Data-Driven Evolutionary Optimization Integrating Evolutionary Computation, Machine Learning and Data Science
Language: English
Published by Springer, 2021
Series: Book 473 of 538 - Studies in Computational Intelligence
- Hardcover
- New
Condition: New
US$ 258.10
Quantity: 2 available
Add to basketItem description from seller
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. Sprache: Englisch Gewicht in Gramm: 741. …
Seller Inventory # IB-1177708
- Title
- Data-Driven Evolutionary Optimization Integrating Evolutionary Computation, Machine Learning and Data Science
- Author
- Handing Wang, Yaochu Jin, Chaoli Sun
- Publisher
- Springer
- Publication year
- 2021
- Condition
- Neu
- Binding
- Gebundene Ausgabe
- Language
- English
- ISBN 10
- 3030746399
- ISBN 13
- 9783030746391
- Item weight
- 741 grams
- Series
- Book 473 of 538: Studies in Computational Intelligence
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.
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.
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