Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Large-scale phylogenetic analysis on current HPC architectures
Scientific Programming - Large-Scale Programming Tools and Environments
Hybrid MPI/OpenMP Parallel Programming on Clusters of Multi-Core SMP Nodes
PDP '09 Proceedings of the 2009 17th Euromicro International Conference on Parallel, Distributed and Network-based Processing
Firefly algorithm, stochastic test functions and design optimisation
International Journal of Bio-Inspired Computation
Parallel multi-objective approaches for inferring phylogenies
EvoBIO'10 Proceedings of the 8th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Maximum likelihood of evolutionary trees is hard
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
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Phylogenetic inference is one of the most challenging problems in Computational Biology. As recent research lines aim to introduce multiobjective optimization techniques to resolve incongruences in Phylogenetics, parallel multiobjective metaheuristics can be useful to address the computational complexity required to perform phylogenetic analyses according to multiple criteria simultaneously. In this work, we propose several master-worker hybrid approaches based on MPI and OpenMP to parallelize a multiobjective algorithm inspired by the behaviour of fireflies for inferring phylogenies on multicore cluster architectures. Experiments on four real biological data sets suggest that this algorithm can achieve significant speedup and efficiency values by using a proper hybrid model designed to exploit parallelism at the inference and assessment levels.