A study on fuzzy rules discovery using Pseudo-Bacterial Genetic Algorithm with adaptive operator

Abstract

This paper presents a new operator called adaptive operator for the 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 discovery of fuzzy rules. The aim of the newly introduced adaptive operator is to improve the quality of the generated fuzzy rules, producing blocks of effective rules and more compact rule bases. The new operator adaptively decides the division points of each chromosome for the bacterial mutation and the cutting points for the crossover. In order to verify the efficiency of the proposed adaptive operator, the PBGA is applied to a simple fuzzy modeling problem. The new operator actuates according to the distribution of degrees of truth values of the rules. The results show the benefits that can be obtained with this operator.