A tutorial on the balanced minimum evolution problem

Catanzaro, Daniele; Frohn, Martin; Gascuel, Olivier; Pesenti, Raffaele

doi:10.1016/j.ejor.2021.08.004

Cited by 4 publications

(2 citation statements)

References 137 publications

(287 reference statements)

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“…Distance methods constitute a well-consolidated theoretical and algorithmic framework to carry out practical phylogenetic analyses. These methods are typically based on hypotheses and assumptions that are considerably simpler than those at the core of more sophisticated estimation methods, such as Maximum Likelihood (ML) or Bayesian Inference (BI), and this fact can make them poor at modelling complex evolutionary processes ( Schwartz 2019 , Catanzaro et al 2022 ). However, off-the-shelf distance methods can run with almost any kind of data for which a dissimilarity measure is available and are computationally less demanding than more sophisticated estimation methods based on ML and BI.…”

Section: Introductionmentioning

confidence: 99%

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An evolution strategy approach for the balanced minimum evolution problem

Gasparin,

Camerota Verdù,

Catanzaro

et al. 2023

Bioinformatics

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Motivation The Balanced Minimum Evolution (BME) is a powerful distance based phylogenetic estimation model introduced by Desper and Gascuel and nowadays implemented in popular tools for phylogenetic analyses. It was proven to be computationally less demanding than more sophisticated estimation methods, e.g. maximum likelihood or Bayesian inference while preserving the statistical consistency and the ability to run with almost any kind of data for which a dissimilarity measure is available. BME can be stated in terms of a nonlinear non-convex combinatorial optimisation problem, usually referred to as the Balanced Minimum Evolution Problem (BMEP). Currently, the state-of-the-art among approximate methods for the BMEP is represented by FastME (version 2.0), a software which implements several deterministic phylogenetic construction heuristics combined with a local search on specific neighbourhoods derived by classical topological tree rearrangements. These combinations, however, may not guarantee convergence to close-to-optimal solutions to the problem due to the lack of solution space exploration, a phenomenon which is exacerbated when tackling molecular datasets characterised by a large number of taxa. Results To overcome such convergence issues, in this article we propose a novel metaheuristic, named PhyloES, which exploits the combination of an exploration phase based on Evolution Strategies, a special type of evolutionary algorithm, with a refinement phase based on two local search algorithms. Extensive computational experiments show that PhyloES consistently outperforms FastME, especially when tackling larger datasets, providing solutions characterised by a shorter tree length but also significantly different from the topological perspective. Availability The software is available at https://github.com/andygaspar/PHYLOES Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Introductionmentioning

confidence: 99%

“…The BMEP, recently reviewed in Catanzaro et al (2022) , is

-hard and inapproximable within a

-factor, for some positive constant

, unless

( Fiorini and Joret 2012 ). The BMEP is instead polynomially solvable if the input distance matrix

is additive ( Gascuel 2005 ), i.e.…”

Section: Introductionmentioning

confidence: 99%