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.

Keywords

Local Search Examination Timetabling Acceptance Policy Local Search Neighborhood Domain Vocabulary 
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.
    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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