A study on the discovery of relevant fuzzy rules using pseudobacterial genetic algorithm

This paper presents a new method for the discovery of relevant fuzzy rules using the pseudobacterial genetic algorithm (PBGA). The PBGA was proposed by the authors as a new approach combining a genetic algorithm with a local improvement mechanism inspired by a process in bacterial genetics, named bacterial operation. The presented system aims at the improvement of the quality of the generated fuzzy rules, producing blocks of effective rules and more compact rule bases. This is achieved by encoding the fuzzy rules in the chromosomes in a suitable form in order to make the bacterial operation more effective and by using a crossover operation that adaptively decides the cutting points according to the distribution of degrees of truth values of the rules. In this paper, first, results obtained when using the PBGA for a simple fuzzy modeling problem are presented and compared with other methods. Second, the PBGA is used in the design of a fuzzy logic controller for a semi-active suspension system. The results show the benefits obtained with this approach in both of the studied cases.