Name
Title | ||
|---|---|---|
Guidelines for developing effective Estimation of Distribution Algorithms in solving single machine scheduling problems |
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.eswa.2010.02.073 | Expert Syst. Appl. |
Keywords | Field | DocType |
diversification,hybridizing edas,estimation of distribution algorithms,intensification,important guideline,adaptive ea,scheduling problem,different computational time,single machine scheduling problems,probabilistic model,effective eda algorithm,single machine scheduling problem,effective estimation,just-in-time,distribution algorithms,just-in-time scheduling environment,statistical significance,estimation of distribution algorithm,search space,premature convergence | Population,EDAS,Single-machine scheduling,Mathematical optimization,Estimation of distribution algorithm,Premature convergence,Scheduling (computing),Computer science,Artificial intelligence,Probabilistic logic,Overfitting,Machine learning | Journal |
Volume | Issue | ISSN |
37 | 9 | Expert Systems With Applications |
Citations | PageRank | References |
14 | 0.61 | 38 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shih-Hsin Chen | 1 | 370 | 20.26 |
| Min-Chih Chen | 2 | 38 | 2.04 |
| Pei-Chann Chang | 3 | 1752 | 109.32 |
| Qingfu Zhang | 4 | 7634 | 255.05 |
| Yuh-Min Chen | 5 | 379 | 32.12 |