淡江大學機構典藏:Item 987654321/121408
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    題名: EA/G-GA for Single Machine Scheduling Problems with Earliness/Tardiness Costs
    作者: Chen, S. H.;Chen, M. C.;Chang, P. C.;Chen, Y. M.
    關鍵詞: Estimation of Distribution Algorithms;probability estimation;statistical learning problem;EA/G;diversity;single machine scheduling problems
    日期: 2011-06-14
    上傳時間: 2021-09-30 12:10:30 (UTC+8)
    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.
    關聯: Entropy 13(6), p.1152-1169
    DOI: 10.3390/e13061152
    顯示於類別:[資訊工程學系暨研究所] 期刊論文

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