Selection strategy for XCS with adaptive action mapping

Authors:
Masaya Nakata;Pier Luca Lanzi;Keiki Takadama
Affiliations:
The University of Electro-Communications, Chofu, Japan;Politecnico di Milano, Milano, Italy;The University of Electro-Communications, Chofu, Japan
Venue:
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Year:
2013

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Abstract

XCS with Adaptive Action Mapping (XCSAM) evolves so- lutions focused on classifiers that advocate the best action in every state. Accordingly, XCSAM usually evolves more compact solutions than XCS which, in contrast, works to- ward solutions representing complete state-action mappings. Experimental results have however shown that, in some prob- lems, XCSAM may produce bigger populations than XCS. In this paper, we extend XCSAM with a novel selection strat- egy to reduce, even further, the size of the solutions XCSAM produces. The proposed strategy selects the parent classi- fiers based both on their fitness values (like XCS) and on the effect they have on the adaptive map. We present experi- mental results showing that XCSAM with the new selection strategy can evolve more compact solutions than XCS which, at the same time, are also maximally general and maximally accurate.