UID:
almahu_9949115079502882
Umfang:
XXV, 393 p. 159 illus., 76 illus. in color.
,
online resource.
Ausgabe:
1st ed. 2021.
ISBN:
9783030746407
Serie:
Studies in Computational Intelligence, 975
Inhalt:
Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics. Written by world-leading academic researchers who are extremely experienced in industrial applications, this self-contained book is the first of its kind that provides comprehensive background knowledge, particularly practical guidelines, and state-of-the-art techniques. New algorithms are carefully explained, further elaborated with pseudocode or flowcharts, and full working source code is made freely available. This is followed by a presentation of a variety of data-driven single- and multi-objective optimization algorithms that seamlessly integrate modern machine learning such as deep learning and transfer learning with evolutionary and swarm optimization algorithms. Applications of data-driven optimization ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included.
Anmerkung:
Introduction to Optimization -- Classical Optimization Algorithms -- Evolutionary and Swarm Optimization -- Introduction to Machine Learning -- Data-Driven Surrogate-Assisted Evolutionary Optimization -- Multi-Surrogate-Assisted Single-Objective Optimization -- Surrogate-Assisted Multi-Objective Evolutionary Optimization.
In:
Springer Nature eBook
Weitere Ausg.:
Printed edition: ISBN 9783030746391
Weitere Ausg.:
Printed edition: ISBN 9783030746414
Weitere Ausg.:
Printed edition: ISBN 9783030746421
Sprache:
Englisch
Fachgebiete:
Informatik
DOI:
10.1007/978-3-030-74640-7
URL:
https://doi.org/10.1007/978-3-030-74640-7
URL:
Volltext
(URL des Erstveröffentlichers)
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