A hybrid evolutionary approach for optimal fuzzy classifier design

Abstract

One of the important issues in the design of fuzzy classifier is the formation of fuzzy if-then rules and the membership functions. This paper presents a Niched Pareto Genetic Algorithm (NPGA) approach to obtain the optimal rule-set and the membership function. To develop the fuzzy system the rule set and the membership functions are encoded into the chromosome and evolved simultaneously using NPGA. The performance of the proposed approach is demonstrated through development of fuzzy classifier for Iris data available in the UCI machine learning repository. From the simulation study, it is found that that NPGA produces a fuzzy classifier which has minimum number of rules and high classification accuracy compared with the existing methods.