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A New Hyperheuristic Algorithm for Cross-Domain Search Problems

  • Andreas Lehrbaum
  • Nysret Musliu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7219)

Abstract

This paper describes a new hyperheuristic algorithm that performs well over a variety of different problem classes. A novel method for switching between working on a single solution and a pool of solutions is proposed. This method is combined with an adaptive strategy that guides the selection of the underlying low-level heuristics throughout the search. The algorithm was implemented based on the HyFlex framework and was submitted as a candidate for the Cross-Domain Heuristic Search Challenge 2011.

Keywords

Problem Instance Flow Shop Tabu List Search Phase Permutation Flow Shop 
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.
    Cowling, P.I., 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
  2. 2.
    Burke, E., Hyde, M., Kendall, G., Ochoa, G., Ozcan, E., Qu, R.: A survey of hyper-heuristics. Computer Science Technical Report No. NOTTCS-TR-SUB-0906241418-2747, School of Computer Science and Information Technology, University of Nottingham (2009)Google Scholar
  3. 3.
    Burke, E., Curtois, T., Hyde, M., Kendall, G., Ochoa, G., Petrovic, S., Antonio, J.: HyFlex: A Flexible Framework for the Design and Analysis of Hyper-heuristics. In: Multidisciplinary International Scheduling Conference (MISTA 2009), Dublin, Ireland, p. 790 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Andreas Lehrbaum
    • 1
  • Nysret Musliu
    • 1
  1. 1.Database and Artificial Intelligence GroupVienna University of TechnologyAustria

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