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Progressive Tree Neighborhood Applied to the Maximum Parsimony Problem

Adrien Goëffon 1, 2 Jean-Michel Richer 1, * Jin-Kao Hao 1
* Corresponding author
2 MAGNOME - Models and Algorithms for the Genome
Inria Bordeaux - Sud-Ouest, UB - Université de Bordeaux, CNRS - Centre National de la Recherche Scientifique : UMR5800
Abstract : The Maximum Parsimony problem aims at reconstructing a phylogenetic tree from DNA sequences while minimizing the number of genetic transformations. To solve this NP-complete problem, heuristic methods have been developed, often based on local search. In this article, we focus on the influence of the neighborhood relations. After analyzing the advantages and drawbacks of the well-known NNI, SPR and TBR neighborhoods, we introduce the concept of Progressive Neighborhood, which consists in constraining progressively the size of the neighborhood as the search advances. We empirically show that applied to the Maximum Parsimony problem, this progressive neighborhood turns out to be more efficient and robust than the classic neighborhoods using a descent algorithm. Indeed, it allows to find better solutions with a smaller number of iterations or trees evaluated.
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Submitted on : Wednesday, January 7, 2009 - 10:35:13 AM
Last modification on : Monday, June 1, 2020 - 4:54:02 PM

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Adrien Goëffon, Jean-Michel Richer, Jin-Kao Hao. Progressive Tree Neighborhood Applied to the Maximum Parsimony Problem. IEEE/ACM Transactions on Computational Biology and Bioinformatics, Institute of Electrical and Electronics Engineers, 2008, 5 (1), pp.136--145. ⟨10.1109/TCBB.2007.1065⟩. ⟨inria-00350539⟩

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