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 Swiercz


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
  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