High-Performance Simulation-Based Optimization (Hardcover, 1st ed. 2020)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Studies in Computational Intelligence, 833
Release date:	June 2019
First published:	2020
Editors:	Thomas Bartz-beielstein • Bogdan Filipic • Peter Korosec • El--Ghazali Talbi
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	291
Edition:	1st ed. 2020
ISBN-13:	978-3-03-018763-7
Categories:	Books > Computing & IT > Applications of computing > Computer modelling & simulation Books > Science & Mathematics > Mathematics > Optimization > General Books > Computing & IT > Applications of computing > Artificial intelligence > General Promotions
LSN:	3-03-018763-2
Barcode:	9783030187637

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