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How XCS Evolves Accurate Classifiers

Martin V. Butz, Tim Kovacs, Pier Luca Lanzi, Stewart W. Wilson, How XCS Evolves Accurate Classifiers. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001). Lee Spector et al. , (eds.), pp. 927–934. July 2001. PDF, 284 Kbytes.


Due to the accuracy based fitness approach, the ultimate goal for XCS is the evolution of a compact, complete, and accurate payoff mapping of an environment. This paper investigates what causes the XCS classifier system to evolve accurate classifiers. The investigation leads to two challenges for XCS, the covering challenge and the schema challenge. Both challenges are revealed theoretically and experimentally. Furthermore, the paper provides suggestions for overcoming the challenges as well as investigates environmental properties that can help XCS to overcome the challenges autonomously. Along those lines, a deeper insight into how to set the initial parameter values in XCS is provided.

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