Genetic Algorithm-Optimized BP Neural Network Algorithm

This code implements a BP neural network optimization algorithm based on genetic algorithms. Through personal testing, it has been verified as fully functional. The primary objective of this algorithm is to enhance BP neural network performance using genetic optimization techniques. The genetic algorithm simulates natural evolutionary processes through operations like crossover and mutation to discover optimal solutions. Meanwhile, the BP neural network is a widely-used artificial neural network that employs backpropagation algorithm for network training to optimize performance. By integrating these two algorithms, we achieve a more refined BP neural network implementation with improved performance and accuracy.

Key implementation details include: using genetic algorithms to optimize BP neural network's initial weights and thresholds, where chromosome encoding typically represents connection weights and bias values. The fitness function evaluates network performance metrics like mean squared error. The genetic operations involve selection based on fitness scores, crossover between parent chromosomes, and random mutation to maintain population diversity. The optimized parameters are then transferred to the BP network for traditional gradient-based training, combining global search capabilities of genetic algorithms with local refinement of backpropagation.