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Unified encoding for hyper-heuristics with application to bioinformatics

Author

Listed:
  • Aleksandra Swiercz
  • Edmund Burke
  • Mateusz Cichenski
  • Grzegorz Pawlak
  • Sanja Petrovic
  • Tomasz Zurkowski
  • Jacek Blazewicz

Abstract

This paper introduces a new approach to applying hyper-heuristic algorithms to solve combinatorial problems with less effort, taking into account the modelling and algorithm construction process. We propose a unified encoding of a solution and a set of low level heuristics which are domain-independent and which change the solution itself. This approach enables us to address NP-hard problems and generate good approximate solutions in a reasonable time without a large amount of additional work required to tailor search methodologies for the problem in hand. In particular, we focused on solving DNA sequencing by hybrydization with errors, which is known to be strongly NP-hard. The approach was extensively tested by solving multiple instances of well-known combinatorial problems and compared with results generated by meta heuristics that have been tailored for specific problem domains. Copyright The Author(s) 2014

Suggested Citation

  • 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.
    • Handle: RePEc:spr:cejnor:v:22:y:2014:i:3:p:567-589
    DOI: 10.1007/s10100-013-0321-8
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    References listed on IDEAS

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    1. 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.
    2. Edmund K Burke & Michel Gendreau & Matthew Hyde & Graham Kendall & Gabriela Ochoa & Ender Özcan & Rong Qu, 2013. "Hyper-heuristics: a survey of the state of the art," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(12), pages 1695-1724, December.
    3. Burke, Edmund K. & McCollum, Barry & Meisels, Amnon & Petrovic, Sanja & Qu, Rong, 2007. "A graph-based hyper-heuristic for educational timetabling problems," European Journal of Operational Research, Elsevier, vol. 176(1), pages 177-192, January.
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    Cited by:

    1. 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.
    2. Hermann Maurer & Rizwan Mehmood, 2015. "Merging image databases as an example for information integration," 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. 23(2), pages 441-458, June.

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