Cognitive Computation

, Volume 6, Issue 1, pp 66–73 | Cite as

Searching the Hyper-heuristic Design Space

  • Jerry Swan
  • John Woodward
  • Ender Özcan
  • Graham Kendall
  • Edmund Burke
Article

Abstract

We extend a previous mathematical formulation of hyper-heuristics to reflect the emerging generalization of the concept. We show that this leads naturally to a recursive definition of hyper-heuristics and to a division of responsibility that is suggestive of a blackboard architecture, in which individual heuristics annotate a shared workspace with information that may also be exploited by other heuristics. Such a framework invites consideration of the kind of relaxations of the domain barrier that can be achieved without loss of generality. We give a concrete example of this architecture with an application to the 3-SAT domain that significantly improves on a related token-ring hyper-heuristic.

Keywords

Hyper-heuristics Metaheuristics Optimization Machine-learning Blackboard architecture 

Reference

  1. 1.
    Bishop JM, Erden YJ. Computational creativity, intelligence and autonomy. Cognit Comput. 2012;4(3):209–1.CrossRefGoogle Scholar
  2. 2.
    Kendall G, Su Y. Imperfect evolutionary systems, Evolutionary Computation. IEEE Trans. 2007;11(3):294–7 doi: 10.1109/TEVC.2006.887348.Google Scholar
  3. 3.
    d’Inverno M, Luck M. Creativity through autonomy and interaction. Cognit Comput. 2012;4(3):332–46.CrossRefGoogle Scholar
  4. 4.
    Burke EK, Hyde M, Kendall G, Ochoa G, Ozcan E, Woodward JR. A classification of hyper-heuristics approaches. In: Gendreau M, Potvin J-Y, editors. Handbook of metaheuristics, 2nd Edition, vol 57 of international series in operations research & management science. Berlin:Springer; 2010. Ch. 15, p. 449–68.Google Scholar
  5. 5.
    Hayes-Roth B. A blackboard architecture for control. Artif Intell. 1985;26(3):251–21.CrossRefGoogle Scholar
  6. 6.
    Denzinger J, Fuchs M, Fuchs M. High performance ATP systems by combining several AI methods, Tech. rep., University of Kaiserslautern 1997.Google Scholar
  7. 7.
    Burke EK, Kendall G, Soubeiga E . A tabu-search hyperheuristic for timetabling and rostering. J Heuristics. 2003;9(6):451–70.CrossRefGoogle Scholar
  8. 8.
    Rattadilok P, Gaw A, Kwan RK. Distributed choice function hyper-heuristics for timetabling and scheduling. In: Burke E, Trick M, editors. Practice and theory of automated timetabling, vol. 3616 of Lecture Notes in Computer Science. Springer: Berlin; 2005. p. 51–67.Google Scholar
  9. 9.
    Cowling P, Chekhlevitch K. Hyperheuristics for managing a large collection of low-level heuristics to schedule personnel. In: The 2003 congress on evolutionary computation (CEC ‘03), vol. 2; 2003. p. 1214–21.Google Scholar
  10. 10.
    Woodward J, Parkes A, Ochoa G. A mathematical formalization of hyper-heuristics, Presented to the ’Workshop on Hyper-Heuristics’ at 10th international conference on parallel problem solving from nature (PPSN-08), Technische University Dortmund, Germany. (September 2008).Google Scholar
  11. 11.
    Peyton Jones S, et al. The Haskell 98 language and libraries: the revised report. J Funct Program. 2003;13(1):0–255 http://www.haskell.org/definition/
  12. 12.
    Battiti R, Tecchiolli G. The reactive tabu search. INFORMS J Comput 1994;6(2):126–40.CrossRefGoogle Scholar
  13. 13.
    Spinellis D. Another level of indirection. In: Oram A, Wilson G editors. Beautiful code: leading programmers explain how they think. Sebastopol: O’Reilly and Associates; 2007. Ch. 17, p. 279–91.Google Scholar
  14. 14.
    Swan J, Özcan E, Kendall G. Hyperion - a recursive hyper-heuristic framework. In: Coello C editors. Learning and intelligent optimization, Vol. 6683 of Lecture Notes in Computer Science. Springer: Berlin; 2011. p. 616–30.Google Scholar
  15. 15.
    Glover F. Tabu search-Part I. INFORMS J Comput 1989;1(3):190–206CrossRefGoogle Scholar
  16. 16.
    Kaelbling LP, Littman ML, Moore AW. Reinforcement learning: a survey. J Artif Intell Res (JAIR) 1996;4:237–85.Google Scholar
  17. 17.
    Glover F. Tabu search-Part II. INFORMS J Comput 1990;2(1):4–32.CrossRefGoogle Scholar
  18. 18.
    Eiben AE, Ruttkay Z. Self-adaptivity for constraint satisfaction: learning penalty functions In: International conference on evolutionary computation. 1996, p. 258–61.Google Scholar
  19. 19.
    Marín-Blázquez JG, Schulenburg S. A hyper-heuristic framework with xcs: learning to create novel problem-solving algorithms constructed from simpler algorithmic ingredients. In: IWLCS, 2005, p. 193–218.Google Scholar
  20. 20.
    Burke EK, Petrovic S, Qu R. Case-based heuristic selection for timetabling problems. J Sched. 2006;9(2):115–32.CrossRefGoogle Scholar
  21. 21.
    Burke EK, Hyde MR, Kendall G, Ochoa G, Ozcan E, Woodward JR. Exploring hyper-heuristic methodologies with genetic programming. In: Mumford CL, Jain LC editors. Computational intelligence, Vol. 1 of intelligent systems reference library. Berlin:Springer; 2009. Ch. 6, p. 177–201.Google Scholar
  22. 22.
    Stadler PF. Landscapes and their correlation functions. J Math Chem. 1996;20:1–45. doi: 10.1007/BF01165154.CrossRefGoogle Scholar
  23. 23.
    Stadler PF. Towards a theory of landscapes. In: Lpez-Pena R, Capovilla R, Garca-Pelayo R, Waelbroeck H, Zertuche F editors. Complex systems and binary networks, Vol. 461 of Lecture notes in physics. Berlin: Springer; 1995. p. 77–163.Google Scholar
  24. 24.
    Hordijk W. A measure of landscapes. Evol Comput 1997;4(4):335–60.CrossRefGoogle Scholar
  25. 25.
    Reeves CR. Landscapes, operators and heuristic search. Ann Oper Res 1999;86:473–90.CrossRefGoogle Scholar
  26. 26.
    Reeves CR. Fitness landscapes and evolutionary algorithms. In: AE ’99: selected papers from the 4th European conference on artificial evolution. London: Springer; 2000. p. 3–20.Google Scholar
  27. 27.
    Kallel L, Naudts B, Reeves CR. Properties of fitness functions and search landscapes. London: Springer; 2001. p. 175–206.Google Scholar
  28. 28.
    Weinberger E. Correlated and uncorrelated fitness landscapes and how to tell the difference. Biol Cybern 1990;63(5):325–36.CrossRefGoogle Scholar
  29. 29.
    Berry DA, Fristedt B. Bandit problems: sequential allocation of experiments. Berlin: Springer; 1985.CrossRefGoogle Scholar
  30. 30.
    Dzeroski S, Todorovski L. Discovering dynamics: From inductive logic programming to machine discovery. J Intell Inf Syst 1995;4:89–108 doi: 10.1007/BF00962824.CrossRefGoogle Scholar
  31. 31.
    Milano M, Roli A. Magma: a multiagent architecture for metaheuristics, systems, man, and cybernetics, part B: cybernetics. IEEE Trans. 2004;34(2):925–941 doi: 10.1109/TSMCB.2003.818432.Google Scholar
  32. 32.
    Ouelhadj D, Petrovic S. A cooperative hyper-heuristic search framework. J Heuristics 2009;1–23 doi: 10.1007/s10732-009-9122-6.
  33. 33.
    Brooks R. Intelligence without representation. Artif Intell 1991;47:139–59.CrossRefGoogle Scholar
  34. 34.
    Burke E, Kendall G, Newall J, Hart E, Ross P, Schulenburg S. Hyper-heuristics: an emerging direction in modern search technology. In: Glover F, Kochenberger G, Hillier FS, editors. Handbook of Metaheuristics, Vol. 57 of international series in operations research and management science. New York: Springer; 2003. p. 457–74.CrossRefGoogle Scholar
  35. 35.
    Booch G, Maksimchuk RA, Engle MW, Young BJ, Connallen J, Houston KA. Object-oriented analysis and design with applications, third edition, ACM SIGSOFT software engineering notes 33(5).Google Scholar
  36. 36.
    Carver N, Lesser V. The evolution of blackboard control architectures, Tech. rep., Amherst, USA; 1992.Google Scholar
  37. 37.
    al Rifaie MM, Bishop JM, Caines S. Creativity and autonomy in swarm intelligence systems. Cognit Comput 2012;4(3):320–31.CrossRefGoogle Scholar
  38. 38.
    Kirkpatrick S, Gelatt CD, Vecchi MP. Optimization by simulated annealing. Science 1983;220:671–80.CrossRefPubMedGoogle Scholar
  39. 39.
    White S. Concepts of scale in simulated annealing. In: Proceedings of international conference on computer design; 1984, p. 646–51.Google Scholar
  40. 40.
    Hoos HH, Stützle T. SATLIB: An online resource for research on SAT. In: Gent IP, Maaren Hv, Walsh T, editors. SAT 2000, SATLIB is available online at http://www.satlib.org 2000.
  41. 41.
    Xu L, Hutter F, Hoos HH, Leyton-Brown K. Satzilla: portfolio-based algorithm selection for sat. J Artif Int Res 2008;32(1):565–606.Google Scholar
  42. 42.
    Gaspero LD, Schaerf A. Easylocal++: an object-oriented framework for the flexible design of local-search algorithms. Softw Pract Exper 2003;33(8):733–765.CrossRefGoogle Scholar
  43. 43.
    Jones T, Forrest S. Fitness distance correlation as a measure of problem difficulty for genetic algorithms. In: Proceedings of the sixth international conference on genetic algorithms. Morgan Kaufmann; 1995. p. 184–92.Google Scholar
  44. 44.
    Birattari M, Stützle T, Paquete L, Varrentrapp K. A racing algorithm for configuring metaheuristics. In: Proceedings of the genetic and evolutionary computation conference, GECCO ’02. San Francisco :Morgan Kaufmann Publishers Inc.; 2002. p. 11–18.Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Jerry Swan
    • 1
  • John Woodward
    • 1
  • Ender Özcan
    • 2
  • Graham Kendall
    • 2
  • Edmund Burke
    • 1
  1. 1.Computing Science and Mathematics, School of Natural SciencesUniversity of StirlingStirlingScotland, UK
  2. 2.School of Computer ScienceUniversity of NottinghamNottinghamUK

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