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標題:Addressing the Advantages of Using Ensemble Probabilistic Models in Estimation of Distribution Algorithms for Scheduling Problems
學年101
學期1
出版(發表)日期2013/01/01
作品名稱Addressing the Advantages of Using Ensemble Probabilistic Models in Estimation of Distribution Algorithms for Scheduling Problems
作品名稱(其他語言)
著者Chen, S. H.; M. C. Chen
單位
出版者
著錄名稱、卷期、頁數International Journal of Production Economics 141(1), p.24–33
摘要Estimation of Distribution Algorithms (EDAs) have recently been recognized as a prominent alternative to traditional evolutionary algorithms due to their increasing popularity. The core of EDAs is a probabilistic model which directly impacts performance of the algorithm. Previous EDAs have used a univariate, bi-variate, or multi-variable probabilistic model each time. However, application of only one probabilistic model may not represent the parental distribution well. This paper advocates the importance of using ensemble probabilistic models in EDAs. We combine the univariate probabilistic model with the bi-variate probabilistic model which learns different population characteristics. To explain how to employ the two probabilistic models, we proposed the Ensemble Self-Guided Genetic Algorithm (eSGGA). The extensive computation results on two NP-hard scheduling problems indicate the advantages of adopting two probabilistic models. Most important of all, eSGGA can avoid the computation effort overhead when compared with other EDAs employing two models. As a result, this paper might point out a next generation approach for EDAs.
關鍵字Estimation of Distribution Algorithms;Single machine scheduling problem;Permutation flowshop scheduling problem;Self-Guided Genetic Algorithm
語言英文
ISSN0925-5273
期刊性質國外
收錄於SCI;
產學合作
通訊作者
審稿制度
國別荷蘭
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