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A Multi-objective Evolutionary Approach for Phylogenetic Inference

  • Conference paper
Evolutionary Multi-Criterion Optimization (EMO 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4403))

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Abstract

The phylogeny reconstruction problem consists of determining the most accurate tree that represents evolutionary relationships among species. Different criteria have been employed to evaluate possible solutions in order to guide a search algorithm towards the best tree. However, these criteria may lead to distinct phylogenies, which are often conflicting among them. In this context, a multi-objective approach can be useful since it could produce a spectrum of equally optimal trees (Pareto front) according to all criteria. We propose a multi-objective evolutionary algorithm, named PhyloMOEA, which employs the maximum parsimony and likelihood criteria to evaluate solutions. PhyloMOEA was tested using four datasets of nucleotide sequences. This algorithm found, for all datasets, a Pareto front representing a trade-off between the criteria. Moreover, SH-test showed that most of solutions have scores similar to those obtained by phylogenetic programs using one criterion.

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Authors and Affiliations

  1. Institute of Mathematics and Computer Science, University of Sao Paulo, Sao Carlos, SP. 13560–970, Brazil

    Waldo Cancino & Alexandre C. B. Delbem

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  1. Waldo Cancino
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  2. Alexandre C. B. Delbem
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Shigeru Obayashi Kalyanmoy Deb Carlo Poloni Tomoyuki Hiroyasu Tadahiko Murata

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Cancino, W., Delbem, A.C.B. (2007). A Multi-objective Evolutionary Approach for Phylogenetic Inference. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds) Evolutionary Multi-Criterion Optimization. EMO 2007. Lecture Notes in Computer Science, vol 4403. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70928-2_34

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  • DOI: https://doi.org/10.1007/978-3-540-70928-2_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-70927-5

  • Online ISBN: 978-3-540-70928-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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