Skip to main content
SpringerLink
Log in
Book cover

European Conference on Evolutionary Computation in Combinatorial Optimization

EvoCOP 2005: Evolutionary Computation in Combinatorial Optimization pp 58–67Cite as

On the Application of Evolutionary Algorithms to the Consensus Tree Problem

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3448))

Abstract

Computing consensus trees amounts to finding a single tree that summarizes a collection of trees. Three evolutionary algorithms are defined for this problem, featuring characteristics of genetic programming (GP), evolution strategies (ES) and evolutionary programming (EP) respectively. These algorithms are evaluated on a benchmark composed of phylogenetic trees computed from genomic data. The GP-like algorithm is shown to provide better results than the other evolutionary algorithms, and than two greedy heuristics defined ad hoc for this problem.

This is a preview of subscription content, 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Foster, C.: Information retrieval: information storage and retrieval using AVL trees. In: Proceedings of the 1965 20th ACM National Conference, pp. 192–205. ACM Press, New York (1965)

    Chapter  Google Scholar 

  2. Garofalakis, M., Özden, B., Silberschatz, A.: Resource scheduling in enhanced pay-per-view continuous media databases. In: Jarke, M., et al. (eds.) Proceedings of the 1997 International Conference on Very Large Databases, Athens, Greece, pp. 516–525. Morgan Kaufmann, San Francisco (1997)

    Google Scholar 

  3. Naylor, B.: Partitioning tree image representation and generation from 3D geometric models. In: Booth, K., Fournier, A. (eds.) Proceedings of the 1992 Conference on Graphics Interface, pp. 201–212. Morgan Kaufmann, San Francisco (1992)

    Google Scholar 

  4. Holmes, S.: Phylogenies: An overview. In: Halloran, M., Geisser, S. (eds.) Statistics and Genetics, pp. 81–119. Springer, New York (1999)

    Google Scholar 

  5. Kim, J., Warnow, T.: Tutorial on phylogenetic tree estimation. In: Lengauer, T., et al. (eds.) Proceedings of the 7th International Conference on Intelligent Systems for Molecular Biology, Heidelberg, pp. 196–205. AAAI Press, Menlo Park (1999)

    Google Scholar 

  6. Day, W., Johnson, D., Sankoff, D.: The computational complexity of inferring rooted phylogenies by parsimony. Mathematical Biosciences 81, 33–42 (1986)

    Article  MATH  MathSciNet  Google Scholar 

  7. Foulds, L., Graham, R.: The Steiner problem in phylogeny is NP−complete. Advances in Applied Mathematics 3, 439–449 (1982)

    Article  MathSciNet  Google Scholar 

  8. Wu, B., Chao, K.M., Tang, C.: Approximation and exact algorithms for constructing minimum ultrametric trees from distance matrices. Journal of Combinatorial Optimization 3, 199–211 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  9. King, B.: Step-wise clustering procedures. Journal of the American Statistical Association 69, 86–101 (1967)

    Article  Google Scholar 

  10. Moilanen, A.: Searching for the most parsimonious trees with simulated evolution. Cladistics 15, 39–50 (1999)

    Article  Google Scholar 

  11. Andreatta, A., Ribeiro, C.: Heuristics for the phylogeny problem. Journal of Heuristics 8, 429–447 (2002)

    Article  MATH  Google Scholar 

  12. Cotta, C., Moscato, P.: Inferring phylogenetic trees using evolutionary algorithms. In: Merelo, J., et al. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 720–729. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  13. Barker, D.: LVB: parsimony and simulated annealing in the search for phylogenetic trees. Bioinformatics 20, 274–275 (2004)

    Article  Google Scholar 

  14. Bryant, D.: A classification of consensus methods for phylogenetics. In: Janowitz, M., et al. (eds.) Bioconsensus. DIMACS-AMS, pp. 163–184 (2003)

    Google Scholar 

  15. Swofford, D.: When are phylogeny estimates from molecular and morphological data incongruent? In: Miyamoto, M., Cracraft, J. (eds.) Phylogenetic analysis of DNA sequences, pp. 295–333. Oxford University Press, Oxford (1991)

    Google Scholar 

  16. Phillips, C., Warnow, T.: The asymmetric median tree – a new model for building consensus trees. Discrete Applied Mathematics 71, 311–335 (1996)

    Article  MATH  MathSciNet  Google Scholar 

  17. Gusfield, D.: Efficient algorithms for inferring evolutionary trees. Networks 21, 19–28 (1991)

    Article  MATH  MathSciNet  Google Scholar 

  18. Day, W.: Optimal algorithms for comparing trees with labeled leaves. Journal of Classiffication 2, 7–28 (1985)

    Article  MATH  Google Scholar 

  19. Barthélemy, J.P., McMorris, F.: The median procedure for n-trees. Journal of Classiffication 3, 329–334 (1986)

    Article  MATH  Google Scholar 

  20. Östlin, A.: Constructing evolutionary trees. Algorithms and complexity. PhD thesis, Department of Computer Science, Lund University (2001)

    Google Scholar 

  21. Penny, D., Hendy, M.: The use of tree comparison metrics. Systematic Zoology 34 (1985)

    Google Scholar 

  22. Allen, B., Steel, M.: Subtree transfer operations and their induced metrics on evolutionary trees. Annals of Combinatorics 5, 1–15 (2001)

    Article  MathSciNet  Google Scholar 

  23. DasGupta, B., He, X., Jiang, T., Li, M., Tromp, J., Zhang, L.: On computing the nearest neighbor interchange distance. In: Du, D., Pardalos, P., Wang, J. (eds.) Proceedings of the DIMACS Workshop on Discrete Problems with Medical Applications, American Mathematical Society. DIMACS Series in Discrete Mathematics and Theoretical Computer Science, vol. 55, pp. 125–143 (2000)

    Google Scholar 

  24. Wang, J., Shan, H., Shasha, D., Piel, W.: Treerank: A similarity measure for nearest neighbor searching in phylogenetic databases. In: Proceedings of the 15th International Conference on Scientific and Statistical Database Management, Cambridge MA. IEEE Press, Los Alamitos (2003)

    Google Scholar 

  25. Jain, A., Murty, N., Flynn, P.: Data clustering: A review. ACM Computing Surveys 31, 264–323 (1999)

    Article  Google Scholar 

  26. Raidl, G., Julstrom, B.: Edge sets: an effective evolutionary coding of spanning trees. IEEE Transactions on Evolutionary Computation 7, 225–239 (2003)

    Article  Google Scholar 

  27. Rothlauf, F.: Representations for Genetic and Evolutionary Algorithms. Studies in Fuzziness and Soft Computing, Physica, Heidelberg (2002)

    Google Scholar 

  28. Sneath, P., Sokal, R.: Numerical Taxonomy, Freeman, London, UK (1973)

    Google Scholar 

  29. Ward, J.: Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association 58, 236–244 (1963)

    Article  MathSciNet  Google Scholar 

  30. Cotta, C.: Scatter search with path relinking for phylogenetic inference. European Journal of Operational Research (2005) (to appear)

    Google Scholar 

  31. Hibbett, D., Donoghue, M.: Analysis of character correlations among wood decay mechanisms, mating systems, and substrate ranges in homobasidiomycetes. Systematic Biology 50, 1–27 (2001)

    Article  Google Scholar 

  32. Binder, M., Hibbett, D., Molitoris, H.: Phylogenetic relationships of the marine gasteromycete Nia vibrissa. Mycologia 93, 679–688 (2001)

    Article  Google Scholar 

  33. Hibbett, D., Gilbert, L.B., Donoghue, M.: Evolutionary instability of ectomycorrhizal symbioses in basidiomycetes. Nature 407, 506–508 (2000)

    Article  Google Scholar 

  34. Lehmann, E., D’Abrera, H.: Nonparametrics: Statistical Methods Based on Ranks. Prentice-Hall, Englewood Cliffs (1998)

    Google Scholar 

  35. Fogel, D.: Evolutionary Computation: Toward a New Philosophy of Machine Intelligence. IEEE Press, Piscataway (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. Lenguajes y Ciencias de la Computación, University of Málaga, ETSI Informática, Campus de Teatinos, 29071, Málaga, Spain

    Carlos Cotta

Authors
  1. Carlos Cotta
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Computer Graphics and Algorithms, Vienna University of Technology, Favoritenstraße 9–11/1861, 1040, Vienna, Austria

    Günther R. Raidl

  2. SAP AG, Neurottstr. 16, P.O. Box, Walldorf, Germany

    Jens Gottlieb

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Cotta, C. (2005). On the Application of Evolutionary Algorithms to the Consensus Tree Problem. In: Raidl, G.R., Gottlieb, J. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2005. Lecture Notes in Computer Science, vol 3448. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31996-2_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-31996-2_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25337-2

  • Online ISBN: 978-3-540-31996-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation