Multi-objective evolutionary algorithms and phylogenetic inference with multiple data sets |
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Authors: | L Poladian LS Jermiin |
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Affiliation: | (1) School of Mathematics and Statistics, University of Sydney, NSW, 2006, Australia;(2) School of Biological Sciences, and the Sydney University Biological Informatics and Technology Centre, University of Sydney, NSW, 2006, Australia |
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Abstract: | Evolutionary relationships among species are usually (1) illustrated by means of a phylogenetic tree and (2) inferred by optimising
some measure of fitness, such as the total evolutionary distance between species or the likelihood of the tree (given a model
of the evolutionary process and a data set). The combinatorial complexity of inferring the topology of the best tree makes
phylogenetic inference an ideal candidate for evolutionary algorithms. However, difficulties arise when different data sets
provide conflicting information about the inferred `best' tree(s). We apply the techniques of multi-objective optimisation
to phylogenetic inference for the first time. We use the simplest model of evolution and a four species problem to illustrate
the method. |
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Keywords: | Phylogenetic inference Evolutionary algorithms Multiobjective optimisation |
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