Multi-Criterion Phylogenetic Inference using Evolutionary Algorithms

Various phylogenetic reconstruction methods have been proposed in order to determine the most accurate tree that represents evolutionary relationships among species. Each method defines a criterion for evaluation of possible solutions. This criterion leads the search to the best phylogenetic tree. However, different criteria may lead to distinct phylogenies, which often conflict with each other. In this context, a multi-objective approach can be useful since it could produce a set of optimal trees (Pareto front) according to multiple criteria. We propose a multi-objective evolutionary algorithm, called Phylo-MOEA, which is focused on maximum parsimony and maximum likelihood criteria. In experiments, several PhyloMOEA trials were performed using four datasets of nucleotide sequences. For each dataset, the proposed algorithm found a Pareto front representing a trade-off between the criteria used. Moreover, SH-test showed that a number of solutions from PhyloMOEA are not significantly worse than solutions found by phylogenetic programs using one criterion