Evolutionary Deep Neural Network Design
Fundamentals and Methods
Seiten
2022
Wiley-Blackwell (Verlag)
978-1-119-69985-9 (ISBN)
Wiley-Blackwell (Verlag)
978-1-119-69985-9 (ISBN)
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Offers a systematic and comprehensive text to evolutionary deep neural network architecture design
Evolutionary Deep Neural Network Design is a comprehensive text that offers an in-depth exploration of the concepts, methods, and challenges of evolutionary deep neural networks design. The authors—noted experts on the topic—provide an introduction to deep neural networks, evolutionary computation algorithms and include a number of representative examples of both. The book puts the focus on four components: encoding strategy, recombination operator, fitness evaluation, as well as the selection.
The book clearly describes the concepts and scope of evolutionary deep neural network design and includes information on the fundamental methods of evolutionary deep neural network architecture design. The book also features the main challenges and some potential research directions on this emerging topic. This important book:
Puts the focuses on four major components of architecture design: encoding strategy, recombination operator, fitness evaluation, and selection
Includes information ranging from the fundamentals to the most current research
Includes a supplemental website which features codes with sample data for testing draft architectures
Written for students and software engineers, Evolutionary Deep Neural Network Design offers a comprehensive review of all related aspects of evolutionary deep neural network architecture design.
Evolutionary Deep Neural Network Design is a comprehensive text that offers an in-depth exploration of the concepts, methods, and challenges of evolutionary deep neural networks design. The authors—noted experts on the topic—provide an introduction to deep neural networks, evolutionary computation algorithms and include a number of representative examples of both. The book puts the focus on four components: encoding strategy, recombination operator, fitness evaluation, as well as the selection.
The book clearly describes the concepts and scope of evolutionary deep neural network design and includes information on the fundamental methods of evolutionary deep neural network architecture design. The book also features the main challenges and some potential research directions on this emerging topic. This important book:
Puts the focuses on four major components of architecture design: encoding strategy, recombination operator, fitness evaluation, and selection
Includes information ranging from the fundamentals to the most current research
Includes a supplemental website which features codes with sample data for testing draft architectures
Written for students and software engineers, Evolutionary Deep Neural Network Design offers a comprehensive review of all related aspects of evolutionary deep neural network architecture design.
YANAN SUN, PhD, is currently a Research Professor in Sichuan University, China. GARY G. YEN, PhD, is a Regents Professor at Oklahoma State University, Stillwater. MENGJIE ZHANG, PhD, is Professor of Computer Science at Victoria University of Wellington.
| Erscheint lt. Verlag | 2.5.2022 |
|---|---|
| Reihe/Serie | IEEE Press Series on Computational Intelligence |
| Verlagsort | Hoboken |
| Sprache | englisch |
| Gewicht | 666 g |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Technik ► Elektrotechnik / Energietechnik | |
| Technik ► Nachrichtentechnik | |
| ISBN-10 | 1-119-69985-1 / 1119699851 |
| ISBN-13 | 978-1-119-69985-9 / 9781119699859 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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