Multi-sample evolution of robust black-box search algorithms

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  author =       "Matthew A. Martin and Daniel R. Tauritz",
  title =        "Multi-sample evolution of robust black-box search
  booktitle =    "GECCO Comp '14: Proceedings of the 2014 conference
                 companion on Genetic and evolutionary computation
  year =         "2014",
  editor =       "Christian Igel and Dirk V. Arnold and 
                 Christian Gagne and Elena Popovici and Anne Auger and 
                 Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and 
                 Kalyanmoy Deb and Benjamin Doerr and James Foster and 
                 Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and 
                 Hitoshi Iba and Christian Jacob and Thomas Jansen and 
                 Yaochu Jin and Marouane Kessentini and 
                 Joshua D. Knowles and William B. Langdon and Pedro Larranaga and 
                 Sean Luke and Gabriel Luque and John A. W. McCall and 
                 Marco A. {Montes de Oca} and Alison Motsinger-Reif and 
                 Yew Soon Ong and Michael Palmer and 
                 Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and 
                 Guenther Ruhe and Tom Schaul and Thomas Schmickl and 
                 Bernhard Sendhoff and Kenneth O. Stanley and 
                 Thomas Stuetzle and Dirk Thierens and Julian Togelius and 
                 Carsten Witt and Christine Zarges",
  isbn13 =       "978-1-4503-2881-4",
  keywords =     "genetic algorithms, genetic programming, self-*
                 search: Poster",
  pages =        "195--196",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Vancouver, BC, Canada",
  URL =          "",
  DOI =          "doi:10.1145/2598394.2598448",
  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 poster paper presents a second generation
                 hyper-heuristic employing a multi-sample training
                 approach to alleviate the overspecialization problem. A
                 variety of experiments demonstrated the significant
                 increase in the robustness of the generated algorithms
                 due to the multi-sample approach, clearly showing its
                 ability to outperform established BBSAs. 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{2598448} Distributed at

Genetic Programming entries for Matthew A Martin Daniel R Tauritz