title: Parsimony accelerated maximum likelihood searches.
abstract: Phylogenetic search is a key tool used in a variety of biological research endeavours. However, this search problem is known to be computationally difficult, due to the astronomically large search space, making the use of heuristic methods necessary. The performance of heuristic methods for finding Maximum Likelihood (ML) trees can be improved by using parsimony as an initial estimator for ML. The time spent in performing the parsimony search to boost performance is insignificant compared to the time spent in the ML search, leading to an overall gain in search time. These parsimony boosted ML searches lead to topologies with scores statistically similar to the unboosted searches, but in less time.
authors.affiliation: Computer Science Department, Brigham Young University, USA. kasundberg@gmail.com
authors.forename: Kenneth
authors.initials: K
authors.lastname: Sundberg
authors.forename: Timothy
authors.initials: T
authors.lastname: O'Connor
authors.forename: Hyrum
authors.initials: H
authors.lastname: Carroll
authors.forename: Mark
authors.initials: M
authors.lastname: Clement
authors.forename: Quinn
authors.initials: Q
authors.lastname: Snell
mesh_terms: D019295:Computational Biology; D003198:Computer Simulation; D030541:Databases, Genetic; D016013:Likelihood Functions; D008957:Models, Genetic; D010802:Phylogeny; D016415:Sequence Alignment / Q000706:statistics & numerical data; D012984:Software