Deep Neural Evolution

Deep Learning with Evolutionary Computation

Inbunden, Engelska, 2020

2 699 kr

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Consequently, EC researchers are enthusiastic about applying their arsenal for the design and optimization of deep neural networks (DNN). This book brings together the recent progress in DL research where the focus is particularly on three sub-domains that integrate EC with DL: (1) EC for hyper-parameter optimization in DNN;

Produktinformation

Utgivningsdatum2020-05-21
Mått155 x 235 x 30 mm
Vikt840 g
FormatInbunden
SpråkEngelska
SerieNatural Computing Series
Antal sidor438
Upplaga20001
FörlagSpringer Verlag, Singapore
ISBN9789811536847

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Hitoshi Iba received his Ph.D. degree from The University of Tokyo, Japan, in 1990. From 1990 to 1998, he was with the Electro Technical Laboratory in Ibaraki, Japan. Since 1998, he has been with The University of Tokyo, where he is currently a professor in the Graduate School of Information Science and Technology. His research interests include evolutionary computation, artificial life, artificial intelligence, and robotics. He is an associate editor of the Journal of Genetic Programming and Evolvable Machines (GPEM). Dr. Iba is also is an underwater naturalist and experienced Professional Association of Diving Instructors (PADI) divemaster, having completed more than a thousand dives.Nasimul Noman received his Ph.D. degree from The University of Tokyo, Japan, in 2007. He was a faculty member in the Department of Computer Science and Engineering, University of Dhaka, Bangladesh, from 2002 to 2012. In 2013, he joined the School of Electrical Engineering and Computing at The University of Newcastle, Australia, and currently he is working as a senior lecturer there. His research interests include evolutionary computation, computational biology, bioinformatics, and machine learning.

Chapter 1: Evolutionary Computation and meta-heuristics.- Chapter 2: A Shallow Introduction to Deep Neural Networks.- Chapter 3: On the Assessment of Nature-Inspired Meta-Heuristic Optimization Techniques to Fine-Tune Deep Belief Networks.- Chapter 4: Automated development of DNN based spoken language systems using evolutionary algorithms.- Chapter 5: Search heuristics for the optimization of DBN for Time Series Forecasting.- Chapter 6: Particle Swarm Optimisation for Evolving Deep Convolutional Neural Networks for Image Classification: Single- and Multi-objective Approaches.- Chapter 7: Designing Convolutional Neural Network Architectures Using Cartesian Genetic Programming.- Chapter 8: Fast Evolution of CNN Architecture for Image Classificaiton.- Chapter 9: Discovering Gated Recurrent Neural Network Architectures.- Chapter 10: Investigating Deep Recurrent Connections and Recurrent Memory Cells Using Neuro-Evolution.- Chapter 11: Neuroevolution of Generative Adversarial Networks.- Chapter 12: Evolving deep neural networks for X-ray based detection of dangerous objects.- Chapter 13: Evolving the architecture and hyperparameters of DNNs for malware detection.- Chapter 14: Data Dieting in GAN Training.- Chapter 15: One-Pixel Attack: Understanding and Improving Deep Neural Networks with Evolutionary Computation.