| ![](/p.gif) |
![](/p.gif) |
| Artikel-Nr.: 5667A-9783030187637 Herst.-Nr.: 9783030187637 EAN/GTIN: 9783030187637 |
| |
|
| ![](/p.gif) | ![](/p.gif) |
![](/p.gif) | 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. Weitere Informationen: ![](/p.gif) | ![](/p.gif) | Author: | Thomas Bartz-Beielstein; Bogdan Filipi?; Peter Koro?ec; El-Ghazali Talbi | Verlag: | Springer International Publishing | Sprache: | eng |
|
| ![](/p.gif) | ![](/p.gif) |
![](/p.gif) | | ![](/p.gif) | ![](/p.gif) |
![](/p.gif) | Weitere Suchbegriffe: maschinenbau und fertigungstechnik, ComputationalIntelligence; Many-ObjectiveOptimization; Surrogate-basedoptimization; ParallelOptimization; High-performanceAlgorithms; machinelearning, Computational Intelligence, Many-Objective Optimization, Surrogate-Based Optimization, Parallel Optimization, High-performance Algorithms, Machine Learning |
| ![](/p.gif) | ![](/p.gif) |
| |