Design of Optimal Fuzzy Classifier Using Enhanced Genetic Algorithm 105 2 . 1 . Fuzzy Sets
نویسنده
چکیده
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 Genetic Algorithm (GA) approach to obtain the optimal rule set and the membership function. To develop the fuzzy system the membership functions and rule set are encoded into the chromosome and evolved simultaneously using Genetic Algorithm. Advanced genetic operators are applied to improve the performance of the GA in designing the fuzzy classifier. 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 Enhanced 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.
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