Annals of Operations Research

, Volume 207, Issue 1, pp 27–41 | Cite as

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

  • Jacek Blazewicz
  • Edmund K. Burke
  • Graham Kendall
  • Wojciech Mruczkiewicz
  • Ceyda Oguz
  • Aleksandra SwierczEmail author


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.


Hyper-heuristics Simulated annealing Tabu search Choice function Sequencing by hybridization 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Aarts, E., Korst, J., & Michiels, W. (2005). Simulated annealing. In E. K. Burke & G. Kendall (Eds.), Search methodologies: introductory tutorials in optimization and decision support techniques (pp. 187–210). Berlin: Springer. Chap. 7. Google Scholar
  2. Ayob, M., & Kendall, G. (2003). A Monte Carlo hyper-heuristic to optimise component placement sequencing for multi head placement machine. In Placement machine, InTech’03 Thailand (pp. 132–141). Google Scholar
  3. Bai, R., & Kendall, G. (2005). An investigation of automated planograms using a simulated annealing based hyper-heuristics. In T. Ibaraki, K. Nonobe, & M. Yagiura (Eds.), Metaheuristics: progress as real problem solvers operations research/computer science interfaces series (Vol. 32, pp. 87–108). Berlin: Springer. CrossRefGoogle Scholar
  4. Bai, R., Blazewicz, J., Burke, E. K., Kendall, G., & Mccollum, B. (2007). A simulated annealing hyper-heuristic methodology for flexible decision support (Tech. rep.). School of CSiT. University of Nottingham, UK. Google Scholar
  5. Blazewicz, J., & Kasprzak, M. (2003). Complexity of DNA sequencing by hybridization. Theoretical Computer Science, 290(3), 1459–1473. CrossRefGoogle Scholar
  6. Blazewicz, J., Formanowicz, P., Kasprzak, M., Markiewicz, W., & Weglarz, J. (2000). Tabu search for DNA sequencing with false negative and false positives. European Journal of Operational Research, 125, 257–265. CrossRefGoogle Scholar
  7. Blazewicz, J., Formanowicz, P., Guinand, F., & Kasprzak, M. (2002a). A heuristic managing errors for DNA sequencing. Bioinformatics, 18, 652–660. CrossRefGoogle Scholar
  8. Blazewicz, J., Kasprzak, M., & Kuroczycki, W. (2002b). Hybrid genetic algorithm for DNA sequencing with errors. Journal of Heuristics, 8, 495–502. CrossRefGoogle Scholar
  9. Blazewicz, J., Glover, F., & Kasprzak, M. (2004). DNA sequencing—tabu and scatter search combined. INFORMS Journal on Computing, 16, 232–240. CrossRefGoogle Scholar
  10. Blazewicz, J., Glover, F., Swiercz, A., Kasprzak, M., Markiewicz, W., Oguz, C., & Rebholz-Schuhmann, D. (2006). Dealing with repetitions in sequencing by hybridization. Computational Biology and Chemistry, 30(5), 313–320. CrossRefGoogle Scholar
  11. Bui, T., & Youssef, W. (2004). An enhanced genetic algorithm for DNA sequencing by hybridization with positive and negative errors. Lecture Notes in Computer Science, 3103, 908–919. CrossRefGoogle Scholar
  12. Burke, E. K., & Kendall, G. (Eds.) (2005). Search methodologies: introductory tutorials in optimization and decision support techniques. Berlin: Springer. Google Scholar
  13. Burke, E. K., & Soubeiga, E. (2003). Scheduling nurses using a tabu-search hyperheuristic. In Proceedings of the 1st multidisciplinary international conference on scheduling: theory and applications (MISTA 2003), 197–218. Google Scholar
  14. Burke, E., Kendall, G., Newall, J., Hart, E., Ross, P., & Schulenburg, S. (2003a) Hyper-heuristics: An emerging direction in modern search technology. In Handbook of metaheuristics. Dordrecht: Kluwer Academic. Chap. 16. Google Scholar
  15. Burke, E. K., Kendall, G., & Soubeiga, E. (2003b). A tabu-search hyperheuristic for timetabling and rostering. Journal of Heuristics, 9(6), 451–470. CrossRefGoogle Scholar
  16. Burke, E. K., McCollum, B., Meisels, A., Petrovic, S., & Qu, R. (2007). A graph-based hyper-heuristic for timetabling problems. European Journal of Operational Research, 176(1), 177–192. CrossRefGoogle Scholar
  17. Cowling, P., Kendall, G., & Soubeiga, E. (2001). A hyperheuristic approach to scheduling a sales summit. In PATAT ’00: Selected papers from the third international conference on practice and theory of automated timetabling III (pp. 176–190). London: Springer. CrossRefGoogle Scholar
  18. Cowling, P., Kendall, G., & Soubeiga, E. (2002). Hyperheuristics: A tool for rapid prototyping in scheduling and optimisation. In: Lecture notes in computer science, EvoWorkShops, pp. 1–10. Berlin: Springer. Google Scholar
  19. Dowsland, K., Soubeiga, E. & Burke, E.K (2007). A simulated annealing hyper-heuristic for determining shipper sizes. European Journal of Operational Research 179(3), 759–774. CrossRefGoogle Scholar
  20. Dramanac, R., Labat, I., Brukner, I., & Crkvenjakov, R. (1989). Sequencing of megabase plus DNA by hybridization: Theory of the method. Genomics, 4(2), 114–128. CrossRefGoogle Scholar
  21. Gendreau, M., & Potvin, J. Y. (2005). Tabu search. In: E. K. Burke & G. Kendall (Eds.), Search methodologies: introductory tutorials in optimization and decision support techniques (pp. 165–186). Berlin: Springer. Chap. 6. Google Scholar
  22. Glover, F., & Laguna, M. (1997). Tabu search. Boston: Kluwer Academic. CrossRefGoogle Scholar
  23. Han, L., & Kendall, G. (2003). Investigation of a tabu assisted hyper-heuristic genetic algorithm. In Proceedings of congress on evolutionary computation (CEC2003) (Vol. 3, pp. 2230–2237). Google Scholar
  24. Kendall, G., & Hussin, M. (2005a). A tabu search hyper-heuristic approach to the examination timetabling problem at the MARA University of Technology. Lectures Notes in Computer Science, 3616, 270–293. CrossRefGoogle Scholar
  25. Kendall, G., & Hussin, N. M. (2005b). In G. Kendall, E. Burke, S. Petrovic, & M. Gendreau (Eds.), An investigation of a tabu-search-based hyper-heuristic for examination timetabling, multidisciplinary scheduling; theory and applications (pp. 309–328). Berlin: Springer. CrossRefGoogle Scholar
  26. Kendall, G., Soubeiga, E., & Cowling, P. (2002). Choice function and random hyperheuristics. In Proceedings of the 4th Asia-Pacific conference on simulated evolution and learning (SEAL’02) (pp. 667–671). Google Scholar
  27. Kirkpatrick, S., Gelatt, C.D., & Vecchi, M.P. (1983). Optimization by simulated annealing. Science, 220, 671–680. CrossRefGoogle Scholar
  28. Lysov, I.P., Florent’ev, V.L., Khorlin, A.A., Khrapko, K.R., & Shik, V.V. (1988). Determination of the nucleotide sequence of DNA using hybridization with oligonucleotides. A new method. Doklady Akademii Nauk SSSR, 303, 1508–1511. Google Scholar
  29. Mruczkiewicz, W. (2009). Hyper-heuristics for sequencing by hybridisation problem. Master Thesis, Poznan University of Technology, Poland. Google Scholar
  30. Needleman, S. B., Wunsch, C.D. (1970). A general method applicable to the search for similarities of the amino acid sequence of two proteins. Journal of Molecular Biology 48, 443–453. CrossRefGoogle Scholar
  31. Pevzner, P. A. (1989). 1-tuple DNA sequencing: computer analysis. Journal of Biomolecular Structure and Dynamics, 7, 63–73. Google Scholar
  32. Ross, P. (2005). Hyper-heuristics. In E. K. Burke & G. Kendall (Eds.), Search methodologies: introductory tutorials in optimization and decision support techniques (pp. 529–556). Berlin: Springer. Chap. 17. Google Scholar
  33. Ross, P., Marin-Blázquez, J. G., Schulenburg, S., & Hart, E. (2003). Learning a procedure that can solve hard bin-packing problems: A new GA-based approach to hyper-heuristics. In Proceedings of the genetic and evolutionary computation conference (pp. 1295–1306). Berlin: Springer. Google Scholar
  34. Southern, E. (1988). United Kingdom Patent Application GB8810400. Google Scholar
  35. Zhang, J. H., LY, Wu, & Zhang, X. S. (2003). Reconstruction of DNA sequencing by hybridization. Bioinformatics, 19(1), 14–21. CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Jacek Blazewicz
    • 1
    • 2
  • Edmund K. Burke
    • 3
  • Graham Kendall
    • 3
  • Wojciech Mruczkiewicz
    • 1
  • Ceyda Oguz
    • 4
  • Aleksandra Swiercz
    • 1
    • 2
    Email author
  1. 1.Institute of Computing SciencePoznan University of TechnologyPoznanPoland
  2. 2.Institute of Bioorganic ChemistryPolish Academy of SciencePoznanPoland
  3. 3.School of Computer ScienceUniversity of NottinghamNottinghamUK
  4. 4.Department of Industrial EngineeringKoç UniversityIstanbulTurkey

Personalised recommendations