Overview
- 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
Part of the book series: Studies in Computational Intelligence (SCI, volume 780)
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Table of contents (9 chapters)
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Front Matter
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Evolutionary Algorithms
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Front Matter
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Evolutionary Neural Networks
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Front Matter
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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
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Bibliographic Information
Book Title: Evolutionary Algorithms and Neural Networks
Book Subtitle: Theory and Applications
Authors: Seyedali Mirjalili
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-319-93025-1
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: Springer International Publishing AG, part of Springer Nature 2019
Hardcover ISBN: 978-3-319-93024-4Published: 12 July 2018
Softcover ISBN: 978-3-030-06572-0Published: 19 January 2019
eBook ISBN: 978-3-319-93025-1Published: 26 June 2018
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XIV, 156
Number of Illustrations: 8 b/w illustrations, 60 illustrations in colour
Topics: Computational Intelligence, Artificial Intelligence, Mathematical Models of Cognitive Processes and Neural Networks, Simulation and Modeling