Parallel multi-objective approaches for inferring phylogenies

Authors:
Waldo Cancino;Laetitia Jourdan;El-Ghazali Talbi;Alexandre C. B. Delbem
Affiliations:
INRIA Lille Nord Europe, Villeneuve d’Ascq, France;INRIA Lille Nord Europe, Villeneuve d’Ascq, France;INRIA Lille Nord Europe, Villeneuve d’Ascq, France;Institute of Mathematics and Computer Science, University of Sao Paulo, Brazil
Venue:
EvoBIO'10 Proceedings of the 8th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Year:
2010

Citing 8
Cited 1

Multi-Objective Optimization Using Evolutionary Algorithms

Multi-Objective Optimization Using Evolutionary Algorithms
ParadisEO: A Framework for the Reusable Design of Parallel and Distributed Metaheuristics

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Multi-objective evolutionary algorithms and phylogenetic inference with multiple data sets

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pIQPNNI: parallel reconstruction of large maximum likelihood phylogenies

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RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models

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Exploiting Fine-Grained Parallelism in the Phylogenetic Likelihood Function with MPI, Pthreads, and OpenMP: A Performance Study

PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
Fine-grain Parallelism Using Multi-core, Cell/BE, and GPU Systems: Accelerating the Phylogenetic Likelihood Function

ICPP '09 Proceedings of the 2009 International Conference on Parallel Processing
Metaheuristics: From Design to Implementation

Metaheuristics: From Design to Implementation

Parallelizing a multiobjective swarm intelligence approach to phylogenetics using hybrid MPI/OpenMP schemes

Proceedings of the 20th European MPI Users' Group Meeting

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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.