Fuzzy system parameters discovery by bacterial evolutionary algorithm

Abstract

This paper presents a new 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 pseudobacterial genetic algorithm (PBGA) that incorporates a modified mutation operator called bacterial mutation, based on a biological phenomenon of microbial evolution. The BEA has the same features of the PBGA, but introduces a new operation, 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 phenomenon of transfer of strands of genes in a population of bacteria. 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 this method.

Development of Production Control in Small Batch Production

“…Nature inspired evolutionary optimization algorithms are often suitable for global optimization of even nonlinear, high-dimensional, multi-modal, and discontinuous problems. Bacterial Evolutionary Algorithm (BEA) [9] is one of these techniques. BEA uses two operators, the bacterial mutation and the gene transfer operation.…”

Population and Gradient Based Optimization Techniques, a Theoretical Overview

“…Another widely used evolutionary type searching method is the bacterial evolutionary algorithm [2,3], which is based on the microbial evolution. There are two main difference between the bacterial and genetic algorithm:…”

“…Population based algorithms, like genetic algorithm [1], bacterial evolutionary algorithm [2,3] and particle swarm optimization [4] are developed to find global quasi-optimum of any complex search field. They are all inspired by a natural phenomena.…”

A New Metaheuristic Optimization Algorithm, the Weighted Attraction Method

“…The members of this algorithm family are usually inspired by nature, e.g. the genetic algorithm [1,2] (GA), the bacterial evolutionary algorithm [3,4] (BEA), and the particle swarm optimization [5][6][7] (PSO). These techniques deal with many disadvantages as well, e.g.…”