Faster Phylogenetic Inference with MXG

  • David G. Mitchell
  • Faraz Hach
  • Raheleh Mohebali
Conference paper

DOI: 10.1007/978-3-540-75560-9_31

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4790)
Cite this paper as:
Mitchell D.G., Hach F., Mohebali R. (2007) Faster Phylogenetic Inference with MXG. In: Dershowitz N., Voronkov A. (eds) Logic for Programming, Artificial Intelligence, and Reasoning. LPAR 2007. Lecture Notes in Computer Science, vol 4790. Springer, Berlin, Heidelberg

Abstract

We apply the logic-based declarative programming approach of Model Expansion (MX) to a phylogenetic inference task. We axiomatize the task in multi-sorted first-order logic with cardinality constraints. Using the model expansion solver MXG and SAT+cardinality solver MXC, we compare the performance of several MX axiomatizations on a challenging set of test instances. Our methods perform orders of magnitude faster than previously reported declarative solutions. Our best solution involves polynomial-time pre-processing, redundant axioms, and symmetry-breaking axioms. We also discuss our method of test instance generation, and the role of pre-processing in declarative programming.

Keywords

Phylogeny Declarative Programming Model Expansion 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • David G. Mitchell
    • 1
  • Faraz Hach
    • 1
  • Raheleh Mohebali
    • 1
  1. 1.Computational Logic Laboratory, Simon Fraser University, Burnaby BCCanada

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