Michigan and Pittsburgh methods combination for fuzzy classifier design with coevolutionary algorithm

A new method of Michigan and Pittsburgh approaches combination for fuzzy classifier design with evolutionary algorithms is presented. Fuzzy classifier design includes of four stages. The first stage is standard fuzzification. The second one is a special procedure of initial rules forming with a priori information from a learning sample. At the third stage Michigan method is applied and it provides fast search of fuzzy rules with the best grade of certainty values for different classes and smoothing of randomness at initial population forming. At the fourth stage Pittsburgh method provides rules subset search with the best performance and predefined number of the rules and doesn't require a lot of computational power. Besides, a self-tuning cooperative-competitive coevolutionary algorithm for strategy adaptation is applied at Michigan and Pittsburgh stages of the fuzzy classifier design. This algorithm automatically solves the problem of genetic algorithm parameters setting. Thereby the method allows getting a compact fuzzy rule set with appropriate classification performance and with high computation speed. Classification results for machine learning problems from TICI repository and comparison with different alternative classifiers are presented.