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Enhancing Surrogate-Based Optimization Through Parallelization (Studies in Computational Intelligence, 1099) - Hardcover
- PublisherSpringer
- Publication date2023
- ISBN 10 3031306082
- ISBN 13 9783031306082
- BindingHardcover
- LanguageEnglish
- Number of pages125
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Enhancing Surrogate-Based Optimization Through Parallelization
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Springer, Berlin|Springer Nature Switzerland|Springer, 2023
ISBN 10: 3031306082
ISBN 13: 9783031306082
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Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 (S. Seller Inventory # 834045565
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Enhancing Surrogate-Based Optimization Through Parallelization (Studies in Computational Intelligence, 1099)
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Enhancing Surrogate-Based Optimization Through Parallelization
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Springer Nature Switzerland, Springer International Publishing Mai 2023, 2023
ISBN 10: 3031306082
ISBN 13: 9783031306082
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 128 pp. Englisch. Seller Inventory # 9783031306082
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Enhancing Surrogate-Based Optimization Through Parallelization
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Springer Nature Switzerland, Springer Nature Switzerland, 2023
ISBN 10: 3031306082
ISBN 13: 9783031306082
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Seller: AHA-BUCH GmbH, Einbeck, Germany
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Buch. Condition: Neu. 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 # 9783031306082
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Enhancing Surrogate-Based Optimization Through Parallelization (Hardcover)
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ISBN 10: 3031306082
ISBN 13: 9783031306082
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Hardcover. Condition: new. Hardcover. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9783031306082
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Enhancing Surrogate-Based Optimization Through Parallelization (Volume 1099)
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Enhancing Surrogate-Based Optimization Through Parallelization (Volume 1099)
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Enhancing Surrogate-Based Optimization Through Parallelization (Volume 1099)
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Enhancing Surrogate-based Optimization Through Parallelization
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Hardcover. Condition: Brand New. 125 pages. 9.25x6.10x0.51 inches. In Stock. Seller Inventory # x-3031306082
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