Abstract
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.
Highlights
Many machine learning algorithms provide input-output mapping without providing any easy to comprehend explainable logic
Kaur: Evolving Mamdani Fuzzy Rules Using Swarm Algorithms for Accurate Data Classification the authors used TSK based fuzzy systems to tune the parameters of Particle Swarm Optimization (PSO) to achieve better convergence of the algorithm
In this paper, a grammar template based on Mamdani Fuzzy rules was designed for a variety of datasets
Summary
Many machine learning algorithms provide input-output mapping without providing any easy to comprehend explainable logic. This paper evolves fuzzy rules for several datasets that provide intuitive knowledge, which in turn makes sense of the data in terms of fuzzy if- rules This explains the logic of the system. Kaur: Evolving Mamdani Fuzzy Rules Using Swarm Algorithms for Accurate Data Classification the authors used TSK based fuzzy systems to tune the parameters of Particle Swarm Optimization (PSO) to achieve better convergence of the algorithm. Fuzzy rules based on the Mamdani model are evolved using the GWO algorithm which is based on the hunting behavior of the pack of grey wolves as they work with fine coordination to surround and attack the prey.
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