Genetic Algorithm and BP Neural Network Implementation

This is a highly practical MATLAB program that implements a hybrid approach combining genetic algorithms with backpropagation (BP) neural networks. The program provides robust solutions for various computational problems including optimization, classification, and prediction tasks. Its core architecture features a genetic algorithm component for optimal parameter selection and weight initialization, followed by BP neural network training using gradient descent with momentum for efficient convergence. The implementation supports multiple application domains with customizable network architectures and genetic operators. In engineering applications, it can optimize design parameters through genetic algorithm-based search before fine-tuning with neural network training. For financial applications, the program enables stock price forecasting and risk assessment through time-series prediction capabilities using trained neural networks with genetically-optimized initial weights. Key MATLAB functions implemented include population initialization, fitness evaluation, selection/crossover/mutation operators for the genetic algorithm portion, alongside sigmoid activation functions, weight updating mechanisms, and error backpropagation for the neural network component. The program offers comprehensive configuration options for hidden layer sizes, population parameters, and training thresholds, making it suitable for diverse domain requirements.