MAGAD-BFS: A learning method for Beta fuzzy systems based on a multi-agent genetic algorithm

Authors:
Ilhem Kallel;M. Alimi
Affiliations:
Research Group on Intelligent Machines, University of Sfax, ENIS BP W-3038, Sfax, Tunisia;Research Group on Intelligent Machines, University of Sfax, ENIS BP W-3038, Sfax, Tunisia
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Year:
2006

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Abstract

This paper proposes a learning method for Beta fuzzy systems (BFS) based on a multiagent genetic algorithm. This method, called Multi-Agent Genetic Algorithm for the Design of BFS has two advantages. First, thanks to genetic algorithms (GA) efficiency, it allows to design a suitable and precise model for BFS. Second, it improves the GA convergence by reducing rule complexity thanks to the distributed implementation by multi-agent approach. Dynamic agents interact to provide an optimal solution in order to obtain the best BFS reaching the balance interpretability-precision. The performance of the method is tested on a simulated example.