Tailoring hyper-heuristics to specific instances of a scheduling problem using affinity and competence functions

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@Article{Salhi2014,
  author =       "Abdellah Salhi and Jose Antonio {Vazquez Rodriguez}",
  title =        "Tailoring hyper-heuristics to specific instances of a
                 scheduling problem using affinity and competence
                 functions",
  journal =      "Memetic Computing",
  year =         "2014",
  volume =       "6",
  number =       "2",
  pages =        "77--84",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1865-9292",
  DOI =          "doi:10.1007/s12293-013-0121-7",
  abstract =     "Hyper-heuristics are high level heuristics which
                 coordinate lower level ones to solve a given problem.
                 Low level heuristics, however, are not all as
                 competent/good as each other at solving the given
                 problem and some do not work together as well as
                 others. Hence the idea of measuring how good they are
                 (competence) at solving the problem and how well they
                 work together (their affinity). Models of the affinity
                 and competence properties are suggested and evaluated
                 using previous information on the performance of the
                 simple low level heuristics. The resulting model values
                 are used to improve the performance of the
                 hyper-heuristic by tailoring it not only to the
                 specific problem but the specific instance being
                 solved. The test case is a hard combinatorial problem,
                 namely the Hybrid Flow Shop scheduling problem.
                 Numerical results on randomly generated as well as
                 real-world instances are included.",
}

Genetic Programming entries for Abdel Salhi Jose Antonio Vazquez Rodriguez

Citations