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

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@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 =         "2016",
  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.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/TCYB.2016.2562674",
  ISSN =         "2168-2267",
  notes =        "Also known as \cite{7473913}",
}

Genetic Programming entries for Su Nguyen Mengjie Zhang Kay Chen Tan

Citations