MATLAB Neural Networks and Genetic Algorithms

This book provides an in-depth exploration of the principles and applications of neural networks and genetic algorithms in MATLAB. The content begins with fundamental concepts of neural networks, explaining key architectures such as multilayer perceptrons and their training mechanisms using backpropagation algorithms. Practical examples demonstrate network configuration, training parameter optimization, and performance evaluation through MATLAB's Neural Network Toolbox functions like feedforwardnet and trainlm.

The genetic algorithm section covers core concepts including chromosome representation, selection mechanisms, crossover operations, and mutation techniques. Implementation details showcase MATLAB's Global Optimization Toolbox functions such as ga and optimoptions for configuring population size, fitness functions, and termination criteria. Code examples illustrate how to define objective functions, handle constraints, and visualize optimization progress.

All concepts are supported by complete MATLAB source code that readers can execute, modify, and extend. The provided code includes commented implementations of neural network training workflows and genetic algorithm optimization processes, enabling hands-on experimentation with parameter tuning and algorithm customization. This practical approach helps reinforce understanding and facilitates real-world application of these computational intelligence techniques.