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Genetic Programming Fuzzy Rule Extractor Using Class Preserving Representation

This paper describes a genetic programming approach to the construction of fuzzy classification system with if-then fuzzy rules. Recently many research studies were focusing on utilisation of evolutionary techniques for automatically extracting fuzzy rules from data. In this paper we present a method based on genetic programming with a special structure preserving representation and special rule base adjusting operators working on it. First results show that the new features added to standard GP extractor considerably improve both performance and comprehensibility of found fuzzy rules.