Skip to main content
SpringerLink
Log in

A Math-Heuristic Algorithm for the DNA Sequencing Problem

  • Conference paper
Learning and Intelligent Optimization (LION 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6073))

Included in the following conference series:

Abstract

One of the key issues in designing an algorithm in general, and a metaheuristic in particular, concerns the fine tuning of one or more algorithmic parameters. In this paper, we present a simple mechanism aimed at automatically fine tuning a parameter of a novel hybrid algorithm. We design an algorithm that uses mathematical programming techniques in a metaheuristic fashion and we exploit ideas from the corridor method to drive the use of a standard MIP solver over different portions of the solution space. The size and the boundaries of such portions of the solution space are determined by the width of the corridor built around an incumbent solution. In turn, the corridor width is automatically fine tuned by the proposed mechanism, taking into account the evolution of the search process. The proposed algorithm is then tested on a well known problem from computational biology and results on a set of benchmark instances are provided.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Sniedovich, M., Voß, S.: The Corridor Method: A Dynamic Programming Inspired Metaheuristic. Control and Cybernetics 35(3), 551–578 (2006)

    MATH  MathSciNet  Google Scholar 

  2. Maniezzo, V., Stützle, T., Voß, S. (eds.): Matheuristics: Hybridizing Metaheuristics and Mathematical Programming. Springer, Berlin (2009)

    MATH  Google Scholar 

  3. Caserta, M., Voß, S.: Metaheuristics: Intelligent Problem Solving. In: Maniezzo, V., Stützle, T., Voß, S. (eds.) Matheuristics: Hybridizing Metaheuristics and Mathematical Programming, pp. 1–38. Springer, Berlin (2009)

    Google Scholar 

  4. Caserta, M., Quiñonez Rico, E.: A Cross Entropy-Lagrangean Hybrid Algorithm for the Multi-Item Capacitated Lot Sizing Problem with Setup Times. Computers & Operations Research 36(2), 530–548 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  5. Adenso-Diaz, B., Laguna, M.: Fine-tuning of Algorithms Using Fractional Experimental Designs and Local Search. Operations Research 54, 99–114 (2006)

    Article  MATH  Google Scholar 

  6. Coy, S.P., Golden, B.L., Rungen, G.C., Wasil, E.A.: Using Experimental Design to Find Effective Parameter Settings for Heuristics. Journal of Heuristics 7, 77–97 (2000)

    Article  Google Scholar 

  7. Park, M.W., Kim, Y.D.: A Systematic Procedure for Setting Parameters in Simulated Annealing Algorithms. Computers & Operations Research 25(3), 207–217 (1998)

    Article  MATH  Google Scholar 

  8. Xu, J., Chiu, S.Y., Glover, F.: Fine-tuning a Tabu Search Algorithm with Statistical Tests. International Transactions in Operational Research 5(3), 233–244 (1998)

    Article  Google Scholar 

  9. Parson, R., Johnson, M.E.: A Case Study in Experimental Design Applied to Genetic Algorithms with Applications to DNA Sequence Assembly. American Journal of Mathematical and Management Sciences 17(3), 369–396 (1997)

    Google Scholar 

  10. Battiti, R., Tecchioli, G.: The Reactive Tabu Search. ORSA Journal on Computing 6(2), 126–140 (1994)

    MATH  Google Scholar 

  11. Sanger, F., Nicklen, S., Chase, A.R.: DNA Sequencing with Chain Terminating Inhibitors. Proceedings National Academy of Science 74(12), 5463–5468 (1977)

    Article  Google Scholar 

  12. Ronaghi, M., Uhlzn, M., Nyrzn, P.: DNA SEQUENCING: A Sequencing Method Based on Real-Time Pyrophosphate. Science 281(5375), 363–365 (1998)

    Article  Google Scholar 

  13. Drmanac, R., Crkvenjakov, R.: Yugoslav Patent Application 570 (1987)

    Google Scholar 

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

    Google Scholar 

  15. Bains, W., Smith, G.C.: A Novel Method for Nucleid Acid Sequence Determination. Journal of Theoretical Biology 135, 303–307 (1988)

    Article  Google Scholar 

  16. Macevicz, S.C.: International Patent Application PS US8904741 (1989)

    Google Scholar 

  17. Pevzner, P.A., Lipshutz, R.J.: Towards DNA Sequencing Chips. In: Prívara, I., Rovan, B., Ružička, P. (eds.) MFCS 1994. LNCS, vol. 841, pp. 143–158. Springer, Heidelberg (1994)

    Google Scholar 

  18. Pevznev, P.A.: l-tuple DNA Sequencing: Computer Analysis. Journal of Biomolecular Structure and Dynamics 7, 63–73 (1989)

    Google Scholar 

  19. Nikolakopoulos, A., Sarimveis, H.: A Metaheuristic Approach for the Sequencing by Hybridization Problem with Positive and Negative Errors. Engineering Applications of Artificial Intelligence 21(2), 247–258 (2008)

    Article  Google Scholar 

  20. Blazewicz, J., Oguz, C., Swiercz, A., Weglarz, J.: DNA Sequencing by Hybridization via Genetic Search. Operations Research 54(6), 1185–1192 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  21. Bui, T.N., Youssef, W.A.: An Enhanced Genetic Algorithm for DNA Sequencing by Hybridization with Positive and Negative Errors. In: Deb, K., Poli, R., Banzahf, W., Beyer, H., Burke, E., Darwen, P., Dasgupta, D., Floreano, D., Foster, J., Harman, M., Holland, O., Lanzi, P., Spector, L., Tettamanzi, A., Thierens, D., Tyrrell, A. (eds.) GECCO 2004. LNCS, vol. 3103, pp. 908–919. Springer, Heidelberg (2004)

    Google Scholar 

  22. Blazewicz, J., Formanowicz, P., Kasprzak, K., Markiewicz, W.T., Weglarz, J.: Tabu Search for DNA Sequencing with False Negative and False Positive. European Journal of Operational Research 125, 257–265 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  23. Blazewicz, J., Kasprzak, M.: Complexity of DNA Sequencing by Hybridization. Theoretical Computer Science 290, 1459–1473 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  24. Blazewicz, J., Formanovicz, P., Kasprzak, M., Markiewicz, W.T., Weglarz, J.: DNA Sequencing with Positive and Negative Errors. Journal of Computational Biology 6, 113–126 (1999)

    Article  Google Scholar 

  25. Caserta, M., Voß, S.: A Hybrid Algorithm for the DNA Sequencing Problem. Technical report, Institute of Information Systems, University of Hamburg (2009)

    Google Scholar 

  26. Blazewicz, J., Glover, F., Kasprzak, M.: DNA Sequencing – Tabu and Scatter Search Combined. INFORMS Journal on Computing 16(3), 232–240 (2004)

    Article  MathSciNet  Google Scholar 

  27. Blazewicz, J., Kasprzak, M., Kuroczycki, W.: Hybrid Genetic Algorithm for DNA Sequencing with Errors. Journal of Heuristics 8, 495–502 (2002)

    Article  MATH  Google Scholar 

  28. Blazewicz, J., Glover, F., Kasprzak, K., Markiewicz, W.T., Oguz, C., Rebholz-Schuhmann, D., Swiercz, A.: Dealing with Repetitions in Sequencing by Hybridization. Computational Biology and Chemistry 30, 313–320 (2006)

    Article  MATH  Google Scholar 

  29. Tsiligirides, T.: Heuristic Methods Applied to Orienteering. Journal of the Operational Research Society 35(9), 797–809 (1984)

    Google Scholar 

  30. Lougee-Heimer, R.: The Common Optimization INterface for Operations Research. IBM Journal of Research and Development 47(1), 57–66 (2003)

    Article  Google Scholar 

  31. Fischetti, M., Lodi, A.: Local Branching. Mathematical Programming B 98, 23–47 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  32. Hansen, P., Mladenović, N.: An Introduction to Variable Neighborhood Search. In: Voß, S., Martello, S., Osman, I., Roucairol, C. (eds.) Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization, pp. 433–458. Kluwer, Boston (1999)

    Google Scholar 

  33. National Center for Biotechnology Information (May 2009), http://www.ncbi.nlm.nih.gov/

  34. Needelman, S.B., Wunsch, C.D.: A General Method Applicable to the Search for Similarities of the Aminoacid Sequence of Two Proteins. Journal of Molecular Biology 48, 443–453 (1970)

    Article  Google Scholar 

  35. Gentleman, C., Carey, V.J., Bates, D.M., Bolstad, B., Dettling, M., Dudoit, S., Ellis, B., Gautier, L., Ge, Y., Gentry, J., Hornik, K., Hothorn, T., Huber, W., Iacus, S., Irizarry, R., Leisch, F., Li, C., Maechler, M., Rossini, A.J., Sawitzki, G., Smith, C., Smyth, G., Tierney, L., Yang, J.Y.H., Zhang, J.: Bioconductor: Open Software Development for Computational Biology and Bioinformatics. Genome Biology 5, R80 (2004)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Information Systems (IWI), University of Hamburg, Von-Melle-Park 5, 20146, Hamburg, Germany

    Marco Caserta & Stefan Voß

Authors
  1. Marco Caserta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stefan Voß
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. ALBCOM Research Group, Universitat Politècnica de Catalunya, Omega 112, Campus Nord, Jordi Girona 1-3, 08034, Barcelona, Spain

    Christian Blum

  2. LION Research Group, Università degli Studi di Trento, Via Sommarive, 14, 38123, Povo (Trento), Italy

    Roberto Battiti

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Caserta, M., Voß, S. (2010). A Math-Heuristic Algorithm for the DNA Sequencing Problem. In: Blum, C., Battiti, R. (eds) Learning and Intelligent Optimization. LION 2010. Lecture Notes in Computer Science, vol 6073. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13800-3_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-13800-3_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13799-0

  • Online ISBN: 978-3-642-13800-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation