Exploring Hyper-heuristic Methodologies with Genetic Programming

  • Edmund K. Burke
  • Mathew R. Hyde
  • Graham Kendall
  • Gabriela Ochoa
  • Ender Ozcan
  • John R. Woodward
Part of the Intelligent Systems Reference Library book series (ISRL, volume 1)


Hyper-heuristics represent a novel search methodology that is motivated by the goal of automating the process of selecting or combining simpler heuristics in order to solve hard computational search problems. An extension of the original hyper-heuristic idea is to generate new heuristics which are not currently known. These approaches operate on a search space of heuristics rather than directly on a search space of solutions to the underlying problem which is the case with most meta-heuristics implementations. In the majority of hyper-heuristic studies so far, a framework is provided with a set of human designed heuristics, taken from the literature, and with good measures of performance in practice. A less well studied approach aims to generate new heuristics from a set of potential heuristic components. The purpose of this chapter is to discuss this class of hyper-heuristics, in which Genetic Programming is the most widely used methodology. A detailed discussion is presented including the steps needed to apply this technique, some representative case studies, a literature review of related work, and a discussion of relevant issues. Our aim is to convey the exciting potential of this innovative approach for automating the heuristic design process.


Genetic Program Problem Instance Problem Domain Local Search Heuristic Genetic Program System 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Asmuni, H., Burke, E.K., Garibaldi, J.M., Mccollum, B.: A novel fuzzy approach to evaluate the quality of examination timetabling. In: Proceedings of the 6th International Conference on the Practice and Theory of Automated Timetabling, pp. 82–102 (2006)Google Scholar
  2. 2.
    Bäck, T.: An overview of parameter control methods by self-adaption in evolutionary algorithms. Fundam. Inf. 35(1-4), 51–66 (1998)MATHGoogle Scholar
  3. 3.
    Bader-El-Din, M.B., Poli, R.: Generating SAT local-search heuristics using a GP hyper-heuristic framework. In: Monmarché, N., Talbi, E.-G., Collet, P., Schoenauer, M., Lutton, E. (eds.) EA 2007. LNCS, vol. 4926, pp. 37–49. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  4. 4.
    Bai, R., Kendall, G.: An investigation of automated planograms using a simulated annealing based hyper-heuristic. In: Ibaraki, T., Nonobe, K., Yagiura, M. (eds.) Metaheuristics: Progress as Real Problem Solver. Operations Research/Computer Science Interface Serices, vol. 32, pp. 87–108. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic Programming – An Introduction; On the Automatic Evolution of Computer Programs and its Applications. Morgan Kaufmann, San Francisco (1998)MATHGoogle Scholar
  6. 6.
    Battiti, R.: Reactive search: Toward self–tuning heuristics. In: Rayward-Smith, V.J., Osman, I.H., Reeves, C.R., Smith, G.D. (eds.) Modern Heuristic Search Methods, pp. 61–83. John Wiley & Sons Ltd, Chichester (1996)Google Scholar
  7. 7.
    Battiti, R., Brunato, M.: Reactive search: Machine learning for memory-based heuristics. In: Gonzalez, T.F. (ed.) Approximation Algorithms and Metaheuristics, ch. 21, pp. 1–17. Taylor and Francis Books/CRC Press, Washington (2007)Google Scholar
  8. 8.
    Birattari, M.: The problem of tuning metaheuristics as seen from a machine learning perspective. Ph.D. thesis, Universite Libre de Bruxelles (2004)Google Scholar
  9. 9.
    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)Google Scholar
  10. 10.
    Burke, E.K., Hyde, M.R., Kendall, G.: Evolving bin packing heuristics with genetic programming. In: Runarsson, T.P., Beyer, H.-G., Burke, E.K., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) PPSN 2006. LNCS, vol. 4193, pp. 860–869. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    Burke, E.K., Hyde, M.R., Kendall, G., Woodward, J.: Automatic heuristic generation with genetic programming: evolving a jack-of-all-trades or a master of one. In: Thierens, D., et al. (eds.) Proceedings of the 9th annual conference on Genetic and evolutionary computation GECCO 2007, vol. 2, pp. 1559–1565. ACM Press, London (2007)CrossRefGoogle Scholar
  12. 12.
    Burke, E.K., Hyde, M.R., Kendall, G., Woodward, J.R.: The scalability of evolved on line bin packing heuristics. In: Srinivasan, D., Wang, L. (eds.) 2007 IEEE Congress on Evolutionary Computation, pp. 2530–2537. IEEE Computational Intelligence Society/IEEE Press, Singapore (2007)CrossRefGoogle Scholar
  13. 13.
    Burke, E.K., Kendall, G., Soubeiga, E.: A tabu-search hyperheuristic for timetabling and rostering. J. of Heuristics 9(6), 451–470 (2003)CrossRefGoogle Scholar
  14. 14.
    Burke, E.K., McCollum, B., Meisels, A., Petrovic, S., Qu, R.: A graph-based hyper-heuristic for educational timetabling problems. Eur. J. of Oper. Res. 176, 177–192 (2007)MATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Burke, E.K., Petrovic, S., Qu, R.: Case based heuristic selection for timetabling problems. J. of Sched. 9(2), 115–132 (2006)MATHCrossRefGoogle Scholar
  16. 16.
    Coffman Jr., E.G., Galambos, G., Martello, S., Vigo, D.: Bin packing approximation algorithms: Combinatorial analysis. In: Du, D.Z., Pardalos, P.M. (eds.) Handbook of Combinatorial Optimization, pp. 151–207. Kluwer, Dordrecht (1998)Google Scholar
  17. 17.
    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) (selected papers)CrossRefGoogle Scholar
  18. 18.
    Dowsland, K.A., Soubeiga, E., Burke, E.K.: A simulated annealing hyper-heuristic for determining shipper sizes. Eur. J. of Oper. Res. 179(3), 759–774 (2007)MATHCrossRefGoogle Scholar
  19. 19.
    Drechsler, N., Schmiedle, F., Grosse, D., Drechsler, R.: Heuristic learning based on genetic programming. In: Miller, J., Tomassini, M., Lanzi, P.L., Ryan, C., Tetamanzi, A.G.B., Langdon, W.B. (eds.) EuroGP 2001. LNCS, vol. 2038, pp. 1–10. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  20. 20.
    Drechsler, R., Becker, B.: Learning heuristics by genetic algorithms. In: ASP-DAC 1995: Proceedings of the 1995 conference on Asia Pacific design automation (CD-ROM), p. 53. ACM, New York (1995)CrossRefGoogle Scholar
  21. 21.
    Eiben, A.E., Hinterding, R., Michalewicz, Z.: Parameter control in Evolutionary Algorithms. IEEE Trans. on Evol. Comput. 3(2), 124–141 (1999)CrossRefGoogle Scholar
  22. 22.
    Falkenauer, E., Delchambre, A.: A genetic algorithm for bin packing and line balancing. In: Proc. of the IEEE 1992 International Conference on Robotics and Automation, pp. 1186–1192 (1992)Google Scholar
  23. 23.
    Fang, H.L., Ross, P., Corne, D.: A promising hybrid GA/heuristic approach for open-shop scheduling problems. In: Eur. Conference on Artificial Intelligence (ECAI 2004), pp. 590–594 (1994)Google Scholar
  24. 24.
    Fukunaga, A.S.: Automated discovery of composite sat variable-selection heuristics. In: AAAI/IAAI, pp. 641–648 (2002)Google Scholar
  25. 25.
    Fukunaga, A.S.: Automated discovery of local search heuristics for satisfiability testing. Evol. Comput. 16(1), 31–61 (2008)CrossRefGoogle Scholar
  26. 26.
    Gagliolo, M., Schmidhuber, J.: Learning dynamic algorithm portfolios. Ann. of Math. and Artif. Intell. 47(3-4), 295–328 (2006)MATHCrossRefMathSciNetGoogle Scholar
  27. 27.
    Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. Series of Books in the Mathematical Sciences. W. H. Freeman, New York (1979)MATHGoogle Scholar
  28. 28.
    Geiger, C.D., Uzsoy, R., Aytug, H.: Rapid modeling and discovery of priority dispatching rules: An autonomous learning approach. J. of Sched. 9(1), 7–34 (2006)MATHCrossRefGoogle Scholar
  29. 29.
    Hart, E., Ross, P., Nelson, J.: Solving a real-world problem using an evolving heuristically driven schedule builder. Evol. Comput. 6(1), 61–80 (1998)CrossRefGoogle Scholar
  30. 30.
    Hoos, H.H., Sttzle, T.: Stochastic Local Search: Foundations and Applications. Morgan Kaufmann / Elsevier (2005)Google Scholar
  31. 31.
    Huberman, B.A., Lukose, R.M., Hogg, T.: An economics approach to hard computational problems. Sci. 275, 51–54 (1997)CrossRefGoogle Scholar
  32. 32.
    Hutter, F., Hamadi, Y., Hoos, H.H., Leyton-Brown, K.: Performance prediction and automated tuning of randomized and parametric algorithms. In: Benhamou, F. (ed.) CP 2006. LNCS, vol. 4204, pp. 213–228. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  33. 33.
    Hutter, F., Hoos, H.H., Stützle, T.: Automatic algorithm configuration based on local search. In: AAAI, pp. 1152–1157. AAAI Press, Menlo Park (2007)Google Scholar
  34. 34.
    Jakob, W.: HyGLEAM – an approach to generally applicable hybridization of evolutionary algorithms. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 527–536. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  35. 35.
    Jakob, W.: Towards an adaptive multimeme algorithm for parameter optimisation suiting the engineers’ needs. In: Runarsson, T.P., Beyer, H.-G., Burke, E.K., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) PPSN 2006. LNCS, vol. 4193, pp. 132–141. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  36. 36.
    Johnson, D., Demers, A., Ullman, J., Garey, M., Graham, R.: Worst-case performance bounds for simple one-dimensional packaging algorithms. SIAM J. on Comput. 3(4), 299–325 (1974)CrossRefMathSciNetGoogle Scholar
  37. 37.
    Keller, R.E., Poli, R.: Linear genetic programming of parsimonious metaheuristics. In: Srinivasan, D., Wang, L. (eds.) 2007 IEEE Congress on Evolutionary Computation, pp. 4508–4515. IEEE Computational Intelligence Society/IEEE Press, Singapore (2007)CrossRefGoogle Scholar
  38. 38.
    Koza, J.R.: Genetic Programming: on the Programming of Computers by Means of Natural Selection. The MIT Press, Boston (1992)MATHGoogle Scholar
  39. 39.
    Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs. The MIT Press, Cambridge (1994)MATHGoogle Scholar
  40. 40.
    Koza, J.R., Bennett III, F.H., Andre, D., Keane, M.A.: Genetic Programming III: Darwinian Invention and Problem solving. Morgan Kaufmann, San Francisco (1999)MATHGoogle Scholar
  41. 41.
    Koza, J.R., Keane, M.A., Streeter, M.J., Mydlowec, W., Yu, J., Lanza, G.: Genetic Programming IV: Routine Human-Competitive Machine Intelligence (Genetic Programming). Springer, Heidelberg (2003)MATHGoogle Scholar
  42. 42.
    Koza, J.R., Poli, R.: Genetic programming. In: Burke, E.K., Kendall, G. (eds.) Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, pp. 127–164. Springer, Boston (2005)Google Scholar
  43. 43.
    Krasnogor, N., Gustafson, S.: A study on the use of ’self-generation’ in memetic algorithms. Nat. Comput. 3(1), 53–76 (2004)MATHCrossRefMathSciNetGoogle Scholar
  44. 44.
    Krasnogor, N., Smith, J.E.: Emergence of profitable search strategies based on a simple inheritance mechanism. In: Proceedings of the 2001 Genetic and Evolutionary Computation Conference, pp. 432–439. Morgan Kaufmann, San Francisco (2001)Google Scholar
  45. 45.
    Mockus, J.: Application of bayesian approach to numerical methods of global and stochastic optimization. J. of Glob. Optim. 4(4), 347–366 (1994)MATHCrossRefMathSciNetGoogle Scholar
  46. 46.
    Nareyek, A.: Choosing search heuristics by non-stationary reinforcement learning. In: Resende, M.G.C., de Sousa, J.P. (eds.) Metaheuristics: Computer Decision-Making, ch. 9, pp. 523–544. Kluwer, Dordrecht (2003)Google Scholar
  47. 47.
    Ong, Y.S., Keane, A.J.: Meta-lamarckian learning in memetic algorithms. IEEE Trans. on Evol. Comput. 8, 99–110 (2004)CrossRefGoogle Scholar
  48. 48.
    Ong, Y.S., Lim, M.H., Zhu, N., Wong, K.W.: Classification of adaptive memetic algorithms: a comparative study. IEEE Trans. on Syst. Man and Cybern. Part B 36(1), 141–152 (2006)CrossRefGoogle Scholar
  49. 49.
    Ozcan, E., Bilgin, B., Korkmaz, E.E.: A comprehensive survey of hyperheuristics. Intell. Data Anal. 12(1), 3–23 (2008)Google Scholar
  50. 50.
    Poli, W.B.R., Langdon, N.F.M.: A Field Guide to Genetic Programming. Lulu Enterprises, UK (2008)Google Scholar
  51. 51.
    Rhee, W.T., Talagrand, M.: On line bin packing with items of random size. Math. Oper. Res. 18, 438–445 (1993)MATHCrossRefMathSciNetGoogle Scholar
  52. 52.
    Ropke, S., Pisinger, D.: A unified heuristic for a large class of vehicle routing problems with backhauls. Eur. J. of Oper. Res. 171(3), 750–775 (2006)MATHCrossRefMathSciNetGoogle Scholar
  53. 53.
    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)Google Scholar
  54. 54.
    Ross, P., Marin-Blazquez, J.G., Schulenburg, S., Hart, E.: Learning a procedure that can solve hard bin-packing problems: A new ga-based approach to hyper-heuristics. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2003, pp. 1295–1306. Springer, Heidelberg (2003)Google Scholar
  55. 55.
    Seiden, S.S.: On the online bin packing problem. J. ACM 49(5), 640–671 (2002)CrossRefMathSciNetGoogle Scholar
  56. 56.
    Smith, J.E.: Co-evolving memetic algorithms: A review and progress report. IEEE Trans. in Syst. Man and Cybern. Part B 37(1), 6–17 (2007)CrossRefGoogle Scholar
  57. 57.
    Stephenson, M., O’Reilly, U., Martin, M., Amarasinghe, S.: Genetic programming applied to compiler heuristic optimisation. In: Proceedings of the Eur. Conference on Genetic Programming, pp. 245–257. Springer, Heidelberg (2003)Google Scholar
  58. 58.
    Terashima-Marin, H., Flores-Alvarez, E.J., Ross, P.: Hyper-heuristics and classifier systems for solving 2D-regular cutting stock problems. In: Beyer, H.G., O’Reilly, U.M. (eds.) Proceedings of Genetic and Evolutionary Computation Conference, GECCO 2005, Washington DC, USA, June 25-29, pp. 637–643. ACM, New York (2005)CrossRefGoogle Scholar
  59. 59.
    Terashima-Marin, H., Ross, P., Valenzuela-Rendon, M.: Evolution of constraint satisfaction strategies in examination timetabling. In: Proc. of the Genetic and Evolutionary Computation Conf. GECCO 1999, pp. 635–642. Morgan Kaufmann, San Francisco (1999)Google Scholar
  60. 60.
    Thrun, S., Pratt, L.: Learning to learn: Introduction and overview. In: Thrun, S., Pratt, L. (eds.) Learning To Learn. Kluwer Academic Publishers, Dordrecht (1998)Google Scholar
  61. 61.
    Vazquez-Rodriguez, J.A., Petrovic, S., Salhi, A.: A combined meta-heuristic with hyper-heuristic approach to the scheduling of the hybrid flow shop with sequence dependent setup times and uniform machines. In: Proceedings of the 3rd Multidisciplinary International Scheduling Conference: Theory and Applications (MISTA 2007), pp. 506–513 (2007)Google Scholar
  62. 62.
    Wah, B.W., Ieumwananonthachai, A.: Teacher: A genetics-based system for learning and for generalizing heuristics. In: Yao, X. (ed.) Evol. Comput., pp. 124–170. World Scientific Publishing Co. Pte. Ltd, Singapore (1999)Google Scholar
  63. 63.
    Wah, B.W., Ieumwananonthachai, A., Chu, L.C., Aizawa, A.: Genetics-based learning of new heuristics: Rational scheduling of experiments and generalization. IEEE Trans. on Knowl. and Data Eng. 7(5), 763–785 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Edmund K. Burke
    • 1
  • Mathew R. Hyde
    • 1
  • Graham Kendall
    • 1
  • Gabriela Ochoa
    • 1
  • Ender Ozcan
    • 1
  • John R. Woodward
    • 2
  1. 1.School of Computer ScienceUniversity of NottinghamNottinghamUK
  2. 2.University of NottinghamNingboChina

Personalised recommendations