MATLAB Source Code for Integrating Genetic Algorithm with BP Neural Network

This article explores the integration of genetic algorithms and backpropagation (BP) neural networks in MATLAB with provided source code. The genetic algorithm operates as an optimization technique that mimics natural selection processes to identify optimal solutions, while BP neural networks serve as fundamental machine learning algorithms for classification and prediction tasks. The synergy between these algorithms enhances model accuracy and robustness, making this combination highly valuable for practical applications. Our implementation demonstrates how to architect this hybrid approach through three key components: 1) Genetic algorithm functions for population initialization, fitness evaluation, selection, crossover and mutation operations; 2) BP neural network modules with customizable layer structures and activation functions; 3) Interface mechanisms that use genetic algorithms to optimize neural network weights and architecture parameters. The source package includes sample datasets, configuration files, and comprehensive documentation illustrating parameter tuning techniques and performance metrics analysis to help users effectively understand and apply these advanced methodologies.