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A hyper-heuristic approach to sequencing by hybridization of DNA sequences

Author

Listed:
  • Jacek Blazewicz
  • Edmund Burke
  • Graham Kendall
  • Wojciech Mruczkiewicz
  • Ceyda Oguz
  • Aleksandra Swiercz

Abstract

In this paper we investigate the use of hyper-heuristic methodologies for predicting DNA sequences. In particular, we utilize Sequencing by Hybridization. We believe that this is the first time that hyper-heuristics have been investigated in this domain. A hyper-heuristic is provided with a set of low-level heuristics and the aim is to decide which heuristic to call at each decision point. We investigate three types of hyper-heuristics. Two of these (simulated annealing and tabu search) draw their inspiration from meta-heuristics. The choice function hyper-heuristic draws its inspiration from reinforcement learning. We utilize two independent sets of low-level heuristics. The first set is based on a previous tabu search method, with the second set being a significant extension to this basic set, including utilizing a different representation and introducing the definition of clusters. The datasets we use comprises two randomly generated datasets and also a publicly available biological dataset. In total, we carried out experiments using 70 different combinations of heuristics, using the three datasets mentioned above and investigating six different hyper-heuristic algorithms. Our results demonstrate the effectiveness of a hyper-heuristic approach to this problem domain. It is necessary to provide a good set of low-level heuristics, which are able to both intensify and diversify the search but this approach has demonstrated very encouraging results on this extremely difficult and important problem domain. Copyright Springer Science+Business Media, LLC 2013

Suggested Citation

  • Jacek Blazewicz & Edmund Burke & Graham Kendall & Wojciech Mruczkiewicz & Ceyda Oguz & Aleksandra Swiercz, 2013. "A hyper-heuristic approach to sequencing by hybridization of DNA sequences," Annals of Operations Research, Springer, vol. 207(1), pages 27-41, August.
    • Handle: RePEc:spr:annopr:v:207:y:2013:i:1:p:27-41:10.1007/s10479-011-0927-y
    DOI: 10.1007/s10479-011-0927-y
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    References listed on IDEAS

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    1. Jacek Błażewicz & Fred Glover & Marta Kasprzak, 2004. "DNA Sequencing—Tabu and Scatter Search Combined," INFORMS Journal on Computing, INFORMS, vol. 16(3), pages 232-240, August.
    2. Blazewicz, J. & Formanowicz, P. & Kasprzak, M. & Markiewicz, W. T. & Weglarz, J., 2000. "Tabu search for DNA sequencing with false negatives and false positives," European Journal of Operational Research, Elsevier, vol. 125(2), pages 257-265, September.
    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. Blazewicz, Jacek & Kasprzak, Marta & Kierzynka, Michal & Frohmberg, Wojciech & Swiercz, Aleksandra & Wojciechowski, Pawel & Zurkowski, Piotr, 2018. "Graph algorithms for DNA sequencing – origins, current models and the future," European Journal of Operational Research, Elsevier, vol. 264(3), pages 799-812.
    2. 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.
    3. 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.
    4. Olacir R. Castro & Gian Mauricio Fritsche & Aurora Pozo, 2018. "Evaluating selection methods on hyper-heuristic multi-objective particle swarm optimization," Journal of Heuristics, Springer, vol. 24(4), pages 581-616, August.

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