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

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journal contribution
posted on 28.10.2020, 03:21 by Mengjie Zhang
© 2013 IEEE. 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.

History

Preferred citation

Zhang, M. (2016). Surrogate-assisted Genetic Programming with Simplified Models for Automated Design of Dispatching Rules. IEEE Transactions on Cybernetics, 47(9), 2951-2965. https://doi.org/10.1109/TCYB.2016.2562674

Journal title

IEEE Transactions on Cybernetics

Volume

47

Issue

9

Publication date

01/01/2016

Pagination

2951-2965

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Publication status

Published

Contribution type

Article

ISSN

2168-2267

eISSN

2168-2275

Language

en

Exports