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Accuracy-Based Learning Classifier Systems: Models, Analysis and Applications to Classification Tasks
2003
Evolutionary Computation
Recently, Learning Classifier Systems (LCS) and particularly XCS have arisen as promising methods for classification tasks and data mining. This paper investigates two models of accuracy-based learning classifier systems on different types of classification problems. Departing from XCS, we analyze the evolution of a complete action map as a knowledge representation. We propose an alternative, UCS, which evolves a best action map more efficiently. We also investigate how the fitness pressure
doi:10.1162/106365603322365289
pmid:14558911
fatcat:25wu53rxhbe27mzk5li6nmvs3a