Enhancing Surrogate-Based Optimization Through Parallelization

Language: English

Published by Springer, 2024

3031306112 / 9783031306112

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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH

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Druck auf Anfrage Neuware - Printed after ordering - 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 about how to allocate resources and optimize their models efficiently.…

Seller Inventory # 9783031306112

Title
Enhancing Surrogate-Based Optimization Through Parallelization
Author
Frederik Rehbach
Publisher
Springer
Publication year
2024
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
3031306112
ISBN 13
9783031306112
Item weight
230 grams
Dimensions
235x155x7 mm

AHA-BUCH GmbH

Einbeck, Germany

5-star seller

AbeBooks seller since August 14, 2006

Shipping rates from Germany to U.S.A.

Item7 to 10 business days5 to 7 business days
First itemUS$ 39.21US$ 50.41
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