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Entropy 2011, 13(6), 1152-1169; doi:10.3390/e13061152

EA/G-GA for Single Machine Scheduling Problems with Earliness/Tardiness Costs

1
Department of Electronic Commerce Management, Nanhua University, Dalin Chiayi 62248, Taiwan
2
Institute of Manufacturing Engineering, National Cheng Kung University, Tainan 70101, Taiwan
3
Department of Information Management, Yuan-Ze University, 135 Yuan-Tung Road, Chungli, Taoyuan 32023, Taiwan
*
Author to whom correspondence should be addressed.
Received: 10 May 2011 / Revised: 30 May 2011 / Accepted: 10 June 2011 / Published: 14 June 2011
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Abstract

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.
Keywords: Estimation of Distribution Algorithms; probability estimation; statistical learning problem; EA/G; diversity; single machine scheduling problems Estimation of Distribution Algorithms; probability estimation; statistical learning problem; EA/G; diversity; single machine scheduling problems
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Chen, S.-H.; Chen, M.-C.; Chang, P.-C.; Chen, Y.-M. EA/G-GA for Single Machine Scheduling Problems with Earliness/Tardiness Costs. Entropy 2011, 13, 1152-1169.

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