Selection of fuzzy if‐then rules by a genetic method
Hisao Ishibuchi, Ken Nozaki, Naohisa Yamamoto, Hideo Tanaka · Electronics and Communications in Japan (Part III Fundamental Electronic Science) · 1994
Abstract This paper proposes a genetic algorithm‐based method for constructing a fuzzy classification system with fuzzy if‐then rules. In the proposed method, a rule selection problem for constructing a compact fuzzy system with high classification power is formulated as a combinatorial optimization problem with two objectives: to maximize the classification rate and to minimize the number of rules. Then a method of implementing genetic algorithms is proposed for the application to this problem and its effectiveness is demonstrated by computer simulations. The implementation of genetic algorithms in this paper employs an approach where a set of fuzzy if‐then rules is coded as a single individual.