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商品詳細
Data-Driven Evolutionary Optimization: Integrating Evolutionary Computation, Machine Learning and Data Science. 2021 ed.
・ISBN 978-3-030-74642-1 paper EUR 159.99
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| 著者・編者 | Jin, Yaochu / Wang, Handing / Sun, Chaoli, |
|---|---|
| シリーズ | (Studies in Computational Intelligence) |
| 出版社 | (Springer Nature Switzerland AG, SZ) |
| 出版年月 | 2022 |
| ページ数 | 393 pp. |
| 言語 | ENG |
| ニュース番号 | ＜A02-95887＞ |
解説
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.