Addressing the advantages of using ensemble probabilistic models in Estimation of Distribution Algorithms for scheduling problems

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题名:	Addressing the advantages of using ensemble probabilistic models in Estimation of Distribution Algorithms for scheduling problems
作者:	陳萌智;Chen, Min-Chih
貢獻者:	資訊管理學系
关键词:	Estimation of Distribution Algorithms;Single machine scheduling problem;Permutation flowshop scheduling problem;Self-Guided Genetic Algorithm
日期:	2013-01
上传时间:	2023-09-01 10:40:33 (UTC+8)
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.
關聯:	International Journal of Production Economics vol. 141, no. 1 pp.24-33
显示于类别:	[資訊管理學系] 期刊論文

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