IDEAS home Printed from https://ideas.repec.org/h/spr/isochp/978-1-4419-1665-5_15.html
   My bibliography  Save this book chapter

A Classification of Hyper-heuristic Approaches

In: Handbook of Metaheuristics

Author

Listed:
  • Edmund K. Burke

    (University of Nottingham)

  • Matthew Hyde

    (The University of Nottingham)

  • Graham Kendall

    (The University of Nottingham)

  • Gabriela Ochoa

    (The University of Nottingham)

  • Ender Özcan

    (The University of Nottingham)

  • John R. Woodward

    (The University of Nottingham)

Abstract

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.

Suggested Citation

  • Edmund K. Burke & Matthew Hyde & Graham Kendall & Gabriela Ochoa & Ender Özcan & John R. Woodward, 2010. "A Classification of Hyper-heuristic Approaches," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, chapter 0, pages 449-468, Springer.
    • Handle: RePEc:spr:isochp:978-1-4419-1665-5_15
    DOI: 10.1007/978-1-4419-1665-5_15
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ahmed, Leena & Mumford, Christine & Kheiri, Ahmed, 2019. "Solving urban transit route design problem using selection hyper-heuristics," European Journal of Operational Research, Elsevier, vol. 274(2), pages 545-559.
    2. Marko Ɖurasević & Domagoj Jakobović, 2019. "Creating dispatching rules by simple ensemble combination," Journal of Heuristics, Springer, vol. 25(6), pages 959-1013, December.
    3. Gahm, Christian & Uzunoglu, Aykut & Wahl, Stefan & Ganschinietz, Chantal & Tuma, Axel, 2022. "Applying machine learning for the anticipation of complex nesting solutions in hierarchical production planning," European Journal of Operational Research, Elsevier, vol. 296(3), pages 819-836.
    4. Franck Butelle & Laurent Alfandari & Camille Coti & Lucian Finta & Lucas Létocart & Gérard Plateau & Frédéric Roupin & Antoine Rozenknop & Roberto Wolfler Calvo, 2016. "Fast machine reassignment," Annals of Operations Research, Springer, vol. 242(1), pages 133-160, July.
    5. Vidal, Thibaut & Crainic, Teodor Gabriel & Gendreau, Michel & Prins, Christian, 2014. "A unified solution framework for multi-attribute vehicle routing problems," European Journal of Operational Research, Elsevier, vol. 234(3), pages 658-673.
    6. Carlos Contreras Bolton & Gustavo Gatica & Víctor Parada, 2013. "Automatically Generated Algorithms for the Vertex Coloring Problem," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-9, March.
    7. Drake, John H. & Kheiri, Ahmed & Özcan, Ender & Burke, Edmund K., 2020. "Recent advances in selection hyper-heuristics," European Journal of Operational Research, Elsevier, vol. 285(2), pages 405-428.
    8. Lale Özbakır & Gökhan Seçme, 2022. "A hyper-heuristic approach for stochastic parallel assembly line balancing problems with equipment costs," Operational Research, Springer, vol. 22(1), pages 577-614, March.
    9. Johnes, Jill, 2015. "Operational Research in education," European Journal of Operational Research, Elsevier, vol. 243(3), pages 683-696.
    10. Qiuping Ni & Yuanxiang Tang, 2023. "A Bibliometric Visualized Analysis and Classification of Vehicle Routing Problem Research," Sustainability, MDPI, vol. 15(9), pages 1-37, April.
    11. Aleksandra Swiercz & Wojciech Frohmberg & Michal Kierzynka & Pawel Wojciechowski & Piotr Zurkowski & Jan Badura & Artur Laskowski & Marta Kasprzak & Jacek Blazewicz, 2018. "GRASShopPER—An algorithm for de novo assembly based on GPU alignments," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-23, August.
    12. Longlong Leng & Yanwei Zhao & Zheng Wang & Jingling Zhang & Wanliang Wang & Chunmiao Zhang, 2019. "A Novel Hyper-Heuristic for the Biobjective Regional Low-Carbon Location-Routing Problem with Multiple Constraints," Sustainability, MDPI, vol. 11(6), pages 1-31, March.
    13. Lagos, Felipe & Pereira, Jordi, 2024. "Multi-armed bandit-based hyper-heuristics for combinatorial optimization problems," European Journal of Operational Research, Elsevier, vol. 312(1), pages 70-91.
    14. Gianfranco Chicco & Andrea Mazza, 2020. "Metaheuristic Optimization of Power and Energy Systems: Underlying Principles and Main Issues of the ‘Rush to Heuristics’," Energies, MDPI, vol. 13(19), pages 1-38, September.
    15. Kheiri, Ahmed & Özcan, Ender, 2016. "An iterated multi-stage selection hyper-heuristic," European Journal of Operational Research, Elsevier, vol. 250(1), pages 77-90.
    16. Kassem Danach & Shahin Gelareh & Rahimeh Neamatian Monemi, 2019. "The capacitated single-allocation p-hub location routing problem: a Lagrangian relaxation and a hyper-heuristic approach," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 8(5), pages 597-631, December.
    17. Yanwei Zhao & Longlong Leng & Chunmiao Zhang, 2021. "A novel framework of hyper-heuristic approach and its application in location-routing problem with simultaneous pickup and delivery," Operational Research, Springer, vol. 21(2), pages 1299-1332, June.
    18. Fabio Caraffini & Giovanni Iacca, 2020. "The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms," Mathematics, MDPI, vol. 8(5), pages 1-31, May.
    19. Chen, Yujie & Cowling, Peter & Polack, Fiona & Remde, Stephen & Mourdjis, Philip, 2017. "Dynamic optimisation of preventative and corrective maintenance schedules for a large scale urban drainage system," European Journal of Operational Research, Elsevier, vol. 257(2), pages 494-510.
    20. Kallestad, Jakob & Hasibi, Ramin & Hemmati, Ahmad & Sörensen, Kenneth, 2023. "A general deep reinforcement learning hyperheuristic framework for solving combinatorial optimization problems," European Journal of Operational Research, Elsevier, vol. 309(1), pages 446-468.
    21. Pickardt, Christoph W. & Hildebrandt, Torsten & Branke, Jürgen & Heger, Jens & Scholz-Reiter, Bernd, 2013. "Evolutionary generation of dispatching rule sets for complex dynamic scheduling problems," International Journal of Production Economics, Elsevier, vol. 145(1), pages 67-77.
    22. Zhang, Yuchang & Bai, Ruibin & Qu, Rong & Tu, Chaofan & Jin, Jiahuan, 2022. "A deep reinforcement learning based hyper-heuristic for combinatorial optimisation with uncertainties," European Journal of Operational Research, Elsevier, vol. 300(2), pages 418-427.
    23. Iain Dunning & Swati Gupta & John Silberholz, 2018. "What Works Best When? A Systematic Evaluation of Heuristics for Max-Cut and QUBO," INFORMS Journal on Computing, INFORMS, vol. 30(3), pages 608-624, August.
    24. Vidal, Thibaut & Crainic, Teodor Gabriel & Gendreau, Michel & Prins, Christian, 2013. "Heuristics for multi-attribute vehicle routing problems: A survey and synthesis," European Journal of Operational Research, Elsevier, vol. 231(1), pages 1-21.
    25. Aleksandra Swiercz & Edmund Burke & Mateusz Cichenski & Grzegorz Pawlak & Sanja Petrovic & Tomasz Zurkowski & Jacek Blazewicz, 2014. "Unified encoding for hyper-heuristics with application to bioinformatics," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 22(3), pages 567-589, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:isochp:978-1-4419-1665-5_15. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.