An integrated framework of hybrid evolutionary computations
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Evolutionary search-based test generation for software product line feature models
CAiSE'12 Proceedings of the 24th international conference on Advanced Information Systems Engineering
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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 iterations. Simulations show much improvement in genetic algorithm performance evaluation.