Algoritmos evolutivos multi-objetivo para a reconstrução de árvores filogenéticas

Abstract

The phylogeny reconstruction problem consists of determining the evolutionary relationships (usually represented as a tree) among species. This is a very complex problem since the tree search space is huge. Several phylogenetic reconstruction methods have been proposed. Many of them de nes an optimality criterion for evaluation of possible solutions. However, di erent criteria may lead to distinct phylogenies, which often con ict with each other. In this context, a multi-objective approach for phylogeny reconstruction can be useful since it could produce a set of optimal trees according to mdi cultultiple criteria. In this thesis, a multi-objective evolutionary algorithm for phylogenetic reconstruction, called PhyloMOEA, is proposed. PhyloMOEA uses the parsimony and likelihood criteria, which are two of the most used phylogenetic reconstruction methods. PhyloMOEA was tested using four datasets of nucleotide sequences found in the literature. For each dataset, the proposed algorithm found a Pareto front representing a trade-o between the used criteria. Trees in the Pareto front were statistically validated using the SH-test, which has shown that a number of intermediate solutions from PhyloMOEA are consistent with solutions found by phylogenetic methods using one criterion. Moreover, clade support values from trees found by PhyloMOEA was compared to clade posterior probabilities obtained by Mr.Bayes. Results indicate a correlation between these probabilities for several clades. In summary, PhyloMOEA is able to nd diverse intermediate solutions, which are not statistically worse than the best solutions for the maximum parsimony and maximum likelihood criteria. Moreover, intermediate solutions represent a trade-o between these criteria.