Abstract
The authors propose a genetic algorithm method for choosing an appropriate set of fuzzy if-then rules for classification problems. The aim of the proposed method is to find a minimum set of fuzzy if-then rules that can correctly classify all training patterns. This is achieved by formulating and solving a combinatorial optimization problem that has two objectives, which are to maximize the number of correctly classified patterns and to minimize the number of fuzzy if-then rules. A genetic algorithm was applied to this problem and simulation results are shown. An individual (i.e., a solution) in the genetic algorithm is the set of fuzzy if-then rules, and its fitness is determined by the two objectives in the combinatorial optimization problem. >
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