A Multi-objective Evolutionary Approach for Phylogenetic Inference.

The phylogeny reconstruction problem consists of determining the most accurate tree that represents evolutionary relationships among species. Different criteria have been employed to evaluate possible solutions in order to guide a search algorithm towards the best tree. However, these criteria may lead to distinct phylogenies, which are often conflicting among them. In this context, a multi-objective approach can be useful since it could produce a spectrum of equally optimal trees (Pareto front) according to all criteria. We propose a multi-objective evolutionary algorithm, named PhyloMOEA, which employs the maximum parsimony and likelihood criteria to evaluate solutions. PhyloMOEA was tested using four datasets of nucleotide sequences. This algorithm found, for all datasets, a Pareto front representing a trade-off between the criteria. Moreover, SH-test showed that most of solutions have scores similar to those obtained by phylogenetic programs using one criterion. [ABSTRACT FROM AUTHOR]