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Parallel Multi-Objective Approaches for Inferring Phylogenies

  • Waldo Cancino
  • Laetitia Jourdan
  • El-Ghazali Talbi
  • Alexandre C. B. Delbem
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6023)

Abstract

The inference of the phylogenetic tree that best express the evolutionary relationships concerning data is one of the central problem of bioinformatics. Several single optimality criterion have been proposed for the phylogenetic reconstruction problem. However, different criteria may lead to conflicting phylogenies. In this scenario, a multi-objective approach can be useful since it could produce a set of optimal trees according to multiple criteria. PhyloMOEA is a multi objective evolutionary approach applied to phylogenetic inference using maximum parsimony and maximum likelihood criteria. On the other hand, the computational power required for phylogenetic inference of large alignments easily surpasses the capabilities of single machines. In this context, the parallelization of the heuristic reconstruction methods can not only help to reduce the inference execution time but also improve the results quality and search robustness. On the other hand, The PhyloMOEA parallelization represents the next development step in order to reduce the execution time. In this paper, we present the PhyloMOEA parallel version developed using the ParadisEO framework. The experiments conducted show significant speedup in the execution time for the employed datasets.

Keywords

Phylogenetic Inference Multi-Objective Optimization Parallel Computing 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Waldo Cancino
    • 1
  • Laetitia Jourdan
    • 1
  • El-Ghazali Talbi
    • 1
  • Alexandre C. B. Delbem
    • 2
  1. 1.INRIA Lille Nord EuropeVilleneuve d’AscqFrance
  2. 2.Institute of Mathematics and Computer ScienceUniversity of Sao PauloBrazil

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