Abstract
Fuzzy rules optimization is a crucial step in the development of a fuzzy model. A simple two inputs fuzzy model will have more than ten thousand possible combinations of fuzzy rules. A fuzzy designer normally uses intuition and trial and error method for the rules assignment. This paper is devoted to the development and implementation of genetic optimization library (GOL) to obtain the optimum set of fuzzy rules. In this context, a fitness calculation to handle maximization and minimization problem is employed. A new fitness-scaling mechanism named as Fitness Mapping is also developed. The developed GOL is applied to a case study involving fuzzy expert system for machinability data selection (Wong SV, Hamouda AMS, Baradie M. Int J Flexi Automat Integr Manuf 1997;5(1/2):79–104). The main characteristics of genetic optimization in fuzzy rule design are presented and discussed. The effect of 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.
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