A Classification of Hyper-heuristic Approaches

  • Edmund K. Burke
  • Matthew Hyde
  • Graham Kendall
  • Gabriela Ochoa
  • Ender Özcan
  • John R. Woodward
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 146)


The current state of the art in hyper-heuristic research comprises a set of approaches that share the common goal of automating the design and adaptation of heuristic methods to solve hard computational search problems. The main goal is to produce more generally applicable search methodologies. In this chapter we present an overview of previous categorisations of hyper-heuristics and provide a unified classification and definition, which capture the work that is being undertaken in this field. We distinguish between two main hyper-heuristic categories: heuristic selection and heuristic generation. Some representative examples of each category are discussed in detail. Our goals are to clarify the mainfeatures of existing techniques and to suggest new directions for hyper-heuristic research.


Transportation Expense Prefix 


  1. 1.
    Bader-El-Den, M., Poli, R.: Generating sat local-search heuristics using a gp hyper-heuristic framework. In Proceedings of Artificial Evolution (EA’07). Tours, France, Springer, Berlin (2007)Google Scholar
  2. 2.
    Bai, R.: An Investigation of Novel Approaches for Optimising Retail Shelf Space Allocation. Ph.D. thesis, School of Computer Science and Information Technology, University of Nottingham, September (2005)Google Scholar
  3. 3.
    Bai, R., Burke, E.K., Kendall, G.: Heuristic,meta-heuristic and hyper-heuristic approaches for fresh produce inventory control and shelf space allocation. J. Oper. Res. Soc. 59, 1387–1397 (2008)CrossRefGoogle Scholar
  4. 4.
    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—(Operations Research/Computer Science Interface Serices, vol. 32), pp. 87–108. Springer, Berlin (2005)Google Scholar
  5. 5.
    Bilgin, B., Özcan, E., Korkmaz, E.E.: An experimental study on hyper-heuristics and exam timetabling. Proceedings of the 6th Practice and Theory of Automated Timetabling (PATAT 2006). Lecture Notes in Computer Science, vol. 3867, pp. 394–412. Springer, Heidelberg (2007)Google Scholar
  6. 6.
    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 (2003)Google Scholar
  7. 7.
    Burke, E.K., Hyde, M., Kendall, G., Ochoa, G., Özcan, E., Woodward, J.: Exploring hyper-heuristic methodologies with genetic programming. In: Mumford C., Jain, L. (eds.) Collaborative Computational Intelligence, pp. 177–201. Springer, Berlin (2009)CrossRefGoogle Scholar
  8. 8.
    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. Proceedings of 9th ACM Genetic and Evolutionary Computation Conference (GECCO’07), London, UK, July 2007, pp. 1559–1565. ACM, New York (2007)Google Scholar
  9. 9.
    Burke, E.K., Hyde, M.R., Kendall, G.: Evolving bin packing heuristics with genetic programming. In Proceedings of the 9th International Conference on Parallel Problem Solving from Nature (PPSN 2006), Reykjavik, Iceland vol. 4193 of Lecture Notes in Computer Science, vol. 4193 pp. 860–869, Springer, Berlin September. (2006)Google Scholar
  10. 10.
    Burke, E.K., Hyde, M.R., Kendall, G., Woodward, J.: A genetic programming hyper-heuristic approach for evolving two dimensional strip packing heuristics. IEEE Trans. Evol. Comput. accepted, to appear (2010)Google Scholar
  11. 11.
    Burke, E.K., Hyde, M.R., Kendall, G., Woodward, J.R.: The scalability of evolved on line bin packing heuristics. In 2007 IEEE Congress on Evolutionary Computation, pp. 2530–2537. Singapore IEEE Computational Intelligence Society, IEEE Press (2007)Google Scholar
  12. 12.
    Burke, E.K., Kendall, G., Soubeiga, E.: A tabu-search hyperheuristic for timetabling and rostering. J. Heuristics 9(6), 451–470 (2003)CrossRefGoogle Scholar
  13. 13.
    Burke, E.K., McCollum, B., Meisels, A., Petrovic, S., Qu, R.: A graph-based hyper-heuristic for educational timetabling problems. Euro. J. Oper. Res. 176, 177–192 (2007)CrossRefGoogle Scholar
  14. 14.
    Burke, E.K., Petrovic, S., Qu, R.: Case based heuristic selection for timetabling problems. J. Scheduling 9(2), 115–132 (2006)CrossRefGoogle Scholar
  15. 15.
    Burke, E.K., Newall, J.: Solving examination timetabling problems through adaptation of heuristic orderings. Ann. Oper. Res. 129, 107–134 (2004)CrossRefGoogle Scholar
  16. 16.
    Chakhlevitch, K., Cowling, P.I.: Hyperheuristics: recent developments. In: Cotta, C., Sevaux, M., Sörensen, K. eds. Adaptive and Multilevel Metaheuristics, Studies in Computational Intelligence, vol. 136, pp. 3–29. Springer, Berlin (2008)CrossRefGoogle Scholar
  17. 17.
    Cowling, P., Kendall, G., Han, L.: An investigation of a hyperheuristic genetic algorithm applied to a trainer scheduling problem. In: Proceedings of the Congress on Evolutionary Computation (CEC2002), pp. 1185–1190. Hilton Hawaiian Village Hotel, Honolulu, Hawaii, USA, 12–17 May (2002)Google Scholar
  18. 18.
    Cowling, P., Kendall, G., Soubeiga, E.: A hyperheuristic approach for scheduling a sales summit. In: Selected Papers of the Third International Conference on the Practice And Theory of Automated Timetabling, PATAT 2000, Konstanz, Germany, Lecture Notes in Computer Science, pp. 176–190. Springer August (2000)Google Scholar
  19. 19.
    Cowling, P., Kendall, G., Soubeiga, E.: Hyperheuristics: a tool for rapid prototyping in scheduling and optimisation. In: Cagoni, S., Gottlieb, J., Hart, E., Middendorf, M., Goenther, R., (eds.) Applications of Evolutionary Computing: Proceeding of Evo Workshops 2002, Kinsale, Ireland, Lecture Notes in Computer Science, vol. 2279, pp. 1–10. Springer April 3–4 (2002)Google Scholar
  20. 20.
    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, Carnegie-Mellon University, Pittsburgh, PA (1963)Google Scholar
  21. 21.
    Denzinger, J., Fuchs, M., Fuchs, M.: High performance ATP systems by combining several ai methods. In: Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI 97), pp. 102–107. Morgan Kaufmann, CA, USA (1997)Google Scholar
  22. 22.
    Dimopoulos, C., Zalzala, A.M.S.: Investigating the use of genetic programming for a classic one-machine scheduling problem. Advances in Engineering Software, 32(6), 489–498 (2001)CrossRefGoogle Scholar
  23. 23.
    Dowsland, K.A., Soubeiga, E., Burke, E.K.: A simulated annealing hyper-heuristic for determining shipper sizes. Euro. J. Oper. Res. 179(3), 759–774 (2007)CrossRefGoogle Scholar
  24. 24.
    Fang, H.L., Ross, P., Corne, D.: A promising genetic algorithm approach to job shop scheduling, rescheduling, and open-shop scheduling problems. In Forrest, S. (ed.) Fifth International Conference on Genetic Algorithms, San Mateo pp. 375–382. Morgan Kaufmann, CA, USA (1993).Google Scholar
  25. 25.
    Fang, H.L., Ross, P., Corne, D.: A promising hybrid GA/heuristic approach for open-shop scheduling problems. In: Cohn, A. (ed.) Eleventh European Conference on Artificial Intelligence. John Wiley & Sons, NJ, USA (1994)Google Scholar
  26. 26.
    Fisher, H., Thompson, G.L.: Probabilistic learning combinations of local job-shop scheduling rules. In: Factory Scheduling Conference, Carnegie Institue of Technology, May 10–12 (1961)Google Scholar
  27. 27.
    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
  28. 28.
    Fukunaga, A.: Automated discovery of composite SAT variable selection heuristics. In Proceedings of the National Conference on Artificial Intelligence (AAAI), pp. 641–648. Edmonton, Canada (2002)Google Scholar
  29. 29.
    Fukunaga, A.S.: Evolving local search heuristics for SAT using genetic programming. In: Genetic and Evolutionary Computation–-GECCO-2004, Part II, Lecture Notes in Computer Science, pp. 483–494. Springer, Edmonton, Canada (2004)Google Scholar
  30. 30.
    Fukunaga, A.S.: Automated discovery of local search heuristics for satisfiability testing. Evol. Comput. 16(1), 31–61 (2008)CrossRefGoogle Scholar
  31. 31.
    Geiger, C.D., Uzsoy, R., Aytŭg, H.: Rapid modeling and discovery of priority dispatching rules: an autonomous learning approach. J. Scheduling 9, 7–34 (2006)CrossRefGoogle Scholar
  32. 32.
    Gratch, J., Chien, S.: Adaptive problem-solving for large-scale scheduling problems: a case study. J. Artif. Intell. Res., 4, 365–396 (1996)Google Scholar
  33. 33.
    Hart, E., Ross, P., Nelson, J.A.D.: Solving a real-world problem using an evolving heuristically driven schedule builder. Evolu. Comput. 6(1), 61–80 (1998)CrossRefGoogle Scholar
  34. 34.
    Holland, J.H., Reitman, J.S.: Cognitive systems based on adaptive algorithms. In: Pattern-Directed Inference Systems. Academic, New York (1978)Google Scholar
  35. 35.
    Joslin, D., Clements, D.P. “squeaky wheel” optimization. J. Artif. Intell. Res. 10, 353–373 (1999)Google Scholar
  36. 36.
    Keller, R.E., Poli, R.: Cost-benefit investigation of a genetic-programming hyperheuristic. In: Proceedings of Artificial Evolution (EA’07), pp. 13–24. Tours, France (2007)Google Scholar
  37. 37.
    Keller, R.E., Poli, R.: Linear genetic programming of parsimonious metaheuristics. In: Proceedings of Congress on Evolutionary Computation (CEC 2007), Singapore (2007)Google Scholar
  38. 38.
    Kendall, G., Mohamad, M.: Channel assignment in cellular communication using a great deluge hyper-heuristic. In: Proceedings of the 2004 IEEE International Conference on Network (ICON2004), Singapore, 16–19 November. pp. 769–773 (2004)Google Scholar
  39. 39.
    Kibria, R.H., Li, Y.: Optimizing the initialization of dynamic decision heuristics in DPLL SAT solvers using genetic programming. In: Proceedings of the 9th European Conference on Genetic Programming, Lecture Notes in Computer Science, Budapest, Hungary, vol. 3905, pp. 331–340. Springer, Berlin (2006)Google Scholar
  40. 40.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, MA, USA (1992)Google Scholar
  41. 41.
    Marín-Blázquez, J.G., Schulenburg, S.: A hyper-heuristic framework with XCS: learning to create novel problem-solving algorithms constructed from simpler algorithmic ingredients. In IWLCS, Lecture Notes in Computer Science, vol. 4399 pp. 193–218. Springer, Berlin/Heidelberg (2005)Google Scholar
  42. 42.
    Mladenovic, N., Hansen, P.: Variable neighborhood search. Computers and Operations Research, 24(11), 1097–1100, (1997)CrossRefGoogle Scholar
  43. 43.
    Mockus, J., Mockus, L.: Bayesian approach to global optimization and applications to multi-objective constrained problems. Journal of Optimization Theory and Applications, 70(1), 155–171, July (1991)CrossRefGoogle Scholar
  44. 44.
    Nareyek. A.: Choosing search heuristics by non-stationary reinforcement learning. In Resende, M.G.C., de Sousa, J.P. eds. Metaheuristics: Computer Decision-Making, chapter 9, pp. 523–544. Kluwer, (2003)Google Scholar
  45. 45.
    Ochoa, G., Qu, R., Burke, E.K.: Analyzing the landscape of a graph based hyper-heuristic for timetabling problems. Proceedings of the ACM Genetic and Evolutionary Computation Conference (GECCO 2009), pp. 341–348. Montreal, Canada. (2009)Google Scholar
  46. 46.
    Ochoa, G., Váquez-Rodríguez, J.A., Petrovic, S., Burke, E.K., Dispatching rules for production scheduling: a hyper-heuristic landscape analysis. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2009), pp. 1873–1880. Trondheim, Norway (2009)Google Scholar
  47. 47.
    Oltean, M.: Evolving evolutionary algorithms using linear genetic programming. Evolu. Comput. 13(3), 387–410 (2005)CrossRefGoogle Scholar
  48. 48.
    Özcan, E., Bilgin, B., Korkmaz, E.E.: Hill climbers and mutational heuristics in hyperheuristics. Proceedings of the 9th International Conference on Parallel Problem Solving from Nature (PPSN 2006), Reykjavik, Iceland, September. Lecture Notes in Computer Science, vol. 4193, pp. 202–211. Springer, Heidelberg (2006)Google Scholar
  49. 49.
    Özcan, E., Bilgin, B., Korkmaz, E.E.: A comprehensive analysis of hyper-heuristics. Intell. Data Anal. 12(1), 3–23 (2008)Google Scholar
  50. 50.
    Pisinger, D., Ropke, S.: A general heuristic for vehicle routing problems. Comput. Oper. Res., 34, 2403–2435 (2007)CrossRefGoogle Scholar
  51. 51.
    Qu R., Burke, E.K.: Hybridisations within a graph based hyper-heuristic framework for university timetabling problems. J. Oper. Res. Soc. (2008) to appear, doi: 10.1057/jors. 2008.102Google Scholar
  52. 52.
    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 (2005)Google Scholar
  53. 53.
    Ross, P., Marín-Blázquez. J.G.: Constructive hyper-heuristics in class timetabling. In: IEEE Congress on Evolutionary Computation, Edinburgh, UK. pp. 1493–1500. IEEE, NJ, USA (2005)Google Scholar
  54. 54.
    Ross, P., Marin-Blazquez, J.G., Hart, E.: Hyper-heuristics applied to class and exam timetabling problems. In: Proceedings of the 2004 IEEE Congress on Evolutionary Computation, pp. 1691–1698 IEEE Press Portland, Oregon (2004)Google Scholar
  55. 55.
    Ross, P., Schulenburg, S., Marin-Blázquez, J.G., Hart, E.: Hyper-heuristics: learning to combine simple heuristics in bin-packing problem. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO’02. Morgan-Kauffman, CA, USA (2002)Google Scholar
  56. 56.
    Shaw, P.: Using constraint programming and local search methods to solve vehicle routing problems. In: Proceedings of International Conference on Principles and Practice of Constraint Programming (CP’98), Lecture Notes in Computer Science, vol. 1520, pp. 417–431. Springer (1998)Google Scholar
  57. 57.
    Soubeiga, E.: Development and Application of Hyperheuristics to Personnel Scheduling. Ph.D. thesis, School of Computer Science and Information Technology, University of Nottingham, June (2003)Google Scholar
  58. 58.
    Storer, R.H., Wu, S.D., Vaccari, R.: Problem and heuristic space search strategies for job shop scheduling. ORSA J. Comput., 7(4), 453–467 (1995)Google Scholar
  59. 59.
    Tay, J.C., Ho, N.B.: Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems. Comput Indust Eng 54, 453–473 (2008)CrossRefGoogle Scholar
  60. 60.
    Terashima-Marín, H., Flores-álvarez, 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.) Genetic and Evolutionary Computation Conference, GECCO 2005, Proceedings, Washington DC, USA, June 25–29, 2005, pp. 637–643. ACM, NY, USA (2005)CrossRefGoogle Scholar
  61. 61.
    Terashima-Marin, H., Moran-Saavedra, A., Ross, P.: Forming hyper-heuristics with GAs when solving 2D-regular cutting stock problems. In: Proceedings of the 2005 IEEE Congress on Evolutionary Computation, vol. 2, pp. 1104–1110, IEEE Press Edinburgh, Scotland, UK (2005)Google Scholar
  62. 62.
    Terashima-Marin, H., Ross, P., Valenzuela-Rendon, M.: Evolution of constraint satisfaction strategies in examination timetabling. In: Genetic and Evolutionary Computation Conference, GECCO’99, pp. 635–642 (1999)Google Scholar
  63. 63.
    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: Baptiste, P., Kendall, G. Munier, A. Sourd, F. (eds.) Proceedings of the 3rd Multidisciplinary International Scheduling Conference: Theory and Applications (MISTA 2007) (2007)Google Scholar
  64. 64.
    Wilson, S.W.: Classifier systems based on accuracy. Evolu. Comput., 3(2), 149–175 (1995)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Edmund K. Burke
    • 1
  • Matthew Hyde
    • 2
  • Graham Kendall
    • 2
  • Gabriela Ochoa
    • 2
  • Ender Özcan
    • 2
  • John R. Woodward
    • 2
  1. 1.Automated Scheduling, Optimisation and Planning (ASAP) Group, School of Computer ScienceUniversity of NottinghamNottinghamUK
  2. 2.The University of NottinghamNottinghamUK

Personalised recommendations