Daniele Catanzaro1, Rafflaele Pesenti, Michel C Milinkovitch. 1. Laboratory of Evolutionary Genetics, Institute for Molecular Biology and Medicine (IBMM), Université Libre de Bruxelles (U,L,B,), CP300, Rue Jeener et Brachet 12, B-6041, Gosselies, Belgium. dacatanz@ulb.ac.be
Abstract
BACKGROUND: Distance matrix methods constitute a major family of phylogenetic estimation methods, and the minimum evolution (ME) principle (aiming at recovering the phylogeny with shortest length) is one of the most commonly used optimality criteria for estimating phylogenetic trees. The major difficulty for its application is that the number of possible phylogenies grows exponentially with the number of taxa analyzed and the minimum evolution principle is known to belong to the NRho- hard class of problems. RESULTS: In this paper, we introduce an Ant Colony Optimization (ACO) algorithm to estimate phylogenies under the minimum evolution principle. ACO is an optimization technique inspired from the foraging behavior of real ant colonies. This behavior is exploited in artificial ant colonies for the search of approximate solutions to discrete optimization problems. CONCLUSION: We show that the ACO algorithm is potentially competitive in comparison with state-of-the-art algorithms for the minimum evolution principle. This is the first application of an ACO algorithm to the phylogenetic estimation problem.
BACKGROUND: Distance matrix methods constitute a major family of phylogenetic estimation methods, and the minimum evolution (ME) principle (aiming at recovering the phylogeny with shortest length) is one of the most commonly used optimality criteria for estimating phylogenetic trees. The major difficulty for its application is that the number of possible phylogenies grows exponentially with the number of taxa analyzed and the minimum evolution principle is known to belong to the NRho- hard class of problems. RESULTS: In this paper, we introduce an Ant Colony Optimization (ACO) algorithm to estimate phylogenies under the minimum evolution principle. ACO is an optimization technique inspired from the foraging behavior of real ant colonies. This behavior is exploited in artificial ant colonies for the search of approximate solutions to discrete optimization problems. CONCLUSION: We show that the ACO algorithm is potentially competitive in comparison with state-of-the-art algorithms for the minimum evolution principle. This is the first application of an ACO algorithm to the phylogenetic estimation problem.