Multi-objective evolutionary algorithms and phylogenetic inference with multiple data sets

Authors:
L. Poladian;L. S. Jermiin
Affiliations:
School of Mathematics and Statistics, University of Sydney, 2006, NSW, Australia;School of Biological Sciences, and the Sydney University Biological Informatics and Technology Centre, University of Sydney, 2006, NSW, Australia
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Year:
2006

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Proceedings of the 15th annual conference companion on Genetic and evolutionary computation

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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.