淡江大學機構典藏:Item 987654321/121538
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    題名: A Self-guided Genetic Algorithm for Flowshop Scheduling problems
    作者: Chen, Shih-Shin;Chang, Pei-Chann;Zhang, Qingfu
    關鍵詞: Genetic algorithms;Genetic mutations;Evolutionary computation;Sampling methods;Electronic mail;Scheduling algorithm;Minimization methods;Predictive models;Biological cells;Character generation
    日期: 2009-05-29
    上傳時間: 2021-10-21 12:11:48 (UTC+8)
    摘要: This paper proposed self-guided genetic algorithm, which is one of the algorithms in the category of evolutionary algorithm based on probabilistic models (EAPM), to solve strong NP-hard flowshop scheduling problems with the minimization of makespan. Most EAPM research explicitly used the probabilistic model from the parental distribution, then generated solutions by sampling from the probabilistic model without using genetic 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 combinatorial problems, particularly the sequencing ones. As a result, the probabilistic model serves as a fitness surrogate which estimates the fitness of the new solution beforehand in this research. So the probabilistic model is used to guide the evolutionary process of crossover and mutation. This research studied the flowshop scheduling problems and the corresponding experiment were conducted. From the results, it shows that the self-guided GA outperformed other algorithms significantly. In addition, self-guided GA works more efficiently than previous EAPM. As a result, self-guided GA is promising in solving the flowshop scheduling problems.
    DOI: 10.1109/CEC.2009.4982983
    顯示於類別:[資訊工程學系暨研究所] 會議論文

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