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Evolutionary Algorithms and Neural Networks

Theory and Applications

  • Seyedali¬†Mirjalili

Part of the Studies in Computational Intelligence book series (SCI, volume 780)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Evolutionary Algorithms

    1. Front Matter
      Pages 1-1
    2. Seyedali Mirjalili
      Pages 15-31
    3. Seyedali Mirjalili
      Pages 33-42
    4. Seyedali Mirjalili
      Pages 43-55
    5. Seyedali Mirjalili
      Pages 57-72
  3. Evolutionary Neural Networks

    1. Front Matter
      Pages 73-73
    2. Seyedali Mirjalili
      Pages 75-86
    3. Seyedali Mirjalili
      Pages 87-104
    4. Seyedali Mirjalili
      Pages 105-139
    5. Seyedali Mirjalili
      Pages 141-156

About this book

Introduction

This book introduces readers to the fundamentals of artificial neural networks, with a special emphasis on evolutionary algorithms. At first, the book offers a literature review of several well-regarded evolutionary algorithms, including particle swarm and ant colony optimization, genetic algorithms and biogeography-based optimization. It then proposes evolutionary version of several types of neural networks such as feed forward neural networks, radial basis function networks, as well as recurrent neural networks and multi-later perceptron. Most of the challenges that have to be addressed when training artificial neural networks using evolutionary algorithms are discussed in detail. The book also demonstrates the application of the proposed algorithms for several purposes such as classification, clustering, approximation, and prediction problems. It provides a tutorial on how to design, adapt, and evaluate artificial neural networks as well, and includes source codes for most of the proposed techniques as supplementary materials. 

Keywords

Optimization for Real World Problems Single-objective Optimization Algorithm Stochastic Optimization Algorithms Evolutionary Computation for Real-world Problems Estimation of a Global Optimum Evolutionary Operators Training Neural Networks with Genetic Algorithms Training Algorithms for Neural Networks Backpropagation Algorithms Optimal Set of Features Hand Posture/Gesture Detection Using Neural Networks Population-based Optimization Algorithms Binary PSO Algorithms Mathematical Model of PSO Deep Neural Networks for Image Classification Applied Neural Networks

Authors and affiliations

  • Seyedali¬†Mirjalili
    • 1
  1. 1.Institute for Integrated and Intelligent SystemsGriffith UniversityBrisbaneAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-93025-1
  • Copyright Information Springer International Publishing AG, part of Springer Nature 2019
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-93024-4
  • Online ISBN 978-3-319-93025-1
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site