教師資料查詢 | 類別: 會議論文 | 教師: 陳世興CHEN, SHIH-HSIN (瀏覽個人網頁)

標題:A Self-guided Genetic Algorithm for Flowshop Scheduling problems
學年97
學期2
發表日期2009/05/18
作品名稱A Self-guided Genetic Algorithm for Flowshop Scheduling problems
作品名稱(其他語言)
著者Shih-Shin Chen; Pei-Chann Chang; Qingfu Zhang
作品所屬單位
出版者
會議名稱Proceeding of Congress of Evolutionary Computation 2009 (CEC 2009)
會議地點Trondheim, Norway
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.
關鍵字Genetic algorithms;Genetic mutations;Evolutionary computation;Sampling methods;Electronic mail;Scheduling algorithm;Minimization methods;Predictive models;Biological cells;Character generation
語言英文
收錄於
會議性質國際
校內研討會地點
研討會時間20090518~20090521
通訊作者
國別挪威
公開徵稿
出版型式
出處
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