High-Performance Simulation-Based Optimization: 833 - Rilegato

Rilegato

ISBN 10: 3030187632 ISBN 13: 9783030187637

Casa editrice: Springer-Nature New York Inc, 2019

Riassunto

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.   

Su questo libro

Dalla quarta di copertina

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.   

Dettagli dell'edizione

• Editore: Springer-Nature New York Inc
• Data di pubblicazione: 2019
• Lingua: Inglese
• ISBN 10: 3030187632
• ISBN 13: 9783030187637
• Rilegatura: Copertina rigida
• Numero edizione: 1
• Numero di pagine: 291
• Redattore: Bartz-beielstein Thomas, Filipic Bogdan, Koroec Peter, Talbi El-ghazali
• Contatto del produttore: non disponibile
• Persona responsabile: CMN Printpool Inh. Mathis Neumann; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; Germania