Discovering Fuzzy Classification Rules with Genetic Programming and Co-Evolution
Roberto R. F. Mendes, Fabricio de B. Voznika, Alex A. Freitas, Júlio César Nievola · 2001
Abstract. In essence, data mining consists of extracting knowledge from data. This paper proposes a co-evolutionary system for discovering fuzzy classification rules. The system uses two evolutionary algorithms: a genetic programming (GP) algorithm evolving a population of fuzzy rule sets and a simple evolutionary algorithm evolving a population of membership function definitions. The two populations co-evolve, so that the final result of the co-evolutionary process is a fuzzy rule set and a set of membership function definitions which are well adapted to each other. In addition, our system also has some innovative ideas with respect to the encoding of GP individuals rep-resenting rule sets. The basic idea is that our individual encoding scheme in-corporates several syntactical restrictions that facilitate the handling of rule sets in disjunctive normal form. We have also adapted GP operators to better work with the proposed individual encoding scheme. 1