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Evolutionary Approach to Machine Learning and Deep Neural Networks

Neuro-Evolution and Gene Regulatory Networks

  • Hitoshi Iba

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Hitoshi Iba
    Pages 1-26
  3. Hitoshi Iba
    Pages 77-104
  4. Hitoshi Iba
    Pages 231-234
  5. Back Matter
    Pages 235-245

About this book

Introduction

This book provides theoretical and practical knowledge about a methodology for evolutionary algorithm-based search strategy with the integration of several machine learning and deep learning techniques. These include convolutional neural networks, Gröbner bases, relevance vector machines, transfer learning, bagging and boosting methods, clustering techniques (affinity propagation), and belief networks, among others. The development of such tools contributes to better optimizing methodologies. Beginning with the essentials of evolutionary algorithms and covering interdisciplinary research topics, the contents of this book are valuable for different classes of readers: novice, intermediate, and also expert readers from related fields.

Following the chapters on introduction and basic methods, Chapter 3 details a new research direction, i.e., neuro-evolution, an evolutionary method for the generation of deep neural networks, and also describes how evolutionary methods are extended in combination with machine learning techniques. Chapter 4 includes novel methods such as particle swarm optimization based on affinity propagation (PSOAP), and transfer learning for differential evolution (TRADE), another machine learning approach for extending differential evolution.

The last chapter is dedicated to the state of the art in gene regulatory network (GRN) research as one of the most interesting and active research fields. The author describes an evolving reaction network, which expands the neuro-evolution methodology to produce a type of genetic network suitable for biochemical systems and has succeeded in designing genetic circuits in synthetic biology. The author also presents real-world GRN application to several artificial intelligent tasks, proposing a framework of motion generation by GRNs (MONGERN), which evolves GRNs to operate a real humanoid robot.

Keywords

Evolutionary Computation Evolutionary Computation Meta-Heuristics Deep Learning Machine Learning Gene Regulatory Networks Particle Swarm Optimization Differential Evolution Genetic Programming Genetic Algorithms

Authors and affiliations

  • Hitoshi Iba
    • 1
  1. 1.The University of TokyoTokyoJapan

Bibliographic information

  • DOI https://doi.org/10.1007/978-981-13-0200-8
  • Copyright Information Springer Nature Singapore Pte Ltd. 2018
  • Publisher Name Springer, Singapore
  • eBook Packages Computer Science
  • Print ISBN 978-981-13-0199-5
  • Online ISBN 978-981-13-0200-8
  • Buy this book on publisher's site
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