Abstract
BackgroundDistance 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 -hard class of problems.ResultsIn 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.ConclusionWe 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.
Highlights
Distance matrix methods constitute a major family of phylogenetic estimation methods, and the minimum evolution (ME) principle is one of the most commonly used optimality criteria for estimating phylogenetic trees
Iterative addition Given a set Γ of taxa, let us define a partial tree as a m-leaf tree whose leaves are taxa of a subset Γ' ⊂ Γ, with m = |Γ'|
Each artificial ant r generates a complete phylogenetic tree using the ConstructCompleteReconstruction(r) procedure, as illustrated in Figure 3: ant r randomly selects four leaves from the set Γ, and builds a partial tree k, k = 4, ant r (i) chooses, among the leaves not yet inserted in the partial topology, the leaf i defining the smallest distance dij, j ∈ Γ k, and (ii) computes the probability that i has a common ancestor with the vertex j ∈ V ( k ) using the formula suggested by Approximate Nondeterministic Tree Search (ANTS) [32]: HFiighu-rleev3el pseudo-code for the Ant Colony Optimization (ACO) algorithm High-level pseudo-code for the ACO algorithm
Summary
Published: 15 November 2007 BMC Evolutionary Biology 2007, 7:228 doi:10.1186/1471-2148-7-228
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