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

作     者：Kallel, I Alimi, AM 

作者机构：Univ Sfax Res Grp Intelligent Machines Sfax Tunisia 

出 版 物：《SOFT COMPUTING》 (Soft Comput.)

年 卷 期：2006年第10卷第9期

页      面：757-772页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：beta fuzzy systems learning genetic algorithms multi-agent systems distributed genetic algorithms 

摘      要：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.