¹Department of Computer Science Taiyuan Normal University, Taiyuan, China
²Dalarna University, Borlange, Sweden
³Department of Computing University of Bradford, Bradford, UK
⁴Department of Computer and Information Science Norwegian University of Science and Technology Trondheim, Norway
⁵Department of Computer Science Ubiquitous Healthcare Research Center Inje University, Gimhae, Busan, Korea

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Abstract—Genetic Algorithms are traditionally used to solve combinatorial optimization problems. The implementation of Genetic Algorithms involves of using genetic operators (crossover, mutation, selection, etc.). Meanwhile, parameters (such as population size, probabilities of crossover and mutation) of Genetic Algorithm need to be chosen or tuned. In this paper, we propose a hybrid Fuzzy-Genetic Algorithm (FLGA) approach to solve the multiprocessor scheduling problem. Based on traditional Genetic Algorithms, a fuzzy logic controller is added to tune parameters dynamically which potentially can improve the overall performance. In detail, the probabilities of crossover and mutation is tuned by a fuzzy logic controller based on fuzzy rules. Compared to the Standard Genetic Algorithm (SGA), the results of experiments clearly show that the FLGA method performs significantly better.

Index Terms—Genetic Algorithms, FLGA, SGA, Multiprocessor Scheduling, Fuzzy Logic Controller

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Cite: Huijuan Guo, Yi Feng, Fei Hao, Shengtong Zhong, Shuai Li, "Dynamic Fuzzy Logic Control of Genetic Algorithm Probabilities," Journal of Computers vol. 9, no. 1, pp. 22-27, 2014.