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Self-adaptation is a key feature of evolutionary algorithms (EAs). Although EAs have been used successfully to solve a wide variety of problems, the performance of this technique depends heavily on the selection of the EA parameters. Moreover, the process of setting such parameters is considered a time-consuming task. Several research works have tried to(More)
Learning Classifier System which replaces the genetic algorithm with the evolving cooperative population of discoverers is a focus of current research. This paper presents a modified version of XCS classifier system with self-adaptive discovery module. The new model was confirmed experimentally in a multiplexer environment. The results prove that XCS with(More)
The grammar-based classifier system (GCS) is a new version of learning classifier systems (LCS) in which classifiers are represented by context-free grammar in Chomsky normal form. GCS evolves one grammar during induction (the Michigan approach) which gives it the ability to find the proper set of rules very quickly. However it is quite sensitive to any(More)