Integration of Genetic Algorithm with BP Neural Network

This is a custom-developed code that integrates genetic algorithm with backpropagation (BP) neural network! The implementation performs multi-objective optimization on neural network models. Detailed documentation is provided explaining the results comprehensively. This code implements a practical application example of BP-GA multi-objective optimization! In this application example, we combine genetic algorithm with BP neural network to achieve superior performance and results. The genetic algorithm component handles global optimization and parameter selection, while the BP neural network provides the learning and prediction framework. The detailed documentation explains the algorithm implementation specifics, including chromosome encoding schemes, fitness function design, crossover and mutation operations, as well as analysis of optimization results. Key implementation features include: - Genetic algorithm optimization of neural network weights and thresholds - Multi-objective fitness evaluation for balanced performance metrics - Integration interface between GA population evolution and BP network training - Adaptive parameter tuning mechanisms for convergence optimization This application example helps users better understand and apply BP-GA multi-objective optimization algorithms in practical scenarios. The code demonstrates how to leverage genetic algorithms for enhanced neural network performance through optimal parameter selection. We hope this code proves valuable for your research and development projects!