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ISCC
2006
IEEE

Dejong Function Optimization by Means of a Parallel Approach to Fuzzified Genetic Algorithm

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Dejong Function Optimization by Means of a Parallel Approach to Fuzzified Genetic Algorithm
Genetic Algorithms are very powerful search methods that are used in different optimization problems. Parallel versions of genetic algorithms are easily implemented and usually increase algorithm performance [4]. Fuzzy control as another optimization solution along with genetic algorithms can significantly increase algorithm performance. Two variations for genetic algorithm and fuzzy system composition exist. In the first approach Genetic algorithms are used to optimize and model the structure of fuzzy systems through knowledge base or membership function design while the second approach exploits fuzzy to dynamically supervise genetic algorithm performance by speedily reaching an optimal solution. In this paper we propose a new method for fuzzy parallel genetic algorithms, in which a parallel client-server single population fuzzy genetic algorithm is configured to optimize the performance of the first three Dejong functions in order to reach a global solution in the least possible ite...
Ebrahim Bagheri, Hossein Deldari
Added 12 Jun 2010
Updated 12 Jun 2010
Type Conference
Year 2006
Where ISCC
Authors Ebrahim Bagheri, Hossein Deldari
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