Enhancing Surrogate-Based Optimization Through Parallelization
Seiten
2024
|
2023
Springer International Publishing (Verlag)
978-3-031-30611-2 (ISBN)
Springer International Publishing (Verlag)
978-3-031-30611-2 (ISBN)
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.
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.
Introduction.- Background.- Methods/Contributions.- Application.- Final Evaluation.
| Erscheinungsdatum | 01.06.2024 |
|---|---|
| Reihe/Serie | Studies in Computational Intelligence |
| Zusatzinfo | X, 115 p. 33 illus., 26 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Technik | |
| Schlagworte | Computational Intelligence • data engineering • Parallelization • SBO • surrogate-based optimization |
| ISBN-10 | 3-031-30611-2 / 3031306112 |
| ISBN-13 | 978-3-031-30611-2 / 9783031306112 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
| Haben Sie eine Frage zum Produkt? |
Mehr entdecken
aus dem Bereich
aus dem Bereich
Buch | Softcover (2024)
REDLINE (Verlag)
CHF 27,95
Eine kurze Geschichte der Informationsnetzwerke von der Steinzeit bis …
Buch | Hardcover (2024)
Penguin (Verlag)
CHF 39,20
was sie kann & was uns erwartet
Buch | Softcover (2023)
C.H.Beck (Verlag)
CHF 25,20
