisit its rule-deletion strategy in order to promote the evolution of adequate rules. Different from the existing deletion strategy which deletes two rules for each rule-evolution, our deletion strategy is modified to delete more than two rules as necessary. Experimental results on a benchmark classification task validate our modification powerfully works to evolve adequate rules, improving the performance of XCSAM. We further show our modification robustly enables XCSAM to perform well on real world classification task.