Skip to main content
SpringerLink
Log in

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

  • Published:
Annals of Operations Research Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • 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 

  • 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 

  • 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.

    Chapter  Google Scholar 

  • 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.

  • Blazewicz, J., & Kasprzak, M. (2003). Complexity of DNA sequencing by hybridization. Theoretical Computer Science, 290(3), 1459–1473.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Blazewicz, J., Formanowicz, P., Guinand, F., & Kasprzak, M. (2002a). A heuristic managing errors for DNA sequencing. Bioinformatics, 18, 652–660.

    Article  Google Scholar 

  • Blazewicz, J., Kasprzak, M., & Kuroczycki, W. (2002b). Hybrid genetic algorithm for DNA sequencing with errors. Journal of Heuristics, 8, 495–502.

    Article  Google Scholar 

  • Blazewicz, J., Glover, F., & Kasprzak, M. (2004). DNA sequencing—tabu and scatter search combined. INFORMS Journal on Computing, 16, 232–240.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Burke, E. K., & Kendall, G. (Eds.) (2005). Search methodologies: introductory tutorials in optimization and decision support techniques. Berlin: Springer.

    Google Scholar 

  • 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 

  • 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 

  • Burke, E. K., Kendall, G., & Soubeiga, E. (2003b). A tabu-search hyperheuristic for timetabling and rostering. Journal of Heuristics, 9(6), 451–470.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • Glover, F., & Laguna, M. (1997). Tabu search. Boston: Kluwer Academic.

    Book  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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 

  • Kirkpatrick, S., Gelatt, C.D., & Vecchi, M.P. (1983). Optimization by simulated annealing. Science, 220, 671–680.

    Article  Google Scholar 

  • 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 

  • Mruczkiewicz, W. (2009). Hyper-heuristics for sequencing by hybridisation problem. Master Thesis, Poznan University of Technology, Poland.

  • 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.

    Article  Google Scholar 

  • Pevzner, P. A. (1989). 1-tuple DNA sequencing: computer analysis. Journal of Biomolecular Structure and Dynamics, 7, 63–73.

    Google Scholar 

  • 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 

  • 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 

  • Southern, E. (1988). United Kingdom Patent Application GB8810400.

  • Zhang, J. H., LY, Wu, & Zhang, X. S. (2003). Reconstruction of DNA sequencing by hybridization. Bioinformatics, 19(1), 14–21.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Computing Science, Poznan University of Technology, Poznan, Poland

    Jacek Blazewicz, Wojciech Mruczkiewicz & Aleksandra Swiercz

  2. Institute of Bioorganic Chemistry, Polish Academy of Science, Poznan, Poland

    Jacek Blazewicz & Aleksandra Swiercz

  3. School of Computer Science, University of Nottingham, Jubilee Campus, Nottingham, UK

    Edmund K. Burke & Graham Kendall

  4. Department of Industrial Engineering, Koç University, Sariyer, Istanbul, Turkey

    Ceyda Oguz

Authors
  1. Jacek Blazewicz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Edmund K. Burke
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Graham Kendall
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wojciech Mruczkiewicz
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ceyda Oguz
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Aleksandra Swiercz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aleksandra Swiercz.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Blazewicz, J., Burke, E.K., Kendall, G. et al. A hyper-heuristic approach to sequencing by hybridization of DNA sequences. Ann Oper Res 207, 27–41 (2013). https://doi.org/10.1007/s10479-011-0927-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-011-0927-y

Keywords

Search

Navigation