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© 2020

Deep Neural Evolution

Deep Learning with Evolutionary Computation

  • Hitoshi Iba
  • Nasimul Noman
Book

Part of the Natural Computing Series book series (NCS)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Preliminaries

    1. Front Matter
      Pages 1-1
  3. Hyper-Parameter Optimization

    1. Front Matter
      Pages 65-65
    2. Leandro Aparecido Passos, Gustavo Henrique de Rosa, Douglas Rodrigues, Mateus Roder, João Paulo Papa
      Pages 67-96
    3. Takahiro Shinozaki, Shinji Watanabe, Kevin Duh
      Pages 97-129
    4. Takashi Kuremoto, Takaomi Hirata, Masanao Obayashi, Kunikazu Kobayashi, Shingo Mabu
      Pages 131-152
  4. Structure Optimization

    1. Front Matter
      Pages 153-153
    2. Masanori Suganuma, Shinichi Shirakawa, Tomoharu Nagao
      Pages 185-208
    3. Ali Bakhshi, Stephan Chalup, Nasimul Noman
      Pages 209-229
  5. Deep Neuroevolution

    1. Front Matter
      Pages 231-231
    2. Aditya Rawal, Jason Liang, Risto Miikkulainen
      Pages 233-251
    3. Travis Desell, AbdElRahman A. ElSaid, Alexander G. Ororbia
      Pages 253-291
    4. Victor Costa, Nuno Lourenço, João Correia, Penousal Machado
      Pages 293-322
  6. Applications and Others

    1. Front Matter
      Pages 323-323
    2. Ryotaro Tsukada, Lekang Zou, Hitoshi Iba
      Pages 325-355
    3. Jamal Toutouh, Erik Hemberg, Una-May O’Reilly
      Pages 379-400

About this book

Introduction

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).

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; (2) EC for DNN architecture design; and (3) Deep neuroevolution. The book also presents interesting applications of DL with EC in real-world problems, e.g., malware classification and object detection. Additionally, it covers recent applications of EC in DL, e.g. generative adversarial networks (GAN) training and adversarial attacks. The book aims to prompt and facilitate the research in DL with EC both in theory and in practice.

Keywords

Evolutionary Computation Meta-heuristics Deep Learning Deep Neural Network Machine Learning

Editors and affiliations

  • Hitoshi Iba
    • 1
  • Nasimul Noman
    • 2
  1. 1.Graduate School of Information Science and TechnologyThe University of TokyoTokyoJapan
  2. 2.School of Electrical Engineering and ComputingThe University of NewcastleCallaghanAustralia

About the editors

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.

Bibliographic information

  • Book Title Deep Neural Evolution
  • Book Subtitle Deep Learning with Evolutionary Computation
  • Editors Hitoshi Iba
    Nasimul Noman
  • Series Title Natural Computing Series
  • Series Abbreviated Title Natural Computing Series
  • DOI https://doi.org/10.1007/978-981-15-3685-4
  • Copyright Information Springer Nature Singapore Pte Ltd. 2020
  • Publisher Name Springer, Singapore
  • eBook Packages Computer Science Computer Science (R0)
  • Hardcover ISBN 978-981-15-3684-7
  • Softcover ISBN 978-981-15-3687-8
  • eBook ISBN 978-981-15-3685-4
  • Series ISSN 1619-7127
  • Edition Number 1
  • Number of Pages XII, 438
  • Number of Illustrations 114 b/w illustrations, 107 illustrations in colour
  • Topics Machine Learning
    Mathematical Models of Cognitive Processes and Neural Networks
  • Buy this book on publisher's site