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Titlebook: Enhancing Surrogate-Based Optimization Through Parallelization; Frederik Rehbach Book 2023 The Editor(s) (if applicable) and The Author(s)

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發(fā)表于 2025-3-21 18:19:01 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Enhancing Surrogate-Based Optimization Through Parallelization
編輯Frederik Rehbach
視頻videohttp://file.papertrans.cn/312/311322/311322.mp4
概述Presents an in-depth analysis on parallel Surrogate-Based Optimization (SBO) algorithms.Introduces a novel benchmarking framework for the fair comparison of parallel SBO algorithms.Focuses on the appl
叢書名稱Studies in Computational Intelligence
圖書封面Titlebook: Enhancing Surrogate-Based Optimization Through Parallelization;  Frederik Rehbach Book 2023 The Editor(s) (if applicable) and The Author(s)
描述This book presents a solution to the challenging issue of optimizing expensive-to-evaluate industrial problems such as the hyperparameter tuning of machine learning models. The approach combines two well-established concepts, Surrogate-Based Optimization (SBO) and parallelization, to efficiently search for optimal parameter setups with as few function evaluations as possible..Through in-depth analysis, the need for parallel SBO solvers is emphasized, and it is demonstrated that they outperform model-free algorithms in scenarios with a low evaluation budget. The SBO approach helps practitioners save significant amounts of time and resources in hyperparameter tuning as well as other optimization projects. As a highlight, a novel framework for objectively comparing the efficiency of parallel SBO algorithms is introduced, enabling practitioners to evaluate and select the most effective approach for their specific use case..Based on practical examples, decision support is delivered, detailing which parts of industrial optimization projects can be parallelized and how to prioritize which parts to parallelize first. By following the framework, practitioners can make informed decisions abo
出版日期Book 2023
關(guān)鍵詞Computational Intelligence; Surrogate-based Optimization; SBO; Parallelization; Data Engineering
版次1
doihttps://doi.org/10.1007/978-3-031-30609-9
isbn_softcover978-3-031-30611-2
isbn_ebook978-3-031-30609-9Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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發(fā)表于 2025-3-21 23:07:26 | 只看該作者
Enhancing Surrogate-Based Optimization Through Parallelization
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Introduction,cost of computer experiments such as Computational Fluid Dynamics (CFD) simulations. In such a scenario, often only a few tens or hundreds of expensive functions evaluations are feasible due to a strict budget of time or money that has to be met. These hard limits require the use of very sample-efficient optimizers.
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發(fā)表于 2025-3-22 10:58:18 | 只看該作者
Book 2023chine learning models. The approach combines two well-established concepts, Surrogate-Based Optimization (SBO) and parallelization, to efficiently search for optimal parameter setups with as few function evaluations as possible..Through in-depth analysis, the need for parallel SBO solvers is emphasi
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發(fā)表于 2025-3-22 16:38:32 | 只看該作者
1860-949X ir comparison of parallel SBO algorithms.Focuses on the applThis book presents a solution to the challenging issue of optimizing expensive-to-evaluate industrial problems such as the hyperparameter tuning of machine learning models. The approach combines two well-established concepts, Surrogate-Base
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https://doi.org/10.1057/9781403914279literature where needed. The chapter continues by presenting a taxonomy for parallel SBO (Sect.?.) and giving recommendations for practitioners in the field of SBO. The chapter is concluded with a literature review covering existing SBO methods (Sect.?.).
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發(fā)表于 2025-3-23 06:34:14 | 只看該作者
https://doi.org/10.1007/978-1-349-12218-9to-evaluate functions. Rigorous methods for analyzing and assessing algorithm performance are required before any improvements can be made to optimization algorithms. Benchmarks and well-chosen test functions are essential to gain an unbiased insight into optimization algorithms.
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