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Comparing Different Operators and Models to Improve a Multiobjective Artificial Bee Colony Algorithm for Inferring Phylogenies

  • Sergio Santander-Jiménez
  • Miguel A. Vega-Rodríguez
  • Juan A. Gómez-Pulido
  • Juan M. Sánchez-Pérez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7505)

Abstract

Maximum parsimony and maximum likelihood approaches to phylogenetic reconstruction were proposed with the aim of describing the evolutionary history of species by using different optimality principles. These discrepant points of view can lead to situations where discordant topologies are inferred from a same dataset. In recent years, research efforts in Phylogenetics try to apply multiobjective optimization techniques to generate phylogenetic topologies which suppose a consensus among different criteria. In order to generate high quality topologies, it is necessary to perform an exhaustive study about topological search strategies as well as to decide the most fitting molecular evolutionary model in agreement with statistical measurements. In this paper we report a study on different operators and models to improve a Multiobjective Artificial Bee Colony algorithm for inferring phylogenies according to the parsimony and likelihood criteria. Experimental results have been evaluated using the hypervolume metrics and compared with other multiobjective proposals and state-of-the-art phylogenetic software.

Keywords

Phylogenetic inference swarm intelligence multiobjective optimization artificial bee colony 

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References

  1. 1.
    Swofford, D., Olsen, G., Waddell, P., Hillis, D.: Phylogenetic Inference. Molecular Systematics 2, 407–514 (1996)Google Scholar
  2. 2.
    Felsenstein, J.: Inferring phylogenies. Sinauer Associates, Sunderland (2004) ISBN: 0-87893-177-5 Google Scholar
  3. 3.
    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
  4. 4.
    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
  5. 5.
    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
  6. 6.
    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 VII. LNCS, vol. 2439, pp. 720–729. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  7. 7.
    Bos, D.H., Posada, D.: Using models of nucleotide evolution to build phylogenetic trees. Developmental and Comparative Immunology 29, 211–227 (2005)CrossRefGoogle Scholar
  8. 8.
    Rokas, A., Williams, B.L., King, N., Carroll, S.B.: Genome-scale approaches to resolving incongruence in molecular phylogenies. Nature 425(6960), 798–804 (2003)CrossRefGoogle 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.
    Poladian, L., Jermiin, L.: Multi-Objective Evolutionary Algorithms and Phylogenetic Inference with Multiple Data Sets. Soft Computing 10(4), 359–368 (2006)CrossRefGoogle Scholar
  11. 11.
    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
  12. 12.
    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: 978-953-307-053-7 Google Scholar
  13. 13.
    Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Tech. Rep. TR06, Erciyes University, Engineering Faculty, Computer Engineering Department (2005)Google Scholar
  14. 14.
    Schmidt, O., Drake, H.L., Horn, M.A.: Hitherto Unknown [Fe-Fe]-Hydrogenase Gene Diversity in Anaerobes and Anoxic Enrichments from a Moderately Acidic Fen. Applied and Environmental Microbiology 76(6), 2027–2031 (2010)CrossRefGoogle Scholar
  15. 15.
    Pol, D., Siddall, M.E.: Biases in Maximum Likelihood and Parsimony: A Simulation Approach to a 10-Taxon Case. Cladistics 17(3), 266–281 (2001)CrossRefGoogle Scholar
  16. 16.
    Santander-Jiménez, S., Vega-Rodríguez, M.A., Gómez-Pulido, J.A., Sánchez-Pérez, J.M.: Inferring Phylogenetic Trees Using a Multiobjective Artificial Bee Colony Algorithm. In: Giacobini, M., Vanneschi, L., Bush, W.S. (eds.) EvoBIO 2012. LNCS, vol. 7246, pp. 144–155. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  17. 17.
    Snell, Q., Whiting, M., Clement, M., McLaughlin, D.: Parallel Phylogenetic Inference. In: Proceedings of the 2000 ACM/IEEE conference on Supercomputing, Article 35. IEEE Computer Society (2000)Google Scholar
  18. 18.
    Goëffon, A., Richer, J.M., Hao, J.K.: Progressive Tree Neighborhood Applied to the Maximum Parsimony Problem. IEEE/ACM Transactions on Computational Biology and Bioinformatics 5, 136–145 (2008)CrossRefGoogle Scholar
  19. 19.
    Karaboga, D., Gorkemli, B., Ozturk, C., Karaboga, N.: A comprehensive survey: Artificial Bee Colony (ABC) algorithm and applications. Artificial Intelligence Review, 1–37 (2012), doi:10.1007/s10462-012-9328-0Google Scholar
  20. 20.
    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, 188–193 (2006)CrossRefGoogle Scholar
  21. 21.
    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
  22. 22.
    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
  23. 23.
    Goloboff, P.A., Farris, J.S., Nixon, K.C.: TNT, a free program for phylogenetic analysis. Cladistics 24, 774–786 (2008)CrossRefGoogle Scholar
  24. 24.
    Stamatakis, A.: RAxML-VI-HPC: Maximum Likelihood-based Phylogenetic Analyses with Thousands of Taxa and Mixed Models. Bioinformatics 22(21), 2688–2690 (2006)CrossRefGoogle 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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