自指引遺傳演算法

陳世興

doi:10.6838/YZU.2008.00241

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自指引遺傳演算法

The Self-Guided Genetic Algorithm

陳世興 , Ph.D　　Advisor：張百棧　　

英文 DOI： 10.6838/YZU.2008.00241

具機率模式演化式演算法；排程； Evolutionary Algorithm with Probabilistic Models ； Scheduling

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This thesis proposed a Self-Guided genetic algorithm which is one of the algorithms in the category of evolutionary algorithm based on probabilistic models (EAPM). Previous EAPM research explicitly used the probabilistic model from the parental distribution, and then generated solutions by sampling from the probabilistic model without using genetic operators whereas GA employs crossover and mutation operators. Although EAPM is promising in solving different kinds of problems, Self-Guided GA doesn't intend to generate solution by the probabilistic model directly because the time-complexity is high when we solve difficult combinatorial problems, particularly the sequencing ones. In this research, the probabilistic model serves as a fitness surrogate which estimates the fitness of the new solution beforehand. So the probabilistic model is used to guide the evolutionary process of crossover and mutation. When it comes to local search, the fitness surrogate also can be applied in local search and it prunes some bad moves in advance. The hybridization of local search and probabilistic model is named Self-Guided Genetic Local Search. This research studied the single-objective scheduling problems, including the single machine and flowshop scheduling problems. From the experiment results, it shows that the Self-Guided GA outperform other algorithms significantly in terms of solution quality and computational time. Self-Guided Genetic Local Search evens works more efficiently than Genetic Algorithms. As a result, it could be a significant contribution in the branch of EAPM and is of interest for researchers in the field of evolutionary computation.

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