A Multi-Criterion Evolutionary Approach Applied to Phylogenetic Reconstruction

Phylogenetic inference is one of the central problems in computational biology. It consists in finding the best tree that explains the evolutionary history of species from a given dataset. Various phylogenetic reconstruction methods have been proposed in the literature. Most of them use one optimality criterion (or objective function) to evaluate possible solutions in order to determine the best tree. On the other hand, several researches (Huelsenbeck, 1995; Kuhner & Felsenstein, 1994; Tateno et al., 1994) have shown important differences in the results obtained by applying distinct reconstruction methods to the same input data. Rokas et al. (2003) pointed out that there are several sources of incongruity in phylogenetic analysis: the optimality criterion employed, the data sets used and the evolutionary assumptions concerning data. In other words, according to the literature, the selection of the reconstruction method has a great inuence on the results. In this context, a multi-objective approach can be a relevant contribution since it can search for phylogenies using more than one criterion and produce trees which are consistent with all employed criteria. Recently, Handl et al. (2006) discussed the current and future applications of multi-objective optimization in bioinformatics and computational biology problems. Poladian & Jermiin (2006) showed how multi-objective optimization can be used in phylogenetic inference from various conicting datasets. The authors highlighted that this approach reveals sources of such conicts and provides useful information for a robust inference. Coelho et al. (2007) propose a multi-objective Artificial Immune System (De Castro & Timmis, 2002) approach for the reconstruction of phylogenetic trees. The developed algorithm, called omniaiNet, was employed to find a set of Pareto-optimal trees that represent a trade-off between the minimum evolution (Kidd & Sgaramella, 1971) and the least-squares criteria (Cavalli-Sforza & Edwards, 1967). Compared to the tree found by Neighbor Joining (NJ) algorithm (Saitou & Nei, 1987), solutions obtained by omni-aiNet have better minimum evolution and least squares scores. In this paper, we propose a multi-objective approach for phylogenetic reconstruction using maximum parsimony (Fitch, 1972) and maximum likelihood (Felsenstein, 1981) criteria. The basis of this approach and preliminary results were presented in (Cancino & Delbem, 2007a,b). The proposed technique, called PhyloMOEA, is a multi-objective evolutionary algorithm (MOEA) based on the NSGA-II (Deb, 2001). The PhyloMOEA output is a set of

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