Hyperion – A Recursive Hyper-Heuristic Framework

  • Jerry Swan
  • Ender Özcan
  • Graham Kendall
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6683)

Abstract

Hyper-heuristics are methodologies used to search the space of heuristics for solving computationally difficult problems. We describe an object-oriented domain analysis for hyper-heuristics that orthogonally decomposes the domain into generative policy components. The framework facilitates the recursive instantiation of hyper-heuristics over hyper-heuristics, allowing further exploration of the possibilities implied by the hyper-heuristic concept. We describe Hyperion, a JavaTM class library implementation of this domain analysis.

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References

  1. 1.
    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, Inc., New Jersey (1963)Google Scholar
  2. 2.
    Crowston, W., Glover, F., Thompson, G., Trawick, J.: Probabilistic and parameter learning combinations of local job shop scheduling rules. In: ONR Research Memorandum. GSIA, vol. 117, Carnegie Mellon University, Pittsburgh (1963)Google Scholar
  3. 3.
    Denzinger, J., Fuchs, M., Fuchs, M.: High Performance ATP Systems by combining several AI Methods. In: Proceedings of the 4th Asia-Pacific Conference on SEAL, IJCAI, pp. 102–107 (1997)Google Scholar
  4. 4.
    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
  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)CrossRefGoogle Scholar
  6. 6.
    Ross, P.: Hyper-heuristics. In: Burke, E.K., Kendall, G. (eds.) Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, pp. 529–556. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    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
  8. 8.
    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, US (2010)CrossRefGoogle Scholar
  9. 9.
    Özcan, E., Bilgin, B., Korkmaz, E.E.: A comprehensive analysis of hyper-heuristics. Intell. Data Anal. 12, 3–23 (2008)Google Scholar
  10. 10.
    Czarnecki, K., Eisenecker, U.: Generative Programming: Methods, Tools, and Applications. Addison-Wesley Professional, Reading (2000)Google Scholar
  11. 11.
    Fink, A., Voß, S.: Hotframe: A heuristic optimization framework. In: Voß, S., Woodruff, D. (eds.) Optimization Software Class Libraries. OR/CS Interfaces Series, pp. 81–154. Kluwer Academic Publishers, Boston (2002)Google Scholar
  12. 12.
    Gaspero, L.D., Schaerf, A.: Easylocal++: An Object-oriented Framework for the flexible design of Local-Search Algorithms. Softw., Pract. Exper. 33, 733–765 (2003)CrossRefGoogle Scholar
  13. 13.
    Voudouris, C., Dorne, R., Lesaint, D., Liret, A.: iOpt: A Software Toolkit for Heuristic Search Methods. In: Walsh, T. (ed.) CP 2001. LNCS, vol. 2239, pp. 716–729. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  14. 14.
    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 (2009)Google Scholar
  15. 15.
    Gamma, E., Helm, R., Johnson, R.E., Vlissides, J.M.: Design patterns: Abstraction and reuse of object-oriented design. In: Wang, J. (ed.) ECOOP 1993. LNCS, vol. 707, pp. 406–431. Springer, Heidelberg (1993)Google Scholar
  16. 16.
    Ayob, M., Kendall, G.: A monte carlo hyper-heuristic to optimise component placement sequencing for multi head placement machine. In: Proceedings of the International Conference on Intelligent Technologies (InTech 2003), Chiang Mai, Thailand, pp. 132–141 (2003)Google Scholar
  17. 17.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)MathSciNetCrossRefMATHGoogle Scholar
  18. 18.
    Bai, R., Kendall, G.: An investigation of automated planograms using a simulated annealing based hyper-heuristics. In: Ibaraki, T., Nonobe, K., Yagiura, M. (eds.) Metaheuristics: Progress as Real Problem Solver, pp. 87–108. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  19. 19.
    Burke, E., Kendall, G., Misir, M., Özcan, E.: Monte carlo hyper-heuristics for examination timetabling. Annals of Operations Research 2, 1–18 (2010), 10.1007/s10479-010-0782-2MATHGoogle Scholar
  20. 20.
    Dueck, G.: New optimization heuristics: The great deluge algorithm and the record-to record travel. Journal of Computational Physics 104, 86–92 (1993)CrossRefMATHGoogle Scholar
  21. 21.
    Mitchell, M., Holland, J.H.: When will a genetic algorithm outperform hill climbing? In: Proceedings of the 5th International Conference on Genetic Algorithms, vol. 647. Morgan Kaufmann Publishers Inc., San Francisco (1993)Google Scholar
  22. 22.
    Kaelbling, L.P., Littman, M.L., Moore, A.P.: Reinforcement learning: A survey. J. Artif. Intell. Res. (JAIR) 4, 237–285 (1996)Google Scholar
  23. 23.
    Özcan, E., Misir, M., Ochoa, G., Burke, E.: A reinforcement learning - great-deluge hyper-heuristic for examination timetabling. International Journal of Applied Metaheuristic Computing, 39–59 (2010)Google Scholar
  24. 24.
    Herdy, M.: Application of the evolutionsstrategie to discrete optimization problems. In: Schwefel, H.-P., Männer, R. (eds.) PPSN 1990. LNCS, vol. 496, pp. 188–192. Springer, Heidelberg (1991)CrossRefGoogle Scholar
  25. 25.
    Glover, F.: Tabu Search - Part I. INFORMS Journal on Computing 1, 190–206 (1989)CrossRefMATHGoogle Scholar
  26. 26.
    Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)MATHGoogle Scholar
  27. 27.
    Ortiz-Bayliss, J.C., Özcan, E., Parkes, A.J., Terashima-Marin, H.: Mapping the performance of heuristics for constraint satisfaction, pp. 1–8 (2010)Google Scholar
  28. 28.
    Hyde, M., Özcan, E., Burke, E.K.: Multilevel search for evolving the acceptance criteria of a hyper-heuristic. In: Proceedings of the 4th Multidisciplinary Int. Conf. on Scheduling: Theory and Applications, pp. 798–801 (2009)Google Scholar
  29. 29.
    Ersoy, E., Özcan, E., Uyar, C.: Memetic algorithms and hyperhill-climbers. In: Baptiste, P., Kendall, G., Kordon, A.M., Sourd, F. (eds.) 3rd Multidisciplinary Int. Conf. On Scheduling: Theory and Applications, pp. 159–166 (2007)Google Scholar
  30. 30.
    White, S.: Concepts of scale in simulated annealing. In: Proc. Int’l Conf. on Computer Design, pp. 646–651 (1984)Google Scholar
  31. 31.
    Hoos, H.H., Stützle, T.: SATLIB: An online resource for research on SAT. In: Gent, I.P., Maaren, H.V., Walsh, T. (eds.) SAT 2000 (2000), SATLIB is available online at www.satlib.org
  32. 32.
    Montana, D.J.: Strongly typed genetic programming. Evolutionary Computation 3, 199–230 (1995)CrossRefGoogle Scholar
  33. 33.
    Iclanzan, D., Dumitrescu, D.: Overcoming hierarchical difficulty by hill-climbing the building block structure. In: GECCO 2007: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, pp. 1256–1263. ACM, New York (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jerry Swan
    • 1
  • Ender Özcan
    • 1
  • Graham Kendall
    • 1
  1. 1.Automated Scheduling, Optimisation and Planning (ASAP) Research Group, School of Computer ScienceUniversity of NottinghamNottinghamUK

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