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

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{Nguyen:2016:ieeeTCYB,

• author = "Su Nguyen and Mengjie Zhang and Kay Chen Tan",
• journal = "IEEE Transactions on Cybernetics",
• title = "Surrogate-Assisted Genetic Programming With Simplified Models for Automated Design of Dispatching Rules",
• year = "2017",
• volume = "47",
• number = "9",
• pages = "2951--2965",
• month = sep,
• keywords = "genetic algorithms, genetic programming, evolutionary design, hyper-heuristic, scheduling",
• ISSN = "2168-2267",
• DOI = "doi:10.1109/TCYB.2016.2562674",
• size = "15 pages",
• 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.",
• notes = "Also known as \cite{7473913}",

}

Genetic Programming entries for Su Nguyen Mengjie Zhang Kay Chen Tan

Citations