Fuzzy Classifier Design using Modified Genetic Algorithm

Development of fuzzy ifthen rules and formation of membership functions are the important consideration in designing a fuzzy classifier system. This paper presents a Modified Genetic Algorithm (ModGA) approach to obtain the optimal rule set and the membership function for a fuzzy classifier. In the genetic population, the membership functions are represented using real numbers and the rule set is represented by the binary string. A modified form of cross over and mutation operators are proposed to deal with the mixed string. The proposed genetic operators help to improve the convergence speed and quality of the solution. The performance of the proposed approach is demonstrated through development of fuzzy classifier for Iris, Wine and Tcpdump data. From the simulation study it is found that the proposed Modified Genetic Algorithm produces a fuzzy classifier which has minimum number of rules and whose classification accuracy is better than the results reported in the literature.