Comparison of ensemble learning methods for creating ensembles of dispatching rules for the unrelated machines environment

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

@Article{Durasevic:2017:GPEM,
  author =       "Marko Durasevic and Domagoj Jakobovic",
  title =        "Comparison of ensemble learning methods for creating
                 ensembles of dispatching rules for the unrelated
                 machines environment",
  journal =      "Genetic Programming and Evolvable Machines",
  note =         "Online first",
  keywords =     "genetic algorithms, genetic programming, Dispatching
                 rules, Scheduling, Unrelated machines environment,
                 Ensemble learning",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-017-9302-3",
  size =         "40 pages",
  abstract =     "Dispatching rules are often the method of choice for
                 solving various scheduling problems, especially since
                 they are applicable in dynamic scheduling environments.
                 Unfortunately, dispatching rules are hard to design and
                 are also unable to deliver results which are of equal
                 quality as results achieved by different metaheuristic
                 methods. As a consequence, genetic programming is
                 commonly used in order to automatically design
                 dispatching rules. Furthermore, a great amount of
                 research with different genetic programming methods is
                 done to increase the performance of the generated
                 dispatching rules. In order to additionally improve the
                 effectiveness of the evolved dispatching rules, in this
                 paper the use of several different ensemble learning
                 algorithms is proposed to create ensembles of
                 dispatching rules for the dynamic scheduling problem in
                 the unrelated machines environment. Four different
                 ensemble learning approaches will be considered, which
                 will be used in order to create ensembles of
                 dispatching rules: simple ensemble combination
                 (proposed in this paper), BagGP, BoostGP and
                 cooperative coevolution. Additionally, the
                 effectiveness of these algorithms is analysed based on
                 some ensemble learning parameters. Finally, an
                 additional search method, which finds the optimal
                 combinations of dispatching rules to form the
                 ensembles, is proposed and applied. The obtained
                 results show that by using the aforementioned ensemble
                 learning approaches it is possible to significantly
                 increase the performance of the generated dispatching
                 rules.",
}

Genetic Programming entries for Marko Durasevic Domagoj Jakobovic

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