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

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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.

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Authors and Affiliations

  1. Research Group on Intelligent Machines, University of Sfax, ENIS BP W-3038, Sfax, Tunisia

    Ilhem Kallel & Adel M. Alimi

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  1. Ilhem Kallel
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  2. Adel M. Alimi
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Correspondence to Ilhem Kallel.

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Kallel, I., Alimi, A.M. MAGAD-BFS: A learning method for Beta fuzzy systems based on a multi-agent genetic algorithm. Soft Comput 10, 757–772 (2006). https://doi.org/10.1007/s00500-005-0012-z

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