Modifying XCS for Size-Constrained Systems

Extended Classifier Systems, or XCS, is a soft-computing approach to machine learning in rule-based systems. While XCS has been shown effective in learning accurate, compact and complete mappings of an environment’s payoff landscape, it can require significant resources to do so. This paper presents four modifications that allow XCS to achieve high performance even in highly size-constrained populations. By modifying the genetic algorithm trigger function, the classifier deletion-selection mechanism, the frequency of classifier parameter updates and the genetic algorithm selection function – the modified system more efficiently uses the available population resources. Experimental results demonstrate the improvement in performance achieved with the proposed modifications in both the single-step 6-Multiplexer problem and the multi-step Woods-2 problem.