Adjusting membership functions of fuzzy classification rules by genetic algorithms
Tadahiko Murata, Hisao Ishibuchi · 2002
In this paper, we propose a genetic algorithm based method for adjusting the membership functions of antecedent fuzzy sets in fuzzy rules for classification problems. The proposed method determines the fuzzy partition of a pattern space for a classification problem. This means that the number of fuzzy rules and the membership function of each antecedent fuzzy set are simultaneously determined. First we describe how a fuzzy partition of a pattern space is denoted by a string that can be handled in genetic algorithms. In this coding, each axis of a pattern space is partitioned by triangular fuzzy sets and trapezoid fuzzy sets. This coding can also employ the whole domain of each attribute as an antecedent fuzzy set. Next, we show genetic operators for adjusting the membership function of each antecedent fuzzy set. Finally, we demonstrate that our genetic algorithm can construct a classification system with high classification power.>