期刊論文
| 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 | en |
| ISSN | 0925-5273 |
| 期刊性質 | 國外 |
| 收錄於 | SCI |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 是 |
| 國別 | NLD |
| 公開徵稿 | |
| 出版型式 | ,電子版,紙本 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/121395 ) |