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)


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.


Phylogenetic inference swarm intelligence multiobjective optimization artificial bee colony 


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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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