New encoding method of genetic algorithm toward parameters identification of fuzzy expert system

The membership functions of fuzzy expert system need a systematic, self-learning method instead of subjectively tuning method in order to increase the performance of the fuzzy model. The genetic-algorithm learning method is consequently employed. The rule-based encoding scheme would bring the redundant information for the genetic algorithm by repeatedly representing the similar membership function in an individual. The new encoding method, which is parameter-based encoding scheme, would diminish the redundant representation of fuzzy parameters. This method would separate the data structures of fuzzy rules and fuzzy parameters in the genetic-algorithm learning method. This method should efficiently use the memory resources of computers and increase the dimensions of the solved problem. Then, a numerical example and the learning results are demonstrated. Discussions about the effects of population size, reproduction method, crossover rate, mutation rate and fitness scaling are included. Finally, some conclusion remarks.