Phylogeny Inference Using a Multi-objective Evolutionary Algorithm with Indirect Representation

Authors:
Md. Rafiul Hassan;M. Maruf Hossain;C. K. Karmakar;Michael Kirley
Affiliations:
Department of Computer Science and Software Engineering, The University of Melbourne, Australia;Department of Computer Science and Software Engineering, The University of Melbourne, Australia;Department of Electrical & Electronic Engineering, The University of Melbourne, Australia;Department of Computer Science and Software Engineering, The University of Melbourne, Australia
Venue:
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Year:
2008

Citing 4
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Abstract

The inference of phylogenetic trees is one of the most important tasks in computational biology. In this paper, we propose an extension to multi-objective evolutionary algorithms to address this problem. Here, we adopt an enhanced indirect encoding for a tree using the corresponding Prüfer code represented in Newick format. The algorithm generates a range of non-dominated trees given alternative fitness measures such as statistical likelihood and maximum parsimony. A key feature of this approach is the preservation of the evolutionary hierarchy between species. Preliminary experimental results indicate that our model is capable of generating a set of optimized phylogenetic trees for given species data and the results are comparable with other techniques.