Evolutionary Algorithms and Neural Networks

Beschreibung

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. 
   
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

Autor*in

Seyedali Mirjalili

Themen in »Evolutionary Algorithms and Neural Networks«

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

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