Evolving dispatching rules for optimising many-objective criteria in the unrelated machines environment

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@Article{Durasevic:GPEM,
  author =       "Marko Durasevic and Domagoj Jakobovic",
  title =        "Evolving dispatching rules for optimising
                 many-objective criteria in the unrelated machines
                 environment",
  journal =      "Genetic Programming and Evolvable Machines",
  note =         "Online first",
  keywords =     "genetic algorithms, genetic programming, Dispatching
                 rules, Many-objective optimisation, Scheduling,
                 Unrelated machines environment",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-017-9310-3",
  abstract =     "Dispatching rules are often a method of choice for
                 solving various scheduling problems. Most often, they
                 are designed by human experts in order to optimise a
                 certain criterion. However, it is seldom the case that
                 a schedule should optimise a single criterion all
                 alone. More common is the case where several criteria
                 need to be optimised at the same time. This paper deals
                 with the problem of automatic design of dispatching
                 rules (DRs) by the use of genetic programming, for
                 many-objective scheduling problems. Four
                 multi-objective and many-objective algorithms,
                 including nondominated sorting genetic algorithm II,
                 nondominated sorting genetic algorithm III, harmonic
                 distance based multi-objective evolutionary algorithm
                 and multi-objective evolutionary algorithm based on
                 decomposition, have been used in order to obtain sets
                 of Pareto optimal solutions for various many-objective
                 scheduling problems. Through experiments it was shown
                 that automatically generated multi-objective DRs not
                 only achieve good performance when compared to standard
                 DRs, but can also outperform automatically generated
                 single objective DRs for most criteria combinations.",
}

Genetic Programming entries for Marko Durasevic Domagoj Jakobovic

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