A problem configuration study of the robustness of a black-box search algorithm hyper-heuristic

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@InProceedings{Martin:2014:GECCOcompa,
  author =       "Matthew A. Martin and Daniel R. Tauritz",
  title =        "A problem configuration study of the robustness of a
                 black-box search algorithm hyper-heuristic",
  booktitle =    "GECCO 2014 4th workshop on evolutionary computation
                 for the automated design of algorithms",
  year =         "2014",
  editor =       "John Woodward and Jerry Swan and Earl Barr",
  isbn13 =       "978-1-4503-2881-4",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "1389--1396",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Vancouver, BC, Canada",
  URL =          "http://doi.acm.org/10.1145/2598394.2609872",
  DOI =          "doi:10.1145/2598394.2609872",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Black-Box Search Algorithms (BBSAs) tailored to a
                 specific problem class may be expected to significantly
                 outperform more general purpose problem solvers,
                 including canonical evolutionary algorithms. Recent
                 work has introduced a novel approach to evolving
                 tailored BBSAs through a genetic programming
                 hyper-heuristic. However, that first generation of
                 hyper-heuristics suffered from over-specialisation.
                 This paper presents a study on the second generation
                 hyper-heuristic which employs a multi-sample training
                 approach to alleviate the over-specialisation problem.
                 In particular, the study is focused on the affect that
                 the multi-sample approach has on the problem
                 configuration landscape. A variety of experiments are
                 reported on which demonstrate the significant increase
                 in the robustness of the generated algorithms to
                 changes in problem configuration due to the
                 multi-sample approach. The results clearly show the
                 resulting BBSAs' ability to outperform established
                 BBSAs, including canonical evolutionary algorithms. The
                 trade-off between a priori computational time and the
                 generated algorithm robustness is investigated,
                 demonstrating the performance gain possible given
                 additional run-time.",
  notes =        "Also known as \cite{2609872} Distributed at
                 GECCO-2014.",
}

Genetic Programming entries for Matthew A Martin Daniel R Tauritz

Citations