Batched Mode Hyper-heuristics

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7997)

Abstract

A primary role for hyper-heuristics is to control search processes based on moves generated by neighbourhood operators. Studies have shown that such hyper-heuristics can be effectively used, without modification, for solving unseen problem instances not only from a particular domain, but also on different problem domains. They hence provide a general-purpose software component to help reduce the implementation time needed for effective search methods. However, hyper-heuristic studies have generally used time-contract algorithms (i.e. a fixed execution time) and also solved each problem instance independently. We consider the potential gains and challenges of a hyper-heuristic being able to treat a set of instances as a batch; to be completed within an overall joint execution time. In batched mode, the hyper-heuristic can freely divide the computational effort between the individual instances, and also exploit what it learns on one instance to help solve other instances.

Keywords

Combinatorial optimisation Metaheuristics Hyper-heuristics 

References

  1. 1.
    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)Google Scholar
  2. 2.
    Chen, X., Ong, Y.S.: A conceptual modeling of meme complexes in stochastic search. IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 42(5), 612–625 (2012)CrossRefGoogle Scholar
  3. 3.
    Burke, E.K., Hyde, M., Kendall, G., Ochoa, G., Özcan, E., Qu, R.: Hyper-heuristics: a survey of the state of the art. Technical Report NOTTCS-TR-SUB-0906241418-2747, School of Computer Science, University of Nottingham (2010)Google Scholar
  4. 4.
    Özcan, E., Bilgin, B., Korkmaz, E.E.: A comprehensive analysis of hyper-heuristics. Intell. Data Anal. 12(1), 3–23 (2008)Google Scholar
  5. 5.
    Ochoa, G., et al.: HyFlex: a benchmark framework for cross-domain heuristic search. In: Hao, J.-K., Middendorf, M. (eds.) EvoCOP 2012. LNCS, vol. 7245, pp. 136–147. Springer, Heidelberg (2012)Google Scholar
  6. 6.
    Mısır, M., Verbeeck, K., De Causmaecker, P., Vanden Berghe, G.: A new hyper-heuristic implementation in HyFlex: a study on generality. In: Fowler, J., Kendall, G., McCollum, B. (eds.) Proceedings of the MISTA’11, pp. 374–393 (2011)Google Scholar
  7. 7.
    Zilberstein, S., Russell, S.J.: Approximate reasoning using anytime algorithms. In: Natarajan, S. (ed.) Imprecise and Approximate Computation. Kluwer Academic Publishers, The Netherlands (1995)Google Scholar
  8. 8.
    Kheiri, A., Özcan, E.: A Hyper-heuristic with a round robin neighbourhood selection. In: Middendorf, M., Blum, C. (eds.) EvoCOP 2013. LNCS, vol. 7832, pp. 1–12. Springer, Heidelberg (2013)Google Scholar
  9. 9.
    Parkes, A.J., Walser, J.P.: Tuning local search for satisfiability testing. In: Proceedings of AAAI 1996, pp. 356–362 (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.School of Computer ScienceUniversity of NottinghamNottinghamUK

Personalised recommendations