Bacterial evolutionary algorithm for fuzzy system design

Presents a method for discovering the parameters of a fuzzy system, namely the combination of input variables of the rules, the parameters of the membership functions of the variables and a set of relevant rules, from numerical data using the newly proposed bacterial evolutionary algorithm (BEA). In early work, the authors proposed the pseudo-bacterial genetic algorithm (PBGA) that incorporates a modified mutation operator called bacterial mutation, based on a natural phenomenon of microbial evolution. The BEA has the same features of the PBGA, but introduces a new operator, called gene transfer operation, equally inspired by a microbial evolution phenomenon. While the bacterial mutation performs local optimization within the limits of a single chromosome, the gene transfer operation allows the chromosomes to directly transfer information to the other counterparts in the population. The gene transfer is inspired by the natural phenomenon of transfer of strands of genes between bacteria in a population. By means of this mechanism, one bacterium can rapidly spread its genetic information to other cells. Numerical experiments were performed to show the effectiveness of the BEA. The obtained results show the benefits that can be obtained with the newly proposed method