Accelerating Maximum Likelihood Phylogenetic Inference via Early Stopping to Evade (Over-)optimization

Maximum likelihood-based phylogenetic inference constitutes a challenging optimization problem. Given a set of aligned input sequences, phylogenetic inference tools strive to determine the tree topology, the branch lengths, and the evolutionary model parameters that maximize the phylogenetic likelihood function. However, there exist compelling reasons to not push optimization to its limits, by means of early, yet adequate stopping criteria. Because input sequences are typically subject to stochastic and systematic noise, caution is warranted to prevent overoptimization and the risk of overfitting the model to noisy data. To address this, we integrate the Kishino-Hasegawa (KH) test into RAxML-NG as a reliable and fast-to-compute Early Stopping criterion to effectively limit excessive and compute-intensive overoptimization. Initially, we introduce a simplified heuristic tree search strategy in RAxML-NG (sRAxML-NG) as an underlying method for Early Stopping. Subsequently, we use the KH test in combination with sRAxML-NG to statistically assess the significance of differences between intermediate trees prior to and after major optimization steps. The tree search terminates early when improvements are statistically insignificant. We also propose an extension to the standard KH test that allows to correct for multiple testing, which maintains accuracy while achieving even higher speedups. For benchmarking, we use 300 large representative empirical data sets from TreeBASE. For 98% of the DNA data sets, all Early Stopping methods we introduce infer trees that are statistically equivalent to those inferred from RAxML-NG v1.2. For AA data sets, the fraction of data sets where sRAxML-NG, KH, and the KH-multiple testing versions infer statistically equivalent trees is 96%, 95%, and 92%, respectively. In conjunction with sRAxML-NG, the average speedup achieved by the KH-multiple testing version is 5× for DNA and 3.9× for protein data sets compared with RAxML-NG v1.2. We implemented our stopping criteria in RAxML-NG, which is available under GNU GPL at https://github.com/togkousa/raxml-ng/tree/stopping-criteria.

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Publication Details

Journal
Systematic Biology
Published
2025-05-30
DOI
https://doi.org/10.1093/sysbio/syaf043
Citations
3
Primary Topic
Genomics and Phylogenetic Studies
Type
article
Field-Weighted Citation Impact
2.42

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article

Accelerating Maximum Likelihood Phylogenetic Inference via Early Stopping to Evade (Over-)optimization

Anastasis Togkousidis, Alexandros Stamatakis, Olivier Gascuel
3 citations
Systematic Biology
Genomics and Phylogenetic Studies
2.42
article

Accelerating Maximum Likelihood Phylogenetic Inference via Early Stopping to Evade (Over-)optimization

Anastasis Togkousidis, Alexandros Stamatakis, Olivier Gascuel
article en
3 citations

Abstract

Maximum likelihood-based phylogenetic inference constitutes a challenging optimization problem. Given a set of aligned input sequences, phylogenetic inference tools strive to determine the tree topology, the branch lengths, and the evolutionary model parameters that maximize the phylogenetic likelihood function. However, there exist compelling reasons to not push optimization to its limits, by means of early, yet adequate stopping criteria. Because input sequences are typically subject to stochastic and systematic noise, caution is warranted to prevent overoptimization and the risk of overfitting the model to noisy data. To address this, we integrate the Kishino-Hasegawa (KH) test into RAxML-NG as a reliable and fast-to-compute Early Stopping criterion to effectively limit excessive and compute-intensive overoptimization. Initially, we introduce a simplified heuristic tree search strategy in RAxML-NG (sRAxML-NG) as an underlying method for Early Stopping. Subsequently, we use the KH test in combination with sRAxML-NG to statistically assess the significance of differences between intermediate trees prior to and after major optimization steps. The tree search terminates early when improvements are statistically insignificant. We also propose an extension to the standard KH test that allows to correct for multiple testing, which maintains accuracy while achieving even higher speedups. For benchmarking, we use 300 large representative empirical data sets from TreeBASE. For 98% of the DNA data sets, all Early Stopping methods we introduce infer trees that are statistically equivalent to those inferred from RAxML-NG v1.2. For AA data sets, the fraction of data sets where sRAxML-NG, KH, and the KH-multiple testing versions infer statistically equivalent trees is 96%, 95%, and 92%, respectively. In conjunction with sRAxML-NG, the average speedup achieved by the KH-multiple testing version is 5× for DNA and 3.9× for protein data sets compared with RAxML-NG v1.2. We implemented our stopping criteria in RAxML-NG, which is available under GNU GPL at https://github.com/togkousa/raxml-ng/tree/stopping-criteria.

Systematic BiologyVol. 74(6)
Karlsruhe Institute of Technology (DE), Centre National de la Recherche Scientifique (FR), Institut de Systématique, Évolution, Biodiversité (FR), Institute of Molecular Biology and Biotechnology (BG), Heidelberg Institute for Theoretical Studies (DE), Foundation for Research and Technology Hellas (GR)
European Commission, Deutsche Forschungsgemeinschaft, Agence Nationale de la Recherche, Klaus Tschira Stiftung
Openalex Percentile: Top 11%
Genomics and Phylogenetic Studies
2.42
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