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Accuracy exponentiation in UCS and its effect on voting margins
Proceedings of the 13th annual conference on Genetic and evolutionary computation - GECCO '11
UCS is a a Learning Classifier System (LCS) which evolves condition-action rules for supervised classification tasks. In UCS the fitness of a rule is based on its accuracy raised to a power ν, and this fitness is used in both the search for good rules (via a genetic algorithm) and in a classification vote. We trace the origin of the UCS fitness function through three successive versions of the XCS accuracy function, for which we present previously unpublished details and rationales. Throughdoi:10.1145/2001576.2001745 dblp:conf/gecco/KovacsEB11 fatcat:x3sa4jh4xfalxgd3vofav3wosq