Phylogeny Inference Using a Multi-objective Evolutionary Algorithm with Indirect Representation

  • Md. Rafiul Hassan
  • M. Maruf Hossain
  • C. K. Karmakar
  • Michael Kirley
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5361)


The inference of phylogenetic trees is one of the most important tasks in computational biology. In this paper, we propose an extension to multi-objective evolutionary algorithms to address this problem. Here, we adopt an enhanced indirect encoding for a tree using the corresponding Prüfer code represented in Newick format. The algorithm generates a range of non-dominated trees given alternative fitness measures such as statistical likelihood and maximum parsimony. A key feature of this approach is the preservation of the evolutionary hierarchy between species. Preliminary experimental results indicate that our model is capable of generating a set of optimized phylogenetic trees for given species data and the results are comparable with other techniques.


Phylogenetic Tree Maximum Parsimony Likelihood Score Parsimony Score Indirect Representation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Baldauf, S.L.: Phylogeny for the faint of heart: A tutorial. Trends in Genetics 19(6), 345–351 (2003)CrossRefGoogle Scholar
  2. 2.
    Felsenstein, J.: PHYLIP – Phylogeny Inference Package (2000),
  3. 3.
    Congdon, C.B., Septor, K.J.: Phylogenetic trees Using Evolutionary Search: Initial Progress in Extending Gaphyl to Work with Genetic Data. In: The 2003 Congress on Evolutionary Computation (CEC 2003), vol. 1, pp. 320–326 (December 2003)Google Scholar
  4. 4.
    Cancino, W., Delbem, A.C.B.: A Multi-Objective Evolutionary Approach for Phylogenetic Inference. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 428–442. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    Cotta, C., Moscato, P.: Inferring phylogenetic trees using evolutionary algorithms. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 720–729. Springer, Heidelberg (2002)Google Scholar
  6. 6.
    Gottlieb, J., Julstrom, B.A., Raidl, G.R., Rothlauf, F.: Prüfer Numbers: A Poor Representation of Spanning Trees for Evolutionary Search. In: Spector, L., Goodman, E.D., Wu, A., Langdon, W.B., Voigt, H., Gen, M., Sen, S., Dorigo, M., Pezeshk, S., Garzon, M.H., Burke, E. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2001), pp. 343–350. Morgan Kaufmann, San Francisco (2001)Google Scholar
  7. 7.
    Gascuel, O.: BIONJ: an improved version of the NJ algorithm based on a simple model of sequence data. Molecular Biology and Evolution 14(7), 685–695 (1997)CrossRefGoogle Scholar
  8. 8.
    Reijmers, T.H., Wehrens, R., Buydens, L.M.C.: Quality Criteria of Genetic Algorithms for Construction of Phylogenetic Trees. Journal of Computational Chemistry 20(8), 867–876 (1999)CrossRefGoogle Scholar
  9. 9.
    Gen, M., Li, Y.: Spanning tree-based genetic algorithm for the bicriteria fixedcharge transportation problem. In: Proceedings of the 1999 Congress on Evolutionary Computation (CEC 1999), Washington, DC, USA, vol. 3, p. 2271 (1999)Google Scholar
  10. 10.
    Handl, J., Kell, D.B., Knowles, J.: Multiobjective Optimization in Bioinformatics and Computational Biology. IEEE/ACM Transaction on Computational Biology and Bioinformatics 4(2), 279–292 (2007)CrossRefGoogle Scholar
  11. 11.
    Deb, K., Agarwal, S., Pratab, A., Meyarivan, T.: A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II. KanGAL report 2000001, Indian Institute of Technology, Kanpur, India (2000)Google Scholar
  12. 12.
    Zelwer, M., Daubin, V.: Detecting phylogenetic incongruence using BIONJ: an improvement of the ILD test. Molecular Phylogenetics and Evolution 33(3), 687–693 (2004)CrossRefGoogle Scholar
  13. 13.
    Poladian, L., Jermiin, L.S.: Multi-objective evolutionary algorithms and phylogentic inference with multiple data sets. Soft Computing 10, 359–368 (2006)CrossRefGoogle Scholar
  14. 14.
    Huelsenbeck, J.P., Ronquist, F.: MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics 17(8), 754–755 (2001)CrossRefGoogle Scholar
  15. 15.
    Saitou, N., Nei, M.: The Neighbor-Joining Method: A New Method for reconstructing Phylogenetic Trees. Molecular Biology and Evolution 4(4), 406–425 (1987)Google Scholar
  16. 16.
    Takezaki, N., Nei, M.: Genetic distances and reconstruction of phylogenetic trees from microsatellite DNA. Genetics 144(1), 389–399 (1996)Google Scholar
  17. 17.
    Swofford, D., Olsen, G., Waddell, P., Hillis, D.: Phylogeny Reconstruction. In: Molecular Systematics, 3rd edn., pp. 407–514. Sinauer (1996)Google Scholar
  18. 18.
    Lewis, P.O.: A Genetic Algorithm for Maximum-Likelihood Phylogeny Inference Using Nucleotide Data. Molecular Biology and Evolution 15(3), 277–283 (1998)CrossRefGoogle Scholar
  19. 19.
    Fitch, W.: Toward Difining the Course of Evolution: Minimum Change for a Specific Tree Topology. Systemetic Zoology 20(4), 406–416 (1972)CrossRefGoogle Scholar
  20. 20.
    Felsenstein, J.: Evolutionary Trees from DNA Sequences: A Maximum Likelihood Approach. Journal of Molecular Evolution 17, 368–376 (1981)CrossRefGoogle Scholar
  21. 21.
    Cole, J., Chai, B., Farris, R., Wang, K.S., McGarrell, D., Garrity, G., Tiedje, J.: The Ribosomal Database Project (RDP-II): Sequences and Tools for High-throughput rRNA Analysis. Nucleic Acids Research 33, D294–D296 (2005)CrossRefGoogle Scholar
  22. 22.
    Ingman, M., Gyllensten, U.: mtDB: Human Mitochondrial Genome Database, a Resource for Population Genetics and Medical Sciences. Nucleic Acids Research 34, D749– D751 (2006)CrossRefGoogle Scholar
  23. 23.
    Felsenstein, J.: DNAPARS – DNA Parsimony Program (1996),
  24. 24.
    Felsenstein, J.: DNAML – DNA Maximum Likelihood program (1993),

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Md. Rafiul Hassan
    • 1
  • M. Maruf Hossain
    • 1
  • C. K. Karmakar
    • 2
  • Michael Kirley
    • 1
  1. 1.Department of Computer Science and Software EngineeringThe University of MelbourneAustralia
  2. 2.Department of Electrical & Electronic EngineeringThe University of MelbourneAustralia

Personalised recommendations