Data-Driven Evolutionary Optimization: Integrating Evolutionary Computation, Machine Learning and Data Science: 975 - Rilegato

Jin, Yaochu; Wang, Handing; Sun, Chaoli

Rilegato

ISBN 10: 3030746399 ISBN 13: 9783030746391

Casa editrice: Springer-Verlag GmbH, 2021

Riassunto

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.

Su questo libro

Dalla quarta di copertina

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.

Dettagli dell'edizione

• Editore: Springer-Verlag GmbH
• Data di pubblicazione: 2021
• Lingua: Inglese
• ISBN 10: 3030746399
• ISBN 13: 9783030746391
• Rilegatura: Copertina rigida
• Numero edizione: 1
• Numero di pagine: 420
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