Design of intelligent fuzzy logic controllers using genetic algorithms

The paper presents a methodology for combining genetic algorithms and fuzzy algorithms for learning the optimal rules for a FAM. With the aid of genetic algorithms, optimal rules of fuzzy logic controllers can be designed without human operators' experience and/or control engineers' knowledge. The approach presented here maintains the shape of membership functions and searches the optimal control rules based on a fitness value which is defined in terms of a performance criterion. Applications of the method to a fuzzy logic controller using genetic algorithm (FLC-GA) and a model reference adaptive fuzzy-GA controller (MRAFC-GA) are presented to illustrate the effectiveness of the design procedure.<>