Data-Driven Evolutionary Optimization: Integrating Evolutionary Computation, Machine Learning and Data Science - Handing Wang,Chaoli Sun,Yaochu Jin
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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- ... Pilns apraksts
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Aprašymas
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.
Vairāk informācijas
| Autors | Handing Wang, Chaoli Sun, Yaochu Jin |
|---|---|
| Izdevējs | Springer Nature Switzerland |
| Series | Studies in Computational Intelligence |
| Izlaides gads | 2021 |
| Vāka tips | Cietais vāks |
| EAN | 9783030746391 |