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We propose a hybrid algorithm of fuzzy versions of two genetics-based machine learning approaches: Michigan and Pittsburgh approaches. First, we examine the performance of each approach by computer simulations on commonly used data sets. Simulation results clearly demonstrate that each approach has its own advantages and disadvantages. While the Michigan approach has high search ability to efficiently find good fuzzy rules in large search spaces for high-dimensional pattern classification problems, it can not directly optimize fuzzy rule-based systems. On the other hand, the Pittsburgh approach can directly optimize fuzzy rule-based systems while its search ability to find good fuzzy rules is not high. Then we combine these two approaches into a single hybrid algorithm. Our hybrid algorithm is based on the Pittsburgh approach where a set of fuzzy rules is coded as a string. The Michigan approach is …