Paper Info
Title
Applying a multiobjective metaheuristic inspired by honey bees to phylogenetic inference.
Abstract
The development of increasingly popular multiobjective metaheuristics has allowed bioinformaticians to deal with optimization problems in computational biology where multiple objective functions must be taken into account. One of the most relevant research topics that can benefit from these techniques is phylogenetic inference. Throughout the years, different researchers have proposed their own view about the reconstruction of ancestral evolutionary relationships among species. As a result, biologists often report different phylogenetic trees from a same dataset when considering distinct optimality principles. In this work, we detail a multiobjective swarm intelligence approach based on the novel Artificial Bee Colony algorithm for inferring phylogenies. The aim of this paper is to propose a complementary view of phylogenetics according to the maximum parsimony and maximum likelihood criteria, in order to generate a set of phylogenetic trees that represent a compromise between these principles. Experimental results on a variety of nucleotide data sets and statistical studies highlight the relevance of the proposal with regard to other multiobjective algorithms and state-of-the-art biological methods. (C) 2013 Elsevier Ireland Ltd. All rights reserved.
Year
DOI
Venue
2013
10.1016/j.biosystems.2013.07.001
Biosystems
Keywords
Field
DocType
Bioinspired computation,Computational biology,Swarm intelligence,Multiobjective optimization,Phylogeny reconstruction
Artificial bee colony algorithm,Maximum parsimony,Phylogenetic tree,Biology,Swarm intelligence,Multi-objective optimization,Artificial intelligence,Phylogenetics,Optimization problem,Machine learning,Metaheuristic
Journal
Volume
Issue
ISSN
114
1
0303-2647
Citations 
PageRank 
References 
12
0.63
25
Authors
2
Name
Order
Citations
PageRank
Sergio Santander-Jiménez15815.11
Miguel A. Vega-Rodríguez2741113.05