### Abstract

The maximum likelihood (ML) method of phylogenetic tree construction is not as widely used as other tree construction methods (e.g., parsimony, neighbor-joining) because of the prohibitive amount of time required to find the ML tree when the number of sequences under consideration is large. To overcome this difficulty, we propose a stochastic search strategy for estimation of the ML tree that is based on a simulated annealing algorithm. The algorithm works by moving through tree space by way of a "local rearrangement" strategy so that topologies that improve the likelihood are always accepted, whereas those that decrease the likelihood are accepted with a probability that is related to the proportionate decrease in likelihood. Besides greatly reducing the time required to estimate the ML tree, the stochastic search strategy is less likely to become trapped in local optima than are existing algorithms for ML tree estimation. We demonstrate the success of the modified simulated annealing algorithm by comparing it with two existing algorithms (Swofford's PAUP* and Felsenstein's DNAMLK) for several theoretical and real data examples.

Original language | English (US) |
---|---|

Pages (from-to) | 7-17 |

Number of pages | 11 |

Journal | Systematic Biology |

Volume | 50 |

Issue number | 1 |

State | Published - Feb 1 2001 |

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### All Science Journal Classification (ASJC) codes

- Ecology, Evolution, Behavior and Systematics
- Genetics

### Cite this

*Systematic Biology*,

*50*(1), 7-17.

}

*Systematic Biology*, vol. 50, no. 1, pp. 7-17.

**Stochastic search strategy for estimation of maximum likelihood phylogenetic trees.** / Salter, Laura A.; Pearl, Dennis Keith.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Stochastic search strategy for estimation of maximum likelihood phylogenetic trees

AU - Salter, Laura A.

AU - Pearl, Dennis Keith

PY - 2001/2/1

Y1 - 2001/2/1

N2 - The maximum likelihood (ML) method of phylogenetic tree construction is not as widely used as other tree construction methods (e.g., parsimony, neighbor-joining) because of the prohibitive amount of time required to find the ML tree when the number of sequences under consideration is large. To overcome this difficulty, we propose a stochastic search strategy for estimation of the ML tree that is based on a simulated annealing algorithm. The algorithm works by moving through tree space by way of a "local rearrangement" strategy so that topologies that improve the likelihood are always accepted, whereas those that decrease the likelihood are accepted with a probability that is related to the proportionate decrease in likelihood. Besides greatly reducing the time required to estimate the ML tree, the stochastic search strategy is less likely to become trapped in local optima than are existing algorithms for ML tree estimation. We demonstrate the success of the modified simulated annealing algorithm by comparing it with two existing algorithms (Swofford's PAUP* and Felsenstein's DNAMLK) for several theoretical and real data examples.

AB - The maximum likelihood (ML) method of phylogenetic tree construction is not as widely used as other tree construction methods (e.g., parsimony, neighbor-joining) because of the prohibitive amount of time required to find the ML tree when the number of sequences under consideration is large. To overcome this difficulty, we propose a stochastic search strategy for estimation of the ML tree that is based on a simulated annealing algorithm. The algorithm works by moving through tree space by way of a "local rearrangement" strategy so that topologies that improve the likelihood are always accepted, whereas those that decrease the likelihood are accepted with a probability that is related to the proportionate decrease in likelihood. Besides greatly reducing the time required to estimate the ML tree, the stochastic search strategy is less likely to become trapped in local optima than are existing algorithms for ML tree estimation. We demonstrate the success of the modified simulated annealing algorithm by comparing it with two existing algorithms (Swofford's PAUP* and Felsenstein's DNAMLK) for several theoretical and real data examples.

UR - http://www.scopus.com/inward/record.url?scp=0035527377&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0035527377&partnerID=8YFLogxK

M3 - Article

C2 - 12116596

AN - SCOPUS:0035527377

VL - 50

SP - 7

EP - 17

JO - Systematic Biology

JF - Systematic Biology

SN - 1063-5157

IS - 1

ER -