Constraining the optimization of a fuzzy logic controller using an enhanced genetic algorithm

Fuzzy logic controllers (FLC's) are gaining in popularity across a broad ar ray of disciplines because they allow a more human approach to control. Rec ently, the design of the fuzzy sets and the rule base has been automated by the use of genetic algorithms (GA's) which are powerful search techniques, Though the use of GA's can produce near optimal FLC's, it raises problems such as messy overlapping of fuzzy sets and rules not in agreement with com mon sense. This paper describes an enhanced genetic algorithm which constra ins the optimization of FLC's to produce well-formed fuzzy sets and rules w hich can be better understood by human beings. To achieve the above, we dev ised several new genetic operators and used a parallel GA with three popula tions for optimizing FLC's with 3 x 3, 5 x 5, and 7 x 7 rule bases, and we also used a novel method for creating migrants between the three population s of the parallel GA to increase the chances of optimization, In this paper , we also present the results of applying our GA to designing FLC's for con trolling three different plants and compare the performance of these FLC's with their unconstrained counterparts.