A Framework for Evolving Fuzzy Classifier Systems Using Genetic Programming

Brian Carse and Anthony G. Pipe, University of the West of England, United Kingdom

A fuzzy classifier system framework is proposed which employs a tree-based representation for fuzzy rule (classifier) antecedents and genetic programming for fuzzy rule discovery. Such a rule representation is employed because of the expressive power and generality it endows to individual rules. The framework proposes accuracy-based fitness for individual fuzzy classifiers and employs evolutionary competition between simultaneously matched classifiers. The evolutionary algorithm (GP) is therefore searching for compact fuzzy rule bases which are simultaneously general, accurate and co-adapted. Additional extensions to the proposed framework are suggested.


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