Evolving Ensembles of Dispatching Rules using Genetic Programming for Job Shop Scheduling

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

@InProceedings{Park:2015:EuroGP,
  author =       "John Park and Su Nguyen and Mengjie Zhang and 
                 Mark Johnston",
  title =        "Evolving Ensembles of Dispatching Rules using Genetic
                 Programming for Job Shop Scheduling",
  booktitle =    "18th European Conference on Genetic Programming",
  year =         "2015",
  editor =       "Penousal Machado and Malcolm I. Heywood and 
                 James McDermott and Mauro Castelli and 
                 Pablo Garcia-Sanchez and Paolo Burelli and Sebastian Risi and Kevin Sim",
  series =       "LNCS",
  volume =       "9025",
  publisher =    "Springer",
  pages =        "92--104",
  address =      "Copenhagen",
  month =        "8-10 " # apr,
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming, Job shop
                 scheduling, Hyper-heuristics, Ensemble learning,
                 Cooperative coevolution, Robustness, Dispatching rules,
                 Combinatorial optimisation, Evolutionary computation",
  isbn13 =       "978-3-319-16500-4",
  DOI =          "doi:10.1007/978-3-319-16501-1_8",
  abstract =     "Job shop scheduling (JSS) problems are important
                 optimisation problems that have been studied
                 extensively in the literature due to their
                 applicability and computational difficulty. This paper
                 considers static JSS problems with makespan
                 minimisation, which are NP-complete for more than two
                 machines. Because finding optimal solutions can be
                 difficult for large problem instances, many heuristic
                 approaches have been proposed in the literature.
                 However, designing effective heuristics for different
                 JSS problem domains is difficult. As a result,
                 hyper-heuristics (HHs) have been proposed as an
                 approach to automating the design of heuristics. The
                 evolved heuristics have mainly been priority based
                 dispatching rules (DRs). To improve the robustness of
                 evolved heuristics generated by HHs, this paper
                 proposes a new approach where an ensemble of rules are
                 evolved using Genetic Programming (GP) and cooperative
                 coevolution, denoted as Ensemble Genetic Programming
                 for Job Shop Scheduling (EGP-JSS). The results show
                 that EGP-JSS generally produces more robust rules than
                 the single rule GP.",
  notes =        "Part of \cite{Machado:2015:GP} EuroGP'2015 held in
                 conjunction with EvoCOP2015, EvoMusArt2015 and
                 EvoApplications2015",
}

Genetic Programming entries for John Park Su Nguyen Mengjie Zhang Mark Johnston

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