Choosing the Fittest Subset of Low Level Heuristics in a Hyperheuristic Framework

  • Konstantin Chakhlevitch
  • Peter Cowling
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3448)

Abstract

A hyperheuristic is a high level procedure which searches over a space of low level heuristics rather than directly over the space of problem solutions. The sequence of low level heuristics, applied in an order which is intelligently determined by the hyperheuristic, form a solution method for the problem. In this paper, we consider a hyperheuristic-based methodology where a large set of low level heuristics is constructed by combining simple selection rules. Given sufficient time, this approach is able to achieve high quality results for a real-world personnel scheduling problem. However, some low level heuristics in the set do not make valuable contributions to the search and only slow down the solution process. We introduce learning strategies into hyperheuristics in order to select a fit subset of low level heuristics tailored to a particular problem instance. We compare a range of selection approaches applied to a varied collection of real-world personnel scheduling problem instances.

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References

  1. 1.
    Ernst, A.T., Jiang, H., Krishnamoorthy, M., Sier, D.: Staff Scheduling and Rostering: A Review of Applications. Methods and Models. European Journal of Operational Research 153, 3–27 (2004)MATHMathSciNetGoogle Scholar
  2. 2.
    Burke, E., Kendall, G., Newall, J., Hart, E., Ross, P., Schulenburg, S.: An Emerging Direction in Modern Search Technology. In: Glover, F., Kochenberger, G.A. (eds.) Handbook of Metaheuristics, pp. 457–474. Kluwer Academic Publishers, Dordrecht (2003)Google Scholar
  3. 3.
    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
  4. 4.
    Gratch, J., Chien, S.: Adaptive Problem-Solving for Large-Scale Scheduling Problems: A Case Study. Journal of Artificial Intelligence Research 4, 365–396 (1996)Google Scholar
  5. 5.
    Nareyek, A.: Choosing Search Heuristics by Non-Stationary Reinforcement Learning. In: Resende, M., de Souza, J. (eds.) Metaheuristics: Computer Decision-Making, pp. 523–544. Kluwer Academic Publishers, Dordrecht (2003)Google Scholar
  6. 6.
    Ross, P., Schulenburg, S., Marín-Blázquez, J.G., Hart, E.: Hyper-Heuristics: Learning to Combine Simple Heuristics in Bin-packing Problems. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2002), pp. 942–948. Morgan Kauffmann, San Francisco (2002)Google Scholar
  7. 7.
    Hart, E., Ross, P., Nelson, J.: Solving a Real-World Problem Using an Evolving Heuristically Driven Schedule Builder. Evolutionary Computation 6, 61–80 (1998)CrossRefGoogle Scholar
  8. 8.
    Cowling, P., Kendall, G., Han, L.: An Investigation of a Hyperheuristic Genetic Algorithm Applied to a Trainer Scheduling Problem. In: Proceedings of 2002 Congress on Evolutionary Computation (CEC 2002), pp. 1185–1190. IEEE Computer Society Press, Honolulu (2002)CrossRefGoogle Scholar
  9. 9.
    Han, L., Kendall, G., Cowling, P.: An Adaptive Length Chromosome Hyperheuristic Genetic Algorithm for a Trainer Scheduling Problem. In: Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution and Learning (SEAL 2002), Orchid Country Club, Singapore, pp. 267–271 (2002)Google Scholar
  10. 10.
    Burke, E., Kendall, G., Soubeiga, E.: A Tabu-Search Hyperheuristic for Timetabling and Rostering. Journal of Heuristics 9, 451–470 (2003)CrossRefGoogle Scholar
  11. 11.
    Cowling, P., Kendall, G., Soubeiga, E.: A Parameter-Free Hyperheuristic for Scheduling a Sales Summit. In: Proceedings of the Third Metaheuristic International Conference (MIC 2001), Porto, Portugal, pp. 127–131 (2001)Google Scholar
  12. 12.
    Corne, D., Ross, P.: Peckish Initialisation Strategies for Evolutionary Timetabling. In: Burke, E.K., Ross, P. (eds.) PATAT 1995. LNCS, vol. 1153, pp. 227–240. Springer, Heidelberg (1996)Google Scholar
  13. 13.
    Cowling, P., Chakhlevitch, K.: Hyperheuristics for Managing a Large Collection of Low Level Heuristics to Schedule Personnel. In: Proceedings of the 2003 IEEE Congress on Evolutionary Computation (CEC 2003), pp. 1214–1221. IEEE Computer Society Press, Canberra (2003)CrossRefGoogle Scholar
  14. 14.
    Chakhlevitch, K.: Hyperheuristics Which Manage Large Collections of Low Level Heuristics. PhD thesis (in preparation)Google Scholar
  15. 15.
    Glover, F., Laguna, M.: Tabu Search. Kluwer Academic Publishers, Dordrecht (1997)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Konstantin Chakhlevitch
    • 1
  • Peter Cowling
    • 1
  1. 1.MOSAIC Research Centre, Department of ComputingUniversity of BradfordBradfordUnited Kingdom

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