An Intelligent Operator for Genetic Fuzzy Rule Based System

Abstract

This paper proposes a modified form of operator based on Particle Swarm Optimization (PSO) for designing Genetic Fuzzy Rule Based System (GFRBS). The usual procedure of velocity updating in PSO is modified by calculating the velocity using chromosome’s individual best value and global best value based on an updating probability without considering the inertia weight, old velocity and constriction factors. This kind of calculation brings intelligent information sharing mechanism and memory capability to Genetic Algorithm (GA) and can be easily implemented along with other genetic operators. The performance of the proposed operator is evaluated using ten publicly available bench mark data sets. Simulation results show that the proposed operator introduces new material into the population, thereby allows faster and more accurate convergence without struck into a local optima. Statistical analysis of the experimental results shows that the proposed operator produces a classifier model with minimum number of rules and higher classification accuracy.