An ant colony optimization algorithm for phylogenetic estimation under the minimum evolution principle

<p>Abstract</p> <p>Background</p> <p>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 crit...

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Main Authors: Milinkovitch Michel C, Pesenti Rafflaele, Catanzaro Daniele
Format: Article
Language:English
Published: BMC 2007-11-01
Series:BMC Evolutionary Biology
Online Access:http://www.biomedcentral.com/1471-2148/7/228
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spelling doaj-c77342e2e91b4619b558075f007784ec2021-09-02T02:38:06ZengBMCBMC Evolutionary Biology1471-21482007-11-017122810.1186/1471-2148-7-228An ant colony optimization algorithm for phylogenetic estimation under the minimum evolution principleMilinkovitch Michel CPesenti RafflaeleCatanzaro Daniele<p>Abstract</p> <p>Background</p> <p>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 <inline-formula><m:math xmlns:m="http://www.w3.org/1998/Math/MathML" name="1471-2148-7-228-i1"><m:semantics><m:mrow><m:mi mathvariant="script">N</m:mi><m:mi mathvariant="script">P</m:mi></m:mrow><m:annotation encoding="MathType-MTEF"> MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacPC6xNi=xH8viVGI8Gi=hEeeu0xXdbba9frFj0xb9qqpG0dXdb9aspeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGacaGaaiaabeqaaeqabiWaaaGcbaWenfgDOvwBHrxAJfwnHbqeg0uy0HwzTfgDPnwy1aaceaGae8xdX7Kaeeiuaafaaa@3888@</m:annotation></m:semantics></m:math></inline-formula>-hard class of problems.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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.</p> http://www.biomedcentral.com/1471-2148/7/228
collection DOAJ
language English
format Article
sources DOAJ
author Milinkovitch Michel C
Pesenti Rafflaele
Catanzaro Daniele
spellingShingle Milinkovitch Michel C
Pesenti Rafflaele
Catanzaro Daniele
An ant colony optimization algorithm for phylogenetic estimation under the minimum evolution principle
BMC Evolutionary Biology
author_facet Milinkovitch Michel C
Pesenti Rafflaele
Catanzaro Daniele
author_sort Milinkovitch Michel C
title An ant colony optimization algorithm for phylogenetic estimation under the minimum evolution principle
title_short An ant colony optimization algorithm for phylogenetic estimation under the minimum evolution principle
title_full An ant colony optimization algorithm for phylogenetic estimation under the minimum evolution principle
title_fullStr An ant colony optimization algorithm for phylogenetic estimation under the minimum evolution principle
title_full_unstemmed An ant colony optimization algorithm for phylogenetic estimation under the minimum evolution principle
title_sort ant colony optimization algorithm for phylogenetic estimation under the minimum evolution principle
publisher BMC
series BMC Evolutionary Biology
issn 1471-2148
publishDate 2007-11-01
description <p>Abstract</p> <p>Background</p> <p>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 <inline-formula><m:math xmlns:m="http://www.w3.org/1998/Math/MathML" name="1471-2148-7-228-i1"><m:semantics><m:mrow><m:mi mathvariant="script">N</m:mi><m:mi mathvariant="script">P</m:mi></m:mrow><m:annotation encoding="MathType-MTEF"> MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacPC6xNi=xH8viVGI8Gi=hEeeu0xXdbba9frFj0xb9qqpG0dXdb9aspeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGacaGaaiaabeqaaeqabiWaaaGcbaWenfgDOvwBHrxAJfwnHbqeg0uy0HwzTfgDPnwy1aaceaGae8xdX7Kaeeiuaafaaa@3888@</m:annotation></m:semantics></m:math></inline-formula>-hard class of problems.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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.</p>
url http://www.biomedcentral.com/1471-2148/7/228
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