Table of contents
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.
Computational Intelligence Many-Objective Optimization Surrogate-Based Optimization Parallel Optimization High-performance Algorithms Machine Learning
Editors and affiliations
- DOI https://doi.org/10.1007/978-3-030-18764-4
- Copyright Information Springer Nature Switzerland AG 2020
- Publisher Name Springer, Cham
- eBook Packages Intelligent Technologies and Robotics
- Print ISBN 978-3-030-18763-7
- Online ISBN 978-3-030-18764-4
- Series Print ISSN 1860-949X
- Series Online ISSN 1860-9503
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