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An improved hyperparameter optimization framework for AutoML systems using evolutionary algorithms

For any machine learning model, finding the optimal hyperparameter setting has a direct and significant impact on the model’s performance. In this paper, we discuss different types of hyperparameter optimization techniques. We compare the performance of some of the hyperparameter optimization techni...

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Autores principales: Vincent, Amala Mary, Jidesh, P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036546/
https://www.ncbi.nlm.nih.gov/pubmed/36959245
http://dx.doi.org/10.1038/s41598-023-32027-3
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author Vincent, Amala Mary
Jidesh, P.
author_facet Vincent, Amala Mary
Jidesh, P.
author_sort Vincent, Amala Mary
collection PubMed
description For any machine learning model, finding the optimal hyperparameter setting has a direct and significant impact on the model’s performance. In this paper, we discuss different types of hyperparameter optimization techniques. We compare the performance of some of the hyperparameter optimization techniques on image classification datasets with the help of AutoML models. In particular, the paper studies Bayesian optimization in depth and proposes the use of genetic algorithm, differential evolution and covariance matrix adaptation—evolutionary strategy for acquisition function optimization. Moreover, we compare these variants of Bayesian optimization with conventional Bayesian optimization and observe that the use of covariance matrix adaptation—evolutionary strategy and differential evolution improves the performance of standard Bayesian optimization. We also notice that Bayesian optimization tends to perform poorly when genetic algorithm is used for acquisition function optimization.
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spelling pubmed-100365462023-03-25 An improved hyperparameter optimization framework for AutoML systems using evolutionary algorithms Vincent, Amala Mary Jidesh, P. Sci Rep Article For any machine learning model, finding the optimal hyperparameter setting has a direct and significant impact on the model’s performance. In this paper, we discuss different types of hyperparameter optimization techniques. We compare the performance of some of the hyperparameter optimization techniques on image classification datasets with the help of AutoML models. In particular, the paper studies Bayesian optimization in depth and proposes the use of genetic algorithm, differential evolution and covariance matrix adaptation—evolutionary strategy for acquisition function optimization. Moreover, we compare these variants of Bayesian optimization with conventional Bayesian optimization and observe that the use of covariance matrix adaptation—evolutionary strategy and differential evolution improves the performance of standard Bayesian optimization. We also notice that Bayesian optimization tends to perform poorly when genetic algorithm is used for acquisition function optimization. Nature Publishing Group UK 2023-03-23 /pmc/articles/PMC10036546/ /pubmed/36959245 http://dx.doi.org/10.1038/s41598-023-32027-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Vincent, Amala Mary
Jidesh, P.
An improved hyperparameter optimization framework for AutoML systems using evolutionary algorithms
title An improved hyperparameter optimization framework for AutoML systems using evolutionary algorithms
title_full An improved hyperparameter optimization framework for AutoML systems using evolutionary algorithms
title_fullStr An improved hyperparameter optimization framework for AutoML systems using evolutionary algorithms
title_full_unstemmed An improved hyperparameter optimization framework for AutoML systems using evolutionary algorithms
title_short An improved hyperparameter optimization framework for AutoML systems using evolutionary algorithms
title_sort improved hyperparameter optimization framework for automl systems using evolutionary algorithms
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036546/
https://www.ncbi.nlm.nih.gov/pubmed/36959245
http://dx.doi.org/10.1038/s41598-023-32027-3
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