Abstract
Phylogenetic Inference is considered as one of the most important research topics in the field of Bioinformatics. A variety of methods based on different optimality measures has been proposed in order to build and evaluate the trees which describe the evolution of species. A major problem that arises with this kind of techniques is the possibility of inferring discordant topologies from a same dataset. Another question to be resolved is how to manage the tree search process. As the space of possible topologies increases exponentially with the number of species in the input dataset, exhaustive methods cannot be applied. In this paper we propose a multiobjective adaptation of a well-known Swarm Intelligence algorithm, the Artificial Bee Colony, to reconstruct phylogenetic trees according to two criteria: maximum parsimony and maximum likelihood. Our approach shows a significant improvement in the quality of the inferred trees compared to other multiobjective proposals.
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Santander-Jiménez, S., Vega-Rodríguez, M.A., Gómez-Pulido, J.A., Sánchez-Pérez, J.M. (2012). Inferring Phylogenetic Trees Using a Multiobjective Artificial Bee Colony Algorithm. In: Giacobini, M., Vanneschi, L., Bush, W.S. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2012. Lecture Notes in Computer Science, vol 7246. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29066-4_13
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DOI: https://doi.org/10.1007/978-3-642-29066-4_13
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