Optimization of fuzzy rules design using genetic algorithm

Fuzzy rules optimization is a crucial step in the development of a fuzzy mo del. A simple two inputs fuzzy model will have more than ten thousand possi ble combinations of fuzzy rules. A fuzzy designer normally uses intuition a nd trial and error method for the rules assignment. This paper is devoted t o the development and implementation of genetic optimization library (GOL) to obtain the optimum set of fuzzy rules. In this context, a fitness calcul ation to handle maximization and minimization problem is employed. A new fi tness-scaling mechanism named as Fitness Mapping is also developed. The dev eloped GOL is applied to a case study involving fuzzy expert system for mac hinability data selection (Wong SV, Hamouda AMS, Baradie M. Int J Flexi Aut omat Integr Manuf 1997;5(1/2):79-104). The main characteristics of genetic optimization in fuzzy rule design are presented and discussed. The effect o f constraint (rules violation) application is also presented and discussed. Finally, the developed GOL replaces the tedious process of trial and error for better combination of fuzzy rules. (C) 2000 Elsevier Science Ltd. All rights reserved.