The Cross-Domain Heuristic Search Challenge – An International Research Competition

  • Edmund K. Burke
  • Michel Gendreau
  • Matthew Hyde
  • Graham Kendall
  • Barry McCollum
  • Gabriela Ochoa
  • Andrew J. Parkes
  • Sanja Petrovic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6683)

Abstract

The first Cross-domain Heuristic Search Challenge (CHeSC 2011) seeks to bring together practitioners from operational research, computer science and artificial intelligence who are interested in developing more generally applicable search methodologies. The challenge is to design a search algorithm that works well, not only across different instances of the same problem, but also across different problem domains. This article overviews the main features of this challenge.

Keywords

Problem Domain Software Interface Iterate Local Search Algorithm Component Domain Barrier 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Burke, E.K., Curtois, T., Hyde, M., Kendall, G., Ochoa, G., Petrovic, S., Vazquez-Rodriguez, J.A.: HyFlex: A flexible framework for the design and analysis of hyper-heuristics. In: Multidisciplinary International Scheduling Conference (MISTA 2009), Dublin, Ireland, pp. 790–797 (August 2009)Google Scholar
  2. 2.
    Cowling, P., Kendall, G., Soubeiga, E.: A hyperheuristic approach to scheduling a sales summit. In: Burke, E., Erben, W. (eds.) PATAT 2000. LNCS, vol. 2079, pp. 176–190. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  3. 3.
    Burke, E.K., Hart, E., Kendall, G., Newall, J., Ross, P., Schulenburg, S.: Hyper-heuristics: An emerging direction in modern search technology. In: Glover, F., Kochenberger, G. (eds.) Handbook of Metaheuristics, pp. 457–474. Kluwer, Dordrecht (2003)CrossRefGoogle Scholar
  4. 4.
    Bleuler, S., Laumanns, M., Thiele, L., Zitzler, E.: PISA – A Platform and Programming Language Independent Interface for Search Algorithms. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 494–508. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  5. 5.
    Burke, E.K., Curtois, T., Hyde, M., Kendall, G., Ochoa, G., Petrovic, S., Vazquez-Rodriguez, J.A., Gendreau, M.: Iterated local search vs. hyper-heuristics: Towards general-purpose search algorithms. In: IEEE Congress on Evolutionary Computation (CEC 2010), Barcelona, Spain, pp. 3073–3080 (July 2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Edmund K. Burke
    • 1
  • Michel Gendreau
    • 2
  • Matthew Hyde
    • 1
  • Graham Kendall
    • 1
  • Barry McCollum
    • 3
  • Gabriela Ochoa
    • 1
  • Andrew J. Parkes
    • 1
  • Sanja Petrovic
    • 1
  1. 1.Automated Scheduling, Optimisation and Planning (ASAP) Group, School of Computer ScienceUniversity of NottinghamNottinghamUK
  2. 2.CIRRELT, University of MontrealCanada
  3. 3.School of Electronics, Electrical Engineering and Computer ScienceQueen’s UniversityBelfastUK

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