1995 Volume 7 Issue 5 Pages 1041-1049
In this paper, we propose a genetic-algorithm-based approach to the selection of linguistic rules for classification problems. A small number of significant linguistic rules are selected from a large number of rules which are generated in a pattern space. Our rule selection problem is to find a compact rule set that has high classification power. Therefore our problem has two objectives : to maximize the number of correctly classified patterns and to minimize the number of selected rules. For this two-objective problem, we propose a two-objective genetic algorithm (GA) to find a set of Pareto optimal solutions. Pareto optimal solutions are showed to the decision maker, then the decision maker can choose one of the Pareto optimal solutions. In this paper, first a selection procecdure and an eliltist strategy for finding a set of Pareto optimal solutions are proposed. Next we combined a learning method of linguistic rules with the two-objective GA. The learning method is applied to rule sets generated in the execution of the two-objective GA. Finally our two-objective GA is illustrated by computer simulations on the well-known iris data.
