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

  • Md. Rafiul Hassan
  • M. Maruf Hossain
  • C. K. Karmakar
  • Michael Kirley
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5361)

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.

Keywords

Phylogenetic Tree Maximum Parsimony Likelihood Score Parsimony Score Indirect Representation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Md. Rafiul Hassan
    • 1
  • M. Maruf Hossain
    • 1
  • C. K. Karmakar
    • 2
  • Michael Kirley
    • 1
  1. 1.Department of Computer Science and Software EngineeringThe University of MelbourneAustralia
  2. 2.Department of Electrical & Electronic EngineeringThe University of MelbourneAustralia

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