Fuzzy logic controllers generated by pseudo-bacterial genetic algorithm with adaptive operator

This paper presents a new genetic operator called adaptive operator to improve local portions of chromesomes. This new operator is implemented in a pseudo-bacterial genetic algorithm (PBGA). The PBGA was proposed by the authors as a new approach combining a genetic algorithm (GA) with a local improvement mechanism inspired by a process in bacterial genetics. The PBGA was applied for the acquisition of fuzzy rules. The aim of the newly introduced adaptive operator is to improve the quality of the generated rules of the fuzzy models, producing blocks of effective rules and more compact models. The new operator adaptively decides the division points of each chromosome for the bacterial mutation and the cutting points for the crossover, according to the distribution of degrees of truth values of the rules. In this paper, first, results obtained when using the PBGA with the adaptive operator for a simple fuzzy modeling problem are presented. Second, the PBGA with adaptive operator is used in the design of a fuzzy logic controller for a semi-active suspension system. The results show the benefits obtained with this operator