Data-Driven Evolutionary Optimization / Studies in Computational Intelligence Bd.975 (PDF)
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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.
- Autoren: Yaochu Jin , Handing Wang , Chaoli Sun
- 2021, 1st ed. 2021, 393 Seiten, Englisch
- Verlag: Springer Nature Switzerland
- ISBN-10: 3030746402
- ISBN-13: 9783030746407
- Erscheinungsdatum: 28.06.2021
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
- Dateiformat: PDF
- Größe: 13 MB
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