EA/G-GA for Single Machine Scheduling Problems with Earliness/Tardiness Costs
學年 99
學期 2
出版(發表)日期 2011-06-14
作品名稱 EA/G-GA for Single Machine Scheduling Problems with Earliness/Tardiness Costs
著者 Chen, S. H.; M. C. Chen; P. C. Chang; Y. M. Chen
著錄名稱、卷期、頁數 Entropy 13(6), p.1152-1169
摘要 An Estimation of Distribution Algorithm (EDA), which depends on explicitly sampling mechanisms based on probabilistic models with information extracted from the parental solutions to generate new solutions, has constituted one of the major research areas in the field of evolutionary computation. The fact that no genetic operators are used in EDAs is a major characteristic differentiating EDAs from other genetic algorithms (GAs). This advantage, however, could lead to premature convergence of EDAs as the probabilistic models are no longer generating diversified solutions. In our previous research [1], we have presented the evidences that EDAs suffer from the drawback of premature convergency, thus several important guidelines are provided for the design of effective EDAs. In this paper, we validated one guideline for incorporating other meta-heuristics into the EDAs. An algorithm named “EA/G-GA” is proposed by selecting a well-known EDA, EA/G, to work with GAs. The proposed algorithm was tested on the NP-Hard single machine scheduling problems with the total weighted earliness/tardiness cost in a just-in-time environment. The experimental results indicated that the EA/G-GA outperforms the compared algorithms statistically significantly across different stopping criteria and demonstrated the robustness of the proposed algorithm. Consequently, this paper is of interest and importance in the field of EDAs.
關鍵字 Estimation of Distribution Algorithms;probability estimation;statistical learning problem;EA/G;diversity;single machine scheduling problems
語言 en
ISSN 1099-4300
期刊性質 國外
收錄於 SCI
國別 CHE
出版型式 ,電子版,紙本

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/121408 )