Advertisement

An Investigation of Selection Hyper-heuristics in Dynamic Environments

  • Berna Kiraz
  • A. Şima Uyar
  • Ender Özcan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6624)

Abstract

Hyper-heuristics are high level methodologies that perform search over the space of heuristics rather than solutions for solving computationally difficult problems. A selection hyper-heuristic framework provides means to exploit the strength of multiple low level heuristics where each heuristic can be useful at different stages of the search. In this study, the behavior of a range of selection hyper-heuristics is investigated in dynamic environments. The results show that hyper-heuristics embedding learning heuristic selection methods are sufficiently adaptive and can respond to different types of changes in a dynamic environment.

Keywords

Reinforcement Learning Dynamic Environment Choice Function Simple Random Heuristic Selection 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ayob, M., Kendall, G.: A monte carlo hyper-heuristic to optimise component placement sequencing for multi head placement machine. In: Proceedings of the Int. Conf. on Intelligent Technologies, pp. 132–141 (2003)Google Scholar
  2. 2.
    Branke, J.: Evolutionary optimization in dynamic environments. Kluwer, Dordrecht (2002)CrossRefzbMATHGoogle Scholar
  3. 3.
    Burke, E., 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.
    Burke, E., Kendall, G., Misir, M., Özcan, E.: Monte carlo hyper-heuristics for examination timetabling. Annals of Operations Research, 1–18 (2010)Google Scholar
  5. 5.
    Burke, E.K., Hyde, M.R., Kendall, G., Ochoa, G., Özcan, E., Woodward, J.R.: Exploring hyper-heuristic methodologies with genetic programming. In: Kacprzyk, J., Jain, L.C., Mumford, C.L., Jain, L.C. (eds.) Computational Intelligence, Intelligent Systems Reference Library, vol. 1, pp. 177–201. Springer, Heidelberg (2009)Google Scholar
  6. 6.
    Burke, E.K., Hyde, M., Kendall, G., Ochoa, G., Ozcan, E., Qu, R.: A survey of hyper-heuristics. Tech. rep. (2009)Google Scholar
  7. 7.
    Burke, E.K., Hyde, M., Kendall, G., Ochoa, G., Özcan, E., Woodward, J.R.: A classification of hyper-heuristic approaches. In: Gendreau, M., Potvin, J.Y. (eds.) Handbook of Metaheuristics. International Series in Operations Research and Management Science, vol. 146, pp. 449–468. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Cobb, H.G.: An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent nonstationary environments. Tech. Rep. AIC-90-001, Naval Research Lab., Washington, DCGoogle Scholar
  9. 9.
    Cowling, P., Kendall, G., Soubeiga, E.: A hyper-heuristic approach to scheduling a sales summit. In: Burke, E., Erben, W. (eds.) PATAT 2000. LNCS, vol. 2079, p. 176. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  10. 10.
    Crowston, W.B., Glover, F., Thompson, G.L., Trawick, J.D.: Probabilistic and parametric learning combinations of local job shop scheduling rules. ONR Research memorandum, GSIA, Carnegie Mellon University, Pittsburgh -(117) (1963)Google Scholar
  11. 11.
    Denzinger, J., Fuchs, M.: High performance ATP systems by combining several AI methods. In: 4th Asia-Pacific Conf. on SEAL, pp. 102–107 (1997)Google Scholar
  12. 12.
    Fisher, H., Thompson, G.L.: Probabilistic learning combinations of local job-shop scheduling rules. In: Muth, J.F., Thompson, G.L. (eds.) Industrial Scheduling, pp. 225–251. Prentice-Hall, New Jersey (1963)Google Scholar
  13. 13.
    Grefenstette, J.J.: Genetic algorithms for changing environments. In: Proceedings of Parallel Problem Solving from Nature, pp. 137–1446 (1992)Google Scholar
  14. 14.
    Lewis, J., Hart, E., Ritchie, G.: A comparison of dominance mechanisms and simple mutation on nonstationary problems. In: Proceedings of Parallel Problem Solving from Nature, pp. 139–148 (1998)Google Scholar
  15. 15.
    Jin, Y., Branke, J.: Evolutionary optimization in uncertain environments-a survey. IEEE Tran. on Evolutionary Comp. 9(3), 303–317 (2005)CrossRefGoogle Scholar
  16. 16.
    Kendall, G., Mohamad, M.: Channel assignment in cellular communication using a great deluge hyperheuristic. In: IEEE Int. Conf. on Network, pp. 769–773 (2004)Google Scholar
  17. 17.
    Morrison, R.W.: Designing evolutionary algorithms for dynamic environments. Springer, Heidelberg (2004)CrossRefzbMATHGoogle Scholar
  18. 18.
    Özcan, E., Bilgin, B., Korkmaz, E.E.: A comprehensive analysis of hyper-heuristics. Intelligent Data Analysis 12, 3–23 (2008)Google Scholar
  19. 19.
    Özcan, E., Etaner-Uyar, S., Burke, E.: A greedy hyper-heuristic in dynamic environments. In: GECCO 2009 Workshop on Automated Heuristic Design: Crossing the Chasm for Search Methods, pp. 2201–2204 (2009)Google Scholar
  20. 20.
    Özcan, E., Misir, M., Ochoa, G., Burke, E.K.: A reinforcement learning - great-deluge hyper-heuristic for examination timetabling. International Journal of Applied Metaheuristic Computing 1(1), 39–59 (2010)CrossRefGoogle Scholar
  21. 21.
    Ross, P.: Hyper-heuristics. In: Burke, E.K., Kendall, G. (eds.) Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, ch. 17, pp. 529–556. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  22. 22.
    Ursem, R.K.: Multinational GA optimization techniques in dynamic environments. In: Proceedings of the Genetic Evol. Comput. Conf., pp. 19–26 (2000)Google Scholar
  23. 23.
    Uyar, A.S., Harmanci, A.E.: A new population based adaptive domination change mechanism for diploid genetic algorithms in dynamic environments. Soft Computing 9, 803–814 (2005)CrossRefzbMATHGoogle Scholar
  24. 24.
    Vavak, F., Jukes, K., Fogarty, T.C.: Adaptive combustion balancing in multiple burner boiler using a genetic algorithm with variable range of local search. In: Proceedings of the Int. Conf. on Genetic Algorithms, pp. 719–726 (1997)Google Scholar
  25. 25.
    Yang, S.: Genetic algorithms with memory and elitism based immigrants in dynamic environments. Evolutionary Computation 16, 385–416 (2008)CrossRefGoogle Scholar
  26. 26.
    Yang, S., Ong, Y.S., Jin, Y. (eds.): Evolutionary Computation in Dynamic and Uncertain Environments. SCI, vol. 51. Springer, Heidelberg (2007)zbMATHGoogle Scholar
  27. 27.
    Yang, S., Yao, X.: Population-based incremental learning with associative memory for dynamic environments. Trans. on Evolutionary Comp. 12, 542–561 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Berna Kiraz
    • 1
  • A. Şima Uyar
    • 2
  • Ender Özcan
    • 3
  1. 1.Institute of Science and TechnologyIstanbul Technical UniversityTurkey
  2. 2.Faculty of Computer and InformaticsIstanbul Technical UniversityTurkey
  3. 3.School of Computer ScienceUniversity of NottinghamUK

Personalised recommendations