• Peter Ross


Many practical problems are awkward to solve computationally. Whether you are trying to find any solution at all, or perhaps to find a solution that is optimal or close to optimal according to some criteria, exact methods can be unfeasibly expensive. In such cases it is common to resort to heuristic methods, which are typically derived from experience but are inexact or incomplete. For example, in packing and cutting problems a very simple heuristic might be to try to pack the items in some standardized way starting with the largest remaining one first, on the reasonable grounds that the big ones tend to cause the most trouble. But such heuristics can easily lead to suboptimal answers.


  1. Allen S, Burke EK, Hyde M, Kendall G (2009) Evolving reusable 3D packing heuristics with genetic programming. In: Rothlauf F (ed) Proceedings of the GECCO 2009, Montreal. ACM, New York, pp 931–938Google Scholar
  2. ASAP research group (2012)
  3. Bader-El-Den MB, Poli R (2008) Generating SAT local-search heuristics using a GP framework. In: Monmarche N et al (eds) Artificial Evolution: Proceedings of the 8th International Conference EA 2007, Tours, France. Springer LNCS 4926, 37–49, 2008Google Scholar
  4. Bettinger P, Sessions J, Boston K (2009) A review of the status and use of validation procedures for heuristics used in forest planning. Int J Math Comput Forest Nat Resour Sci 1:26–37. Google Scholar
  5. Biazzini M, Bánhelyi B, Montresor A, Jelasity M (2009) Distributed hyper-heuristics for real parameter optimization. In: Rothlauf F (ed) Proceedings of the GECCO 2009, Montreal. ACM, New York, pp 1339–1346Google Scholar
  6. Bittle S, Fox M (2009) Learning and using hyper-heuristics for variable and value ordering in constraint satisfaction problems. In: Rothlauf F (ed) Proceedings of the GECCO 2009, Montreal. ACM, New York, pp 2209–2212Google Scholar
  7. Bräysy O (2003) A reactive variable neighborhood search for the vehicle routing problem with time windows. INFORMS J Comput 15:347–368CrossRefGoogle Scholar
  8. Burke E, Hart E, Kendall G, Newall J, Ross P, Schulenburg S (2003) Hyper-heuristics: an emerging direction in modern search technology. In: Glover F, Kochenberger G (eds) Handbook of meta-heuristics. Kluwer, Dordrecht, pp 457–474Google Scholar
  9. Burke EK, Hyde M, Kendall G, Woodward J (2007a) Automatic heuristic generation with genetic programming: evolving a jack-of-all-trades or a master of one. In: Proceedings of the GECCO 2007, London. ACM, New York, pp 1559–1565Google Scholar
  10. Burke EK, McCollum B, Meisels A, Petrovic S, Qu R (2007b) A graph-based hyper-heuristic for educational timetabling problems. Eur J Oper Res 176:177–192CrossRefGoogle Scholar
  11. Burke EK, Hyde M, Kendall G, Ochoa G, Ozcan E, Woodward J (2009a) A classification of hyper-heuristics approaches. In: Gendreau M, Potvin J-Y (eds) Handbook of metaheuristics, 2nd edn. Springer, Berlin, pp 449–468Google Scholar
  12. Burke EK, Hyde MR, Kendall G, Ochoa G, Ozcan E, Woodward JR (2009b) Exploring hyper-heuristic methodologies with genetic programming. In: Mumford CL, Jain LC (eds) Computational intelligence: collaboration, fusion and emergence. Springer, Berlin, pp 177–201CrossRefGoogle Scholar
  13. Castle T, Johnson CG (2010) Positional effect of crossover and mutation in grammatical evolution. In: Esparcia-Alcazar AI et al (eds) Proceedings of the EuroGP 2010, Istanbul. LNCS 6021. Springer, Berlin, pp 26–37Google Scholar
  14. Chakhlevitch K, Cowling PI (2008) Hyperheuristics: recent developments. In: Cotta C, Sevaux M, Sörensen K (eds) Adaptive and multilevel metaheuristics. Studies in computational intelligence, 136. Springer, Berlin, pp 3–29Google Scholar
  15. Coffman EG, Garey MR, Johnson DS (1996) Approximation algorithms for bin packing: a survey. In: Hochbaum D (ed) Approximation algorithms for NP-hard problems. PWS, Boston, pp 46–93Google Scholar
  16. Cowling PI, Chakhlevitch K (2007) Using a large set of low-level heuristics in a hyperheuristic approach to personnel scheduling. In: Dahal KP, Tan KC, Cowling PI (eds) Evolutionary scheduling. Studies in computational intelligence, 49. Springer, Berlin, pp 543–576Google Scholar
  17. Cowling P, Kendall G, Soubeiga E (2001) A hyperheuristic approach for scheduling a sales summit. In: PATAT 2000, Konstanz. LNCS 2079. Springer, Berlin, pp 176–190Google Scholar
  18. Downey RG, Fellows MR (1999) Parameterized complexity. Springer, New YorkCrossRefGoogle Scholar
  19. Dyckhoff H (1990) A topology of cutting and packing problems. Eur J Oper Res 44:145–159CrossRefGoogle Scholar
  20. Epstein SL, Freuder EC, Wallace RJ, Morozov A, Samuels B (2002) The adaptive constraint engine. In: Van Hentenryck P (ed) Principles and Practice of Constraint Programming – CP 2002, Ithaca. LNCS 2470. Springer, Berlin, pp 525–540Google Scholar
  21. ESA (2012) Global trajectory optimisation problems. C++ and Matlab code available.
  22. Frequency assignment problems (2012)
  23. Fukunaga A (2002) Automated discovery of composite SAT variable-selection heuristics. In: Proceedings of the AAAI 2002, AAAI Press, Edmonton, pp 641–648Google Scholar
  24. Fukunaga AS (2008) Automated discovery of local search heuristics for satisfiability testing. Evol Comput 16:31–61CrossRefGoogle Scholar
  25. Garrido P, Castro C (2009) Stable solving of CVRPs using hyperheuristics. In: Proceedings of the GECCO 2009, Montreal, pp 255–262Google Scholar
  26. Glover F, Kochenberger G (eds) (2003) Handbook of meta-heuristics. Kluwer, DordrechtGoogle Scholar
  27. Goldberg DE, Deb K, Kargupta H, Harik G (1989) Messy genetic algorithms: motivation, analysis and first results. Complex Syst 3:493–530Google Scholar
  28. Gratch J, Chein S, de Jong G (1993) Learning search control knowledge for deep space network scheduling. In: Proceedings of 10th international conference on machine learning, Amherst, pp 135–142Google Scholar
  29. Hauptman A, Sipper M (2005) GP-endchess: using genetic programming to evolve chess endgame players. In: Keijzer M et al (eds) Proceedings of the 8th EuroGP, Lausanne. LNCS 3447. Springer, Berlin, pp 120–131Google Scholar
  30. Johnson DS (1973) Near-optimal bin-packing algorithms. PhD thesis, MIT Department of MathematicsGoogle Scholar
  31. Kendall G (2012) A bibliography of hyper-heuristics and related approaches.
  32. Kubale M (ed) (2004) Graph colorings. AMS, ProvidenceGoogle Scholar
  33. León C, Miranda G, Segura C (2009) A memetic algorithm and a parallel hyperheuristic island-based model for a 2D packing problem. In: Rothlauf F (ed) Proceedings of the GECCO 2009, Montreal. ACM, New York, pp 1371–1378Google Scholar
  34. Marin-Blazquez JG, Schulenburg S (2007) A hyper-heuristic framework with XCS: learning to create novel problem-solving algorithms constructed from simpler algorithmic ingredients. In: Learning classifier systems. LNCS 4399. Springer, Berlin, pp 193–218Google Scholar
  35. Marques-Silva J (2008) Practical applications of Boolean satisfiability. In: Workshop on discrete event systems, Gothenberg, pp 74–80Google Scholar
  36. Martello S, Toth P (1990) Knapsack problems. Algorithms and computer implementations. Wiley, New YorkGoogle Scholar
  37. Neth H et al. (2009) Analysis of human search strategies. Technical report, Large Knowledge Collider Consortium. Deliverable 4.2.2.
  38. Niedermeier R (2006) Invitation to fixed-parameter algorithms. Oxford lecture series in mathematics and its applications, 31. Oxford University Press, Oxford/New YorkGoogle Scholar
  39. Ochoa G, Qu R, Burke EK (2009) Analyzing the landscape of a graph based hyper-heuristic for timetabling problems. In: Proceedings of the GECCO 2009, Montreal. ACM, New York, pp 341–348Google Scholar
  40. Ochoa G, Hyde M, Curtois T, Vazquez-Rodriguez JA, Walker J, Gendreau M, Kendall G, McCollum B, Parkes AJ, Petrovic S, Burke EK (2012) HyFlex: A benchmark framework for cross-domain heuristic search. In: Hao J-K, Middendorf M (eds), European conference on evolutionary computation in combinatorial optimisation EvoCOP 2012. LNCS 7245. Springer, Berlin, pp 136–147Google Scholar
  41. O’Neill M, Ryan C (2003) Grammatical evolution: evolutionary automatic programming in an arbitrary language. Springer, BerlinGoogle Scholar
  42. O’Neill M (2012) The grammatical evolution page.
  43. Poli R, Graff M (2009) There is a free lunch for hyper-heuristics, genetic programming, and computer scientists. In: Vanneschi L et al (eds) Genetic programming. Proceedings of the 12th EuroGP, Tübingen. LNCS 5481. Springer, Berlin, pp 195–207Google Scholar
  44. Poli R, Langdon WB, McPhee N (2008) A field guide to genetic programming. Lulu, Raleigh. Also available as a free PDF from
  45. Qu R, Burke EK (2009) Hybridisations within a graph based hyper-heuristic framework for university timetabling problems. J Oper Res Soc 60:1273–1285CrossRefGoogle Scholar
  46. Ross P, Schulenburg S, Marín-Blázquez JG, Hart E (2002) Hyper-heuristics: learning to combine simple heuristics in bin packing problems. In: Langdon WB et al. (eds) Proceedings of the GECCO 2002, New York. Morgan Kaufman, San Mateo, pp 942–948Google Scholar
  47. Ross P, Marín-Blázquez JG, Schulenburg S, Hart E (2003) Learning a procedure that can solve hard bin-packing problems: a new GA-based approach to hyper-heuristics. In: Cantú-Paz E et al (eds) Proceedings of the GECCO 2003, Chicago. LNCS 2724. Springer, Berlin, pp 1295–1306Google Scholar
  48. Satlib—the satisfiability library (2012)
  49. Schumacher C, Vose MD, Whitley LD (2001) The no free lunch and problem description length. In: Proceedings of the GECCO 2001, San Francisco. Morgan Kaufman, San Mateo, pp 565–570Google Scholar
  50. The ACT-R home page (2012)
  51. The SOAR home page (2012)
  52. Terashima-Marín H, Ortiz-Bayliss JC, Ross P, Valenzuela-Rendón M (2008) Using hyper-heuristics for the dynamic variable ordering in hard constraint satisfaction problems. In: Proceedings of the MICAI 2008, Atizapán de Zaragoza. LNCS 5317. Springer, Berlin, pp 407–417Google Scholar
  53. Terashima-Marín H, Ross P, Farías-Zárate CJ, López-Camacho E, Valenzuela-Rendón M (2010) Generalized hyper-heuristics for solving 2D regular and irregular packing problems. Ann Oper Res 179:369–392CrossRefGoogle Scholar
  54. University of Melbourne (2012) Exam timetabling data
  55. Wah BW, Ieumwananonthachai A, Chu LC, Aizawa A (1995) Genetics-based learning of new heuristics: rational scheduling of experiments and generalization. IEEE Trans Knowl Data Eng 7:763–785CrossRefGoogle Scholar
  56. Weihe K (1998) Covering trains by stations or the power of data reduction. In: Battiti R, Bertossi AA (eds) Proceedings of the ALEX 1998, pp 1–8.
  57. Wilson SW (1995) Classifier systems based on accuracy. Evol Comput 3:149–175CrossRefGoogle Scholar
  58. Wolpert D, MacReady WG (1995) No free lunch theorems for search. Technical report SFI-TR-92-02-010, Santa Fe InstituteGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.School of ComputingEdinburgh Napier UniversityEdinburghUK

Personalised recommendations