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Handbook of Heuristics pp 3–21Cite as

Adaptive and Multilevel Metaheuristics

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Abstract

For the last decades, metaheuristics have become ever more popular as a tool to solve a large class of difficult optimization problems. However, determining the best configuration of a metaheuristic, which includes the program flow and the parameter settings, remains a difficult task. Adaptive metaheuristics (that change their configuration during the search) and multilevel metaheuristics (that change their configuration during the search by means of a metaheuristic) can be a solution for this. This chapter intends to make a quick review of the latest trends in adaptive metaheuristics and in multilevel metaheuristics.

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References

  1. Adenso-Díaz B, Laguna M (2006) Fine-tuning of algorithms using fractional experimental designs and local search. Oper Res 54(1):99–114. https://doi.org/10.1287/opre.1050.0243

  2. Battiti R (1996) Reactive search: toward self-tuning heuristics. In: Modern heuristic search methods. Wiley, Chichester, pp 61–83

    Google Scholar 

  3. Birattari M (2009) Tuning metaheuristics. Springer, Berlin/Heidelberg. https://doi.org/10.1007/978-3-642-00483-4

  4. Bölte A, Thonemann UW (1996) Optimizing simulated annealing schedules with genetic programming. Eur J Oper Res 92(2):402–416. https://doi.org/10.1016/0377-2217(94)00350-5

  5. Boutillon E, Roland C, Sevaux M (2008) Probability-driven simulated annealing for optimizing digital FIR filters. In: Studies in computational intelligence. Springer Science & Business Media, pp 77–93. https://doi.org/10.1007/978-3-540-79438-7_4

  6. Burke EK, Kendall G, Newall J, Hart E, Ross P, Schulenburg S (2003) Hyper-heuristics: an emerging direction in modern search technology. In: Glover F, Kochenberger GA (eds) Handbook of metaheuristics. International series in operations research & management science, vol 57. Springer, pp 457–474. https://doi.org/10.1007/0-306-48056-5_16

  7. Burke EK, Hyde MR, Kendall G, Woodward J (2007) Automatic heuristic generation with genetic programming. In: Proceedings of the 9th annual conference on Genetic and evolutionary computation – GECCO’07. Association for Computing Machinery (ACM). https://doi.org/10.1145/1276958.1277273

  8. Burke EK, Hyde MR, Kendall G, Ochoa G, Ozcan E, Woodward JR (2009) Exploring hyper-heuristic methodologies with genetic programming. In: Intelligent systems reference library. Springer Science & Business Media, pp 177–201. https://doi.org/10.1007/978-3-642-01799-5_6.

  9. Burke EK, Gendreau M, Hyde MR, Kendall G, Ochoa G, Özcan E, Qu R (2013) Hyper-heuristics: a survey of the state of the art. J Oper Res Soc 64(12):1695–1724. https://doi.org/10.1057/jors.2013.71

  10. Dean A, Voss D (eds) (1999) Design and analysis of experiments. Springer, Berlin. https://doi.org/10.1007/b97673

  11. Delorme X, Gandibleux X, Rodriguez J (2004) GRASP for set packing problems. Eur J Oper Res 153(3):564–580. https://doi.org/10.1016/s0377-2217(03)00263-7

  12. Dioşan L, Oltean M (2006) Evolving crossover operators for function optimization. In: Genetic programming. Springer Science & Business Media, pp 97–108. https://doi.org/10.1007/11729976_9

  13. Dioşan L, Oltean M (2009) Evolutionary design of evolutionary algorithms. Genet Program Evolvable Mach 10(3):263–306. https://doi.org/10.1007/s10710-009-9081-6

  14. Dobslaw F (2010) A parameter tuning framework for metaheuristics based on design of experiments and artificial neural networks. In: Proceedings of the international conference on computer mathematics and natural computing 2010. WASET

    Google Scholar 

  15. Drake JH, Kililis N, Ozcan E (2013) Generation of VNS components with grammatical evolution for vehicle routing. In: Genetic programming. Springer Science & Business Media, pp 25–36. https://doi.org/10.1007/978-3-642-37207-0_3

  16. Eiben AE, Smit SK (2011) Evolutionary algorithm parameters and methods to tune them. In: Autonomous search. Springer, Berlin/Heidelberg, pp 15–36. https://doi.org/10.1007/978-3-642-21434-9_2

  17. Eiben AE, Hinterding R, Michalewicz Z (1999) Parameter control in evolutionary algorithms. IEEE Trans Evol Comput 3(2):124–141. https://doi.org/10.1109/4235.771166

  18. Hong L, Woodward J, Li J, Ozcan E (2013) Automated design of probability distributions as mutation operators for evolutionary programming using genetic programming. In: Proceedings of the 16th European conference on genetic programming – EuroGP 2013, vol 7831, pp 85–96

    Google Scholar 

  19. Hooker JN (1995) Testing heuristics: we have it all wrong. J Heuristics 1(1):33–42. https://doi.org/10.1007/bf02430364

  20. Løkketangen A, Olsson R (2009) Generating meta-heuristic optimization code using ADATE. J Heuristics 16(6):911–930. https://doi.org/10.1007/s10732-009-9119-1

  21. Lourenço N, Pereira FB, Costa E (2012) Evolving evolutionary algorithms. In: Proceedings of the fourteenth international conference on genetic and evolutionary computation conference companion – GECCO 2012. ACM Press. https://doi.org/10.1145/2330784.2330794

  22. Lourenço N, Pereira FB, Costa E (2013) The importance of the learning conditions in hyper-heuristics. In: Proceedings of the fifteenth annual conference on genetic and evolutionary computation conference – GECCO 2013. ACM Press. https://doi.org/10.1145/2463372.2463558

  23. Oltean M (2005) Evolving evolutionary algorithms using linear genetic programming. Evol Comput 13(3):387–410. https://doi.org/10.1162/1063656054794815

  24. Oltean M, Groşan C (2003) Evolving evolutionary algorithms using multi expression programming. In: Advances in artificial life. Springer Science & Business Media, pp 651–658. https://doi.org/10.1007/978-3-540-39432-7_70

  25. Prais M, Ribeiro CC (2000) Reactive GRASP: an application to a matrix decomposition problem in TDMA traffic assignment. INFORMS J Comput 12(3):164–176. https://doi.org/10.1287/ijoc.12.3.164.12639

  26. Prins C (2004) A simple and effective evolutionary algorithm for the vehicle routing problem. Comput Oper Res 31(12):1985–2002. https://doi.org/10.1016/s0305-0548(03)00158-8

  27. Qu R, Burke EK, McCollum B, Merlot LTG, Lee SY (2008) A survey of search methodologies and automated system development for examination timetabling. J Sched 12(1):55–89. https://doi.org/10.1007/s10951-008-0077-5

  28. Ross P (2005) Hyper-heuristics. In: Search methodologies. Springer Science & Business Media, pp 529–556. https://doi.org/10.1007/0-387-28356-0_17

  29. Sabar NR, Ayob M, Kendall G, Qu R (2013) Grammatical evolution hyper-heuristic for combinatorial optimization problems. IEEE Trans Evol Comput 17(6):840–861. https://doi.org/10.1109/tevc.2013.2281527

  30. Sevaux M, Thomin P (2001) Heuristics and metaheuristics for parallel machine scheduling: a computational evaluation. In: Proceedings of 4th metaheuristics international conference, MIC 2001, Porto, pp 411–415

    Google Scholar 

  31. Sörensen K, Sevaux M (2006) MA|PM: memetic algorithms with population management. Comput Oper Res 33(5):1214–1225. https://doi.org/10.1016/j.cor.2004.09.011

  32. Talbi E-G (2009) Metaheuristics: from design to implementation. Wiley & Sons, Hoboken. ISBN:978-0-470-27858-1

    Google Scholar 

  33. Tavares J, Pereira FB (2012) Automatic design of ant algorithms with grammatical evolution. In: Genetic programming. Springer Science & Business Media, pp 206–217. https://doi.org/10.1007/978-3-642-29139-5_18

  34. Van Breedam A (1995) Improvement heuristics for the vehicle routing problem based on simulated annealing. Eur J Oper Res 86(3):480–490. https://doi.org/10.1016/0377-2217(94)00064-J

    Article  Google Scholar 

  35. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82. https://doi.org/10.1109/4235.585893

    Article  Google Scholar 

  36. Woodward JR, Swan J (2011) Automatically designing selection heuristics. In: Proceedings of the 13th annual conference companion on genetic and evolutionary computation – GECCO 2011. ACM Press. https://doi.org/10.1145/2001858.2002052

  37. Woodward JR, Swan J (2012) The automatic generation of mutation operators for genetic algorithms. In: Proceedings of the fourteenth international conference on genetic and evolutionary computation conference companion – GECCO 2012. ACM Press. https://doi.org/10.1145/2330784.2330796

  38. Xu J, Kelly JP (1996) A network flow-based tabu search heuristic for the vehicle routing problem. Transp Sci 30(4):379–393. https://doi.org/10.1287/trsc.30.4.379

    Article  Google Scholar 

  39. Xu J, Chiu SY, Glover F (1998) Fine-tuning a tabu search algorithm with statistical tests. Int Trans Oper Res 5(3):233–244. https://doi.org/10.1111/j.1475-3995.1998.tb00117.x

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Université de Bretagne-Sud, Lab-STICC, CNRS, Lorient, France

    Marc Sevaux

  2. University of Antwerp, Antwerp, Belgium

    Kenneth Sörensen

  3. School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, Durban, South Africa

    Nelishia Pillay

Authors
  1. Marc Sevaux
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  2. Kenneth Sörensen
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  3. Nelishia Pillay
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Corresponding author

Correspondence to Marc Sevaux .

Editor information

Editors and Affiliations

  1. Statistics and Operations Research Department, University of Valencia, Valencia, Spain

    Rafael Martí

  2. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

  3. Amazon.com, Inc. and University of Washington, Seattle, Washington, USA

    Mauricio G. C. Resende

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Sevaux, M., Sörensen, K., Pillay, N. (2018). Adaptive and Multilevel Metaheuristics. In: Martí, R., Pardalos, P., Resende, M. (eds) Handbook of Heuristics. Springer, Cham. https://doi.org/10.1007/978-3-319-07124-4_16

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