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Deep Neural Evolution: Deep Learning with Evolutionary Computation - Research Book

Deep Neural Evolution: Deep Learning with Evolutionary Computation - Research Book

Good Research Book in Deep Neural Evolution: Deep Learning with Evolutionary Computation

Author(s) Name:  Hitoshi Iba, Nasimul Noman

About the Book:

   This book delivers the state of the art in deep learning (DL) methods hybridized with evolutionary computation (EC). Over the last decade, DL has dramatically reformed many domains: computer vision, speech recognition, healthcare, and automatic game playing, to mention only a few. All DL models, using different architectures and algorithms, utilize multiple processing layers for extracting a hierarchy of abstractions of data.
   Their remarkable successes notwithstanding, these powerful models are facing many challenges, and this book presents the collaborative efforts by researchers in EC to solve some of the problems in DL.
   EC comprises optimization techniques that are useful when problems are complex or poorly understood, or insufficient information about the problem domain is available. This family of algorithms has proven effective in solving problems with challenging characteristics such as non-convexity, non-linearity, noise, and irregularity, which dampen the performance of most classic optimization schemes.
   Furthermore, EC has been extensively and successfully applied in artificial neural network (ANN) research from parameter estimation to structure optimization. Consequently, EC researchers are enthusiastic about applying their arsenal for the design and optimization of deep neural networks (DNN).

Table of Contents

  • Evolutionary Computation and Meta-heuristics
  • A Shallow Introduction to Deep Neural Networks
  • On the Assessment of Nature-Inspired Meta-Heuristic Optimization Techniques to Fine-Tune Deep Belief Networks
  • Automated Development of DNN Based Spoken Language Systems Using Evolutionary Algorithms
  • Search Heuristics for the Optimization of DBN for Time Series Forecasting
  • Particle Swarm Optimization for Evolving Deep Convolutional Neural Networks for Image Classification: Single- and Multi-Objective Approaches
  • Designing Convolutional Neural Network Architectures Using Cartesian Genetic Programming
  • Fast Evolution of CNN Architecture for Image Classification
  • Discovering Gated Recurrent Neural Network Architectures
  • Investigating Deep Recurrent Connections and Recurrent Memory Cells Using Neuro-Evolution
  • Neuroevolution of Generative Adversarial Networks
  • Evolving Deep Neural Networks for X-ray Based Detection of Dangerous Objects
  • Evolving the Architecture and Hyperparameters of DNNs for Malware Detection
  • Data Dieting in GAN Training
  • ISBN:  978-981-15-3685-4

    Publisher:  Springer

    Year of Publication:  2020

    Book Link:  Home Page Url