Selecting Linguistic Classification Rules by Two-Objective Genetic Algorithms
نویسندگان
چکیده
In this paper, we show how two-objective genetic algorithms can be applied to a rule selection problem of linguistic classification rules. First we briefly describe a generation method of linguistic classification rules from numerical data. Next we formulate a rule selection problem of linguistic classification rules. This problem has two objectives: to maximize the number of correctly classified training patterns and to minimize the number of selected rules. Then we propose a two-objective genetic algorithm for finding non-dominated solutions of the rule selection problem. Last we extend our two-objective genetic algorithm to a hybrid algorithm where a learning method is applied to each individual (i.e., each rule set) generated in the execution of the two-objective genetic algorithm.
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