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


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Article Info

DATE

2006-07

AUTHORS

Ilhem Kallel, Adel M. Alimi

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. More... »

PAGES

757

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00500-005-0012-z

DOI

http://dx.doi.org/10.1007/s00500-005-0012-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1023025494


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