Mixed Genetic Algorithm Approach for Fuzzy Classifier Design

An important issue in the design of FRBS is the formation of fuzzy if-then rules and the membership functions. This paper presents a Mixed Genetic Algorithm (MGA) approach to obtain the optimal rule set and the membership function of the fuzzy classifier. While applying genetic algorithm for fuzzy classifier design, the membership functions are represented as real numbers and the fuzzy rules are represented as binary string. Modified forms of crossover and mutation operators are proposed to deal with the mixed string. The proposed genetic operators help to improve the convergence of GA and accuracy of the classifier. The performance of the proposed approach is evaluated through development of fuzzy classifier for seven standard data sets. From the simulation study it is found that the proposed algorithm produces a fuzzy classifier with minimum number of rules and high classification accuracy. Statistical analysis of the test results shows the superiority of the proposed algorithm over the existing methods.