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

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 mode...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2006-07, Vol.10 (9), p.757-772
Main Authors: Kallel, Ilhem, Alimi, Adel M.
Format: Article
Language:English
Subjects:
Fuzzy systems
Genetic algorithms
Multiagent systems
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Summary: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.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-005-0012-z