Evolutionary Algorithms and Neural Networks
Theory and Applications
Authors: Mirjalili, Seyedali
Free Preview- Introduces beginners to evolutionary algorithms and artificial neural networks
- Shows how to train artificial neural networks using evolutionary algorithms
- Includes extensive examples of the proposed techniques
- Source codes are available on the author’s webpage
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- About this book
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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.
- About the authors
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- Reviews
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- Table of contents (9 chapters)
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Introduction to Evolutionary Single-Objective Optimisation
Pages 3-14
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Particle Swarm Optimisation
Pages 15-31
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Ant Colony Optimisation
Pages 33-42
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Genetic Algorithm
Pages 43-55
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Biogeography-Based Optimisation
Pages 57-72
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Table of contents (9 chapters)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Evolutionary Algorithms and Neural Networks
- Book Subtitle
- Theory and Applications
- Authors
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- Seyedali Mirjalili
- Series Title
- Studies in Computational Intelligence
- Series Volume
- 780
- Copyright
- 2019
- Publisher
- Springer International Publishing
- Copyright Holder
- Springer International Publishing AG, part of Springer Nature
- eBook ISBN
- 978-3-319-93025-1
- DOI
- 10.1007/978-3-319-93025-1
- Hardcover ISBN
- 978-3-319-93024-4
- Softcover ISBN
- 978-3-030-06572-0
- Series ISSN
- 1860-949X
- Edition Number
- 1
- Number of Pages
- XIV, 156
- Number of Illustrations
- 8 b/w illustrations, 60 illustrations in colour
- Topics
