Data-driven Evolutionary Optimization (Studies in Computational Intelligence, 975) 1st ed. 2021 Edition

Jin, Yaochu; Wang, Handing; Sun, Chaoli

ISBN 10: 3030746399 / ISBN 13: 9783030746391

Published by Springer, 2021

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Bibliographic Details

• Title: Data-driven Evolutionary Optimization (Studies in Computational Intelligence, 975) 1st ed. 2021 Edition
• Publisher: Springer
• Publication Date: 2021
• Language: English
• ISBN 10: 3030746399
• ISBN 13: 9783030746391
• Binding: Hardcover
• Condition: New

About this title

Synopsis

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.