Introduction to Artificial Neural Networks and Evolutionary Algorithms

This book introduces readers to the fundamentals of artificial neural networks, with a special emphasis on evolutionary algorithms.

Literature Review of 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.

Evolutionary Neural Network Models

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.

Training Challenges and Applications

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.

Design, Evaluation, and Source Codes

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.