A comparison of genetic programming and artificial neural networks in metamodeling of discrete-event simulation models

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Can2012424,
  author =       "Birkan Can and Cathal Heavey",
  title =        "A comparison of genetic programming and artificial
                 neural networks in metamodeling of discrete-event
                 simulation models",
  journal =      "Computer \& Operations Research",
  volume =       "39",
  number =       "2",
  pages =        "424--436",
  year =         "2012",
  month =        feb,
  ISSN =         "0305-0548",
  DOI =          "doi:10.1016/j.cor.2011.05.004",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0305054811001286",
  keywords =     "genetic algorithms, genetic programming, Simulation
                 metamodel, Symbolic regression, ANN, Neural networks,
                 Design of experiments, Decision support tool",
  ISSN =         "0305-0548",
  size =         "13 pages",
  abstract =     "Genetic programming (GP) and artificial neural
                 networks (ANNs) can be used in the development of
                 surrogate models of complex systems. The purpose of
                 this paper is to provide a comparative analysis of GP
                 and ANNs for metamodelling of discrete-event simulation
                 (DES) models. Three stochastic industrial systems are
                 empirically studied: an automated material handling
                 system (AMHS) in semiconductor manufacturing, an (s,S)
                 inventory model and a serial production line. The
                 results of the study show that GP provides greater
                 accuracy in validation tests, demonstrating a better
                 generalisation capability than ANN. However, GP when
                 compared to ANN requires more computation in metamodel
                 development. Even given this increased computational
                 requirement, the results presented indicate that GP is
                 very competitive in metamodelling of DES models.",
  notes =        "p432 'The results show that across all three systems
                 GP provided greater extrapolation capability'",
  bibdate =      "2011-06-20",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/cor/cor39.html#CanH12",
}

Genetic Programming entries for Birkan Can Cathal Heavey

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