Abstract
Recently, swarm intelligence algorithms have been applied successfully to a wide variety of optimization problems in Computational Biology. Phylogenetic inference represents one of the key research topics in this area. Throughout the years, controversy among biologists has arisen when dealing with this well-known problem, as different optimality criteria can give as a result discordant genealogical relationships. Current research efforts aim to apply multiobjective optimization techniques in order to infer phylogenies that represent a consensus between different principles. In this work, we apply a multiobjective swarm intelligence approach inspired by the behaviour of fireflies to tackle the phylogenetic inference problem according to two criteria: maximum parsimony and maximum likelihood. Experiments on four real nucleotide data sets show that this novel proposal can achieve promising results in comparison with other approaches from the state-of-the-art in Phylogenetics.
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Santander-Jiménez, S., Vega-Rodríguez, M.A. (2013). A Multiobjective Proposal Based on the Firefly Algorithm for Inferring Phylogenies. In: Vanneschi, L., Bush, W.S., Giacobini, M. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2013. Lecture Notes in Computer Science, vol 7833. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37189-9_13
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DOI: https://doi.org/10.1007/978-3-642-37189-9_13
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