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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)
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)
Learning Classifier Systems (LCSs) have gained increasing interest in the genetic and evolutionary computation literature. Many real-world problems are not conveniently expressed using the ternary representation typically used by LCSs and for such problems an interval-based representation is preferable. A new model of LCSs is introduced to classify(More)