Produktnummer:
18f9dbc97b093a4e75a0ec890b47b15a91
| Themengebiete: | Computational Intelligence High-performance Algorithms Machine Learning Many-Objective Optimization Parallel Optimization Surrogate-Based Optimization |
|---|---|
| Veröffentlichungsdatum: | 14.08.2020 |
| EAN: | 9783030187668 |
| Sprache: | Englisch |
| Seitenzahl: | 291 |
| Produktart: | Kartoniert / Broschiert |
| Herausgeber: | Bartz-Beielstein, Thomas Filipic, Bogdan Korošec, Peter Talbi, El-Ghazali |
| Verlag: | Springer International Publishing |
Produktinformationen "High-Performance Simulation-Based Optimization"
This book presents the state of the art in designing high-performance algorithms that combine simulation and optimization in order to solve complex optimization problems in science and industry, problems that involve time-consuming simulations and expensive multi-objective function evaluations. As traditional optimization approaches are not applicable per se, combinations of computational intelligence, machine learning, and high-performance computing methods are popular solutions. But finding a suitable method is a challenging task, because numerous approaches have been proposed in this highly dynamic field of research. That’s where this book comes in: It covers both theory and practice, drawing on the real-world insights gained by the contributing authors, all of whom are leading researchers. Given its scope, if offers a comprehensive reference guide for researchers, practitioners, and advanced-level students interested in using computational intelligence and machine learning to solve expensive optimization problems.
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