High-Performance Simulation-Based Optimization

Thomas Bartz-Beielstein

ISBN 10: 3030187667 / ISBN 13: 9783030187668

Published by Springer International Publishing, Springer International Publishing Aug 2020, 2020

About this Item

Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 308 pp. Englisch.

Seller Inventory # 9783030187668

Bibliographic Details

• Title: High-Performance Simulation-Based Optimization
• Publisher: Springer International Publishing, Springer International Publishing Aug 2020
• Publication Date: 2020
• Language: English
• ISBN 10: 3030187667
• ISBN 13: 9783030187668
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

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.