Surrogate-Assisted Genetic Programming with Simplified Models for Automated Design of Dispatching Rules

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

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Author(s)

Detail(s)

Original languageEnglish
Article number7473913
Pages (from-to)2951-2965
Journal / PublicationIEEE Transactions on Cybernetics
Volume47
Issue number9
Online published19 May 2016
Publication statusPublished - Sept 2017
Externally publishedYes

Abstract

Automated design of dispatching rules for production systems has been an interesting research topic over the last several years. Machine learning, especially genetic programming (GP), has been a powerful approach to dealing with this design problem. However, intensive computational requirements, accuracy and interpretability are still its limitations. This paper aims at developing a new surrogate assisted GP to help improving the quality of the evolved rules without significant computational costs. The experiments have verified the effectiveness and efficiency of the proposed algorithms as compared to those in the literature. Furthermore, new simplification and visualisation approaches have also been developed to improve the interpretability of the evolved rules. These approaches have shown great potentials and proved to be a critical part of the automated design system.

Research Area(s)

  • Evolutionary design, genetic programming (GP), hyper-heuristic, scheduling

Citation Format(s)

Surrogate-Assisted Genetic Programming with Simplified Models for Automated Design of Dispatching Rules. / Nguyen, Su; Zhang, Mengjie; Tan, Kay Chen.
In: IEEE Transactions on Cybernetics, Vol. 47, No. 9, 7473913, 09.2017, p. 2951-2965.

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