Surrogate-assisted Genetic Programming with Simplified Models for Automated Design of Dispatching Rules
© 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.
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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.2016.2562674Publisher DOI
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IEEE Transactions on CyberneticsVolume
47Issue
9Publication date
2016-01-01Pagination
2951-2965Publisher
Institute of Electrical and Electronics Engineers (IEEE)Publication status
PublishedContribution type
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2168-2267eISSN
2168-2275Language
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