What might Evolutionary Algorithms ( EA ) and Multi-objective Optimisation ( MOO ) Contribute to Phylogenetics and the Total Evidence Debate

Evolutionary relationships among species are usually (i) illustrated by means of a phylogeny and (ii) inferred by optimising some measure of fitness, such as the total evolutionary distance between species (given the tree), the parsimony (number of different assumptions required to fit the data to the tree), the likelihood of the tree (given an evolutionary model and a data set) or the posterior probability of the tree (given an evolutionary model, a data set, and the distribution of prior probabilities). A large variety of different types of data can and have been used in reconstructing evolutionary relationships including nucleotide sequences, anatomical features, metabolic processes, behaviour or even the words of languages. Difficulties arise when different sources of evidence provide conflicting information about the inferred ‘best’ tree(s). The Total Evidence Debate focusses on how to assess, combine, modify or reject different types of data. We begin with a review of evolutionary algorithms (EA) used for phylogenetic inference. Then we discuss whether the population-based searches that are an intrinsic attribute of EA and multi-objective optimisation (MOO) can provide a powerful new approach to this area.

[1]  Atte Moilanen,et al.  Simulated Evolutionary Optimization and Local Search: Introduction and Application to Tree Search , 2001 .

[2]  N. Pierce.  Origin of Species , 1914, Nature.

[3]  D. Penny,et al.  Branch and bound algorithms to determine minimal evolutionary trees , 1982 .

[4]  Atte Moilanen,et al.  Searching for Most Parsimonious Trees with Simulated Evolutionary Optimization , 1999 .

[5]  K. Kidd,et al.  Phylogenetic analysis: concepts and methods. , 1971, American journal of human genetics.

[6]  P. Goloboff.  Analyzing Large Data Sets in Reasonable Times: Solutions for Composite Optima , 1999, Cladistics : the international journal of the Willi Hennig Society.

[7]  David Sankoff,et al.  Locating the vertices of a steiner tree in an arbitrary metric space , 1975, Math. Program..

[8]  J. Farris.  Methods for Computing Wagner Trees , 1970 .

[9]  W. Hennig.  Grundzüge einer Theorie der phylogenetischen Systematik , 1950 .

[10]  J. Farris.  A Successive Approximations Approach to Character Weighting , 1969 .

[11]  Faisal Ababneh,et al.  The biasing effect of compositional heterogeneity on phylogenetic estimates may be underestimated. , 2004, Systematic biology.

[12]  David S. Gladstein,et al.  Efficient Incremental Character Optimization , 1997, Cladistics : the international journal of the Willi Hennig Society.

[13]  Clare Bates Congdon.  Gaphyl: An Evolutionary Algorithms Approach For The Study Of Natural Evolution , 2002, GECCO.

[14]  J. Felsenstein.  Cases in which Parsimony or Compatibility Methods will be Positively Misleading , 1978 .

[15]  J. Farris,et al.  Quantitative Phyletics and the Evolution of Anurans , 1969 .

[16]  G. Olsen,et al.  Majority-Rule Consensus of Phylogenetic Trees Obtained by Maximum-Likelihood Analysis , 1997 .

[17]  Daniel R. Brooks,et al.  The Nature of Diversity: An Evolutionary Voyage of Discovery , 2002 .

[18]  Brendan D. McKay,et al.  TrExML: a maximum-likelihood approach for extensive tree-space exploration , 2000, Bioinform..

[19]  Yong-Hyuk Kim,et al.  Optimizing the Order of Taxon Addition in Phylogenetic Tree Construction Using Genetic Algorithm , 2003, GECCO.

[20]  S. Ho,et al.  Tracing the decay of the historical signal in biological sequence data. , 2004, Systematic biology.

[21]  W. Fitch.  Toward Defining the Course of Evolution: Minimum Change for a Specific Tree Topology , 1971 .

[22]  M. Nei,et al.  The optimization principle in phylogenetic analysis tends to give incorrect topologies when the number of nucleotides or amino acids used is small. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[23]  P. Lewis,et al.  A genetic algorithm for maximum-likelihood phylogeny inference using nucleotide sequence data. , 1998, Molecular biology and evolution.

[24]  W. Hennig.  Phylogenetic Systematics , 2002 .

[25]  H Matsuda,et al.  Protein phylogenetic inference using maximum likelihood with a genetic algorithm. , 1996, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[26]  A. Kluge,et al.  Testability and the Refutation and Corroboration of Cladistic Hypotheses , 1997, Cladistics : the international journal of the Willi Hennig Society.

[27]  M. Miyamoto,et al.  CONSENSUS CLADOGRAMS AND GENERAL CLASSIFICATIONS , 1985, Cladistics : the international journal of the Willi Hennig Society.

[28]  K. Nixon,et al.  The Parsimony Ratchet, a New Method for Rapid Parsimony Analysis , 1999, Cladistics : the international journal of the Willi Hennig Society.

[29]  Junhyong Kim,et al.  Separate Versus Combined Analysis of Phylogenetic Evidence , 1995 .