Data-driven evolutionary optimization : integrating evolutionary computation, machine learning and data science / Yaochu Jin, Handing Wang, Chaoli Sun.
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...
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| Main Authors: | , , |
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| Format: | Ebook |
| Language: | English |
| Published: |
Cham, Switzerland :
Springer,
[2021]
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| Series: | Studies in computational intelligence ;
v. 975. |
| Subjects: | |
| Online Access: | Springer eBooks |
| Summary: | 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. |
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| Physical Description: | 1 online resource. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 3030746399 9783030746391 3030746402 9783030746407 |
| ISSN: | 1860-949X ; |