SELECTING FUZZY IF-THEN RULES FOR CLASSIFICATION PROBLEMS USING GENETIC ALGORITHMS

This paper proposes a genetic-algorithm-based method for selecting a s mall number of significant fuzzy if-then rules to construct a compact fuzzy classification system with high classification power. The rule s election problem is Formulated as a combinatorial optimization problem with two objectives: to maximize the number of correctly classified p atterns and to minimize the number of fuzzy if-then rules. Genetic alg orithms are applied to this problem. A set of fuzzy if-then rules is c oded into a string and treated as an individual in genetic algorithms. The fitness of each individual is specified by the two objectives in the combinatorial optimization problem. The performance of the propose d method for training data and test data is examined by computer simul ations on the iris data of Fisher.