Overview
- Presents the state of the art in designing high-performance algorithms that combine machine learning and optimization in order to solve complex problems
- Provides theoretical treatments and real-world insights gained by the contributing authors, all of whom are leading researchers
- Offers a comprehensive reference guide for researchers, practitioners, and advanced-level students interested in the theory and practice of using computational intelligence to solve expensive optimization problems
Part of the book series: Studies in Computational Intelligence (SCI, volume 833)
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About this book
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.
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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Keywords
Table of contents (12 chapters)
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Front Matter
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Many-Objective Optimization
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Front Matter
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Surrogate-Based Optimization
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Front Matter
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Parallel Optimization
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Front Matter
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Editors and Affiliations
Bibliographic Information
Book Title: High-Performance Simulation-Based Optimization
Editors: Thomas Bartz-Beielstein, Bogdan Filipič, Peter Korošec, El-Ghazali Talbi
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-030-18764-4
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-18763-7Published: 14 June 2019
Softcover ISBN: 978-3-030-18766-8Published: 14 August 2020
eBook ISBN: 978-3-030-18764-4Published: 01 June 2019
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XIII, 291
Number of Illustrations: 24 b/w illustrations, 47 illustrations in colour
Topics: Computational Intelligence, Control and Systems Theory