The metapopulation genetic algorithm: An efficient solution for the problem of large phylogeny estimation

Proc Natl Acad Sci U S A. 2002 Aug 6;99(16):10516-21. doi: 10.1073/pnas.162224399. Epub 2002 Jul 25.

Abstract

Large phylogeny estimation is a combinatorial optimization problem that no future computer will ever be able to solve exactly in practical computing time. The difficulty of the problem is amplified by the need to use complex evolutionary models and large taxon samplings. Hence, many heuristic approaches have been developed, with varying degrees of success. Here, we report on a heuristic approach, the metapopulation genetic algorithm, involving several populations of trees that are forced to cooperate in the search for the optimal tree. Within each population, trees are subjected to evaluation, selection, and mutation events, which are directed by using inter-population consensus information. The method proves to be both very accurate and vastly faster than existing heuristics, such that data sets comprised of hundreds of taxa can be analyzed in practical computing times under complex models of maximum-likelihood evolution. Branch support values produced by the metapopulation genetic algorithm might closely approximate the posterior probabilities of the corresponding branches.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Evolution, Molecular*
  • Phylogeny
  • Problem Solving