Inferring Phylogenetic Trees Using a Multiobjective Artificial Bee Colony Algorithm

  • Sergio Santander-Jiménez
  • Miguel A. Vega-Rodríguez
  • Juan A. Gómez-Pulido
  • Juan M. Sánchez-Pérez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7246)


Phylogenetic Inference is considered as one of the most important research topics in the field of Bioinformatics. A variety of methods based on different optimality measures has been proposed in order to build and evaluate the trees which describe the evolution of species. A major problem that arises with this kind of techniques is the possibility of inferring discordant topologies from a same dataset. Another question to be resolved is how to manage the tree search process. As the space of possible topologies increases exponentially with the number of species in the input dataset, exhaustive methods cannot be applied. In this paper we propose a multiobjective adaptation of a well-known Swarm Intelligence algorithm, the Artificial Bee Colony, to reconstruct phylogenetic trees according to two criteria: maximum parsimony and maximum likelihood. Our approach shows a significant improvement in the quality of the inferred trees compared to other multiobjective proposals.


Artificial Bee Colony Swarm Intelligence Phylogenetic Inference Multiobjective Optimization 


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  1. 1.
    Felsenstein, J.: Inferring phylogenies. Sinauer Associates, Sunderland (2004); ISBN: 0-87893-177-5 Google Scholar
  2. 2.
    Handl, J., Kell, D., Knowles, J.: Multiobjective Optimization in Computational Biology and Bioinformatics. IEEE Transactions on Computational Biology and Bioinformatics 4(2), 289–292 (2006)Google Scholar
  3. 3.
    Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes University, Engineering Faculty, Computer Engineering Department (2005)Google Scholar
  4. 4.
    Karaboga, D., Basturk, B.: A Powerful and Efficient Algorithm for Numerical Function Optimization: Artificial Bee Colony (ABC) Algorithm. Journal of Global Optimization 39(3), 459–471 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Swofford, D., Olsen, G., Waddell, P., Hillis, D.: Phylogenetic Inference. Molecular Systematics, vol. 2, pp. 407–514. Sinauer Associates, Sunderland (1996)Google Scholar
  6. 6.
    Matsuda, H.: Construction of phylogenetic trees from amino acid sequences using a genetic algorithm. In: Proceedings of Genome Informatics Workshop, pp. 19–28. Universal Academy Press (1995)Google Scholar
  7. 7.
    Lewis, P.O.: A Genetic Algorithm for Maximum-Likelihood Phylogeny Inference Using Nucleotide Sequence Data. Molecular Biology and Evolution 15(3), 277–283 (1998)CrossRefGoogle Scholar
  8. 8.
    Congdon, C.: GAPHYL: An evolutionary algorithms approach for the study of natural evolution. In: Genetic and Evolutionary Computation Conference, pp. 1057–1064 (2002)Google Scholar
  9. 9.
    Deb, K.: Multi-objective optimization using evolutionary algorithms. Wiley-Interscience Series in Systems and Optimization. John Wiley & Sons, Chichester (2001); ISBN: 978-0-471-87339-6 zbMATHGoogle Scholar
  10. 10.
    Coelho, G.P., da Silva, A.E.A., Von Zuben, F.J.: Evolving Phylogenetic Trees: A Multiobjective Approach. In: Sagot, M.-F., Walter, M.E.M.T. (eds.) BSB 2007. LNCS (LNBI), vol. 4643, pp. 113–125. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    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
  12. 12.
    Poladian, L., Jermiin, L.: Multi-Objective Evolutionary Algorithms and Phylogenetic Inference with Multiple Data Sets. Soft Computing 10(4), 359–368 (2006)CrossRefGoogle Scholar
  13. 13.
    Fitch, W.: Toward Defining the Course of Evolution: Minimum Change for a Specific Tree Topology. Systematic Zoology 20(4), 406–416 (1972)CrossRefGoogle Scholar
  14. 14.
    Felsenstein, J.: Evolutionary Trees from DNA Sequences: A Maximum Likelihood Approach. Journal of Molecular Evolution 17, 368–376 (1981)CrossRefGoogle Scholar
  15. 15.
    Felsenstein, J.: PHYLIP (Phylogeny Inference Package) (2000),
  16. 16.
    Guindon, S., Dufayard, J.F., Lefort, V., Anisimova, M., Hordijk, W., Gascuel, O.: New Algorithms and Methods to Estimate Maximum-Likelihood Phylogenies: Assessing the Performance of PhyML 3.0. Systematic Biology 59(3), 307–321 (2010)CrossRefGoogle Scholar
  17. 17.
    Dutheil, J., Gaillard, S., Bazin, E., Glémin, S., Ranwez, V., Galtier, N., Belkhir, K.: Bio++: a set of C++ libraries for sequence analysis, phylogenetics, molecular evolution and population genetics. BMC Bioinformatics 7, 188 (2006)CrossRefGoogle Scholar
  18. 18.
    Press, W., Teukolsky, S., Vetterling, W., Flannery, B.: Numerical Recipes in C, The Art of Scientific Computing. Cambdrige University Press (1992); ISBN: 0–521–43108–5 Google Scholar
  19. 19.
    Weicker, N., Szabo, G., Weicker, K., Widmayer, P.: Evolutionary multiobjective optimization for base station transmitter placement with frequency assignment. IEEE Transactions on Evolutionary Computation 7(2), 189–203 (2003)CrossRefGoogle Scholar
  20. 20.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2002)CrossRefGoogle Scholar
  21. 21.
    Shimodaira, H., Hasegawa, M.: Multiple comparisons of log-likelihoods with applications to phylogenetic inference. Molecular Biology and Evolution 16, 1114–1116 (1999)CrossRefGoogle Scholar
  22. 22.
    Cancino, W., Delbem, A.C.B.: A Multi-Criterion Evolutionary Approach Applied to Phylogenetic Reconstruction. In: Korosec, P. (ed.) New Achievements in Evolutionary Computation, pp. 135–156, InTech (2010); ISBN: 978-953-307-053-7Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Sergio Santander-Jiménez
    • 1
  • Miguel A. Vega-Rodríguez
    • 1
  • Juan A. Gómez-Pulido
    • 1
  • Juan M. Sánchez-Pérez
    • 1
  1. 1.Department of Technologies of Computers and Communications, ARCO Research Group, Escuela PolitécnicaUniversity of ExtremaduraCáceresSpain

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