Binary Particle Swarm Optimization Versus Hybrid Genetic Algorithm for Inferring Well Supported Phylogenetic Trees

  • Bassam Alkindy
  • Bashar Al-Nuaimi
  • Christophe Guyeux
  • Jean-François Couchot
  • Michel Salomon
  • Reem Alsrraj
  • Laurent Philippe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9874)

Abstract

The amount of completely sequenced chloroplast genomes increases rapidly every day, leading to the possibility to build large-scale phylogenetic trees of plant species. Considering a subset of close plant species defined according to their chloroplasts, the phylogenetic tree that can be inferred by their core genes is not necessarily well supported, due to the possible occurrence of “problematic” genes (i.e., homoplasy, incomplete lineage sorting, horizontal gene transfers, etc.) which may blur the phylogenetic signal. However, a trustworthy phylogenetic tree can still be obtained provided such a number of blurring genes is reduced. The problem is thus to determine the largest subset of core genes that produces the best-supported tree. To discard problematic genes and due to the overwhelming number of possible combinations, this article focuses on how to extract the largest subset of sequences in order to obtain the most supported species tree. Due to computational complexity, a distributed Binary Particle Swarm Optimization (BPSO) is proposed in sequential and distributed fashions. Obtained results from both versions of the BPSO are compared with those computed using an hybrid approach embedding both genetic algorithms and statistical tests. The proposal has been applied to different cases of plant families, leading to encouraging results for these families.

Keywords

Chloroplasts Phylogeny Genetic algorithms Lasso test Binary Particle Swarm Optimization 

Notes

Acknowledgements

All computations have been performed on the Mésocentre de calculs supercomputer facilities of the University of Franche-Comté.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Bassam Alkindy
    • 1
    • 2
  • Bashar Al-Nuaimi
    • 1
  • Christophe Guyeux
    • 1
  • Jean-François Couchot
    • 1
  • Michel Salomon
    • 1
  • Reem Alsrraj
    • 1
  • Laurent Philippe
    • 1
  1. 1.FEMTO-ST Institute, UMR 6174 CNRS, DISC Computer Science DepartmentUniversity of Bourgogne Franche-ComtéBesançonFrance
  2. 2.Department of Computer ScienceUniversity of MustansiriyahBaghdadIraq

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