LAUSR

This paper proposes and develops a fuzzy evolutionary system based on the Grey Wolf Optimizer (GWO) algorithm to evolve Mamdani fuzzy rules that give a knowledge base for accurate classification of data set. GWO takes inspiration from nature and is modeled after the hunting behavior of the grey wolves as they move around within a pack taking cues from the leader alpha, beta, and delta wolves until they find the best position to encircle and attack the prey. The algorithm is mapped onto the data specific rule base structure of the fuzzy systems. A grammar template in the form of fuzzy rules is designed, and then the GWO algorithm is used to evolve the fuzzy rules which classify the datasets. The algorithm will generate meaningful rules that make sense of data in easy to comprehend fuzzy rules. The algorithm was extensively tested on 15 datasets. GWO was compared with the standard Particle Swarm Optimizer (PSO) algorithm in generating a similar type of rules and comparing the accuracy of these two sets of rules in data classification. It was noted that the GWO algorithm converges in a lesser number of iterations and in a shorter time as compared to PSO and achieves the best accuracy.