High-Performance Simulation-Based Optimization

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149,79 

Studies in Computational Intelligence 833

ISBN: 3030187632
ISBN 13: 9783030187637
Herausgeber: Thomas Bartz-Beielstein/Bogdan Filipic/Peter Korosec et al
Verlag: Springer Verlag GmbH
Umfang: xiii, 291 S., 24 s/w Illustr., 47 farbige Illustr., 291 p. 71 illus., 47 illus. in color.
Erscheinungsdatum: 14.06.2019
Auflage: 1/2020
Produktform: Gebunden/Hardback
Einband: Gebunden

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.

Artikelnummer: 7067501 Kategorie:

Beschreibung

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. Thats 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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E-Mail: juergen.hartmann@springer.com

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