Guidelines for developing effective Estimation of Distribution Algorithms in solving single machine scheduling problems

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Shih-Hsin Chen
  • Min-Chih Chen
  • Pei-Chann Chang
  • Qingfu Zhang
  • Yuh-Min Chen

Detail(s)

Original languageEnglish
Pages (from-to)6441-6451
Journal / PublicationExpert Systems with Applications
Volume37
Issue number9
Publication statusPublished - Sep 2010
Externally publishedYes

Abstract

The goal of this research is to deduce important guidelines for designing effective Estimation of Distribution Algorithms (EDAs). These guidelines will enhance the designed algorithms in balancing the intensification and diversification effects of EDAs. Most EDAs have the advantage of incorporating probabilistic models which can generate chromosomes with the non-disruption of salient genes. This advantage, however, may cause the problem of the premature convergence of EDAs resulted in the probabilistic models no longer generating diversified solutions. In addition, due to overfitting of the search space, probabilistic models cannot really represent the general information of the population. Therefore, this research will deduce important guidelines through the convergency speed analysis of EDAs under different computational times for designing effective EDA algorithms. The major idea is to increase the population diversity gradually by hybridizing EDAs with other meta-heuristics and replacing the procedures of sampling new solutions. According to that, this research further proposes an Adaptive EA/G to improve the performance of EA/G. The proposed algorithm solves the single machine scheduling problems with earliness/tardiness cost in a just-in-time scheduling environment. The experimental results indicated that the Adaptive EA/G outperforms ACGA and EA/G statistically significant in different stopping criteria. This paper, hence, is of importance in the field of EDAs as well as for the researchers in studying the scheduling problems. © 2010 Elsevier Ltd. All rights reserved.

Research Area(s)

  • Diversification, Estimation of distribution algorithms, Intensification, Just-in-time, Single machine scheduling problems

Citation Format(s)

Guidelines for developing effective Estimation of Distribution Algorithms in solving single machine scheduling problems. / Chen, Shih-Hsin; Chen, Min-Chih; Chang, Pei-Chann; Zhang, Qingfu; Chen, Yuh-Min.

In: Expert Systems with Applications, Vol. 37, No. 9, 09.2010, p. 6441-6451.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review