Optimizing Weights and Thresholds of BP Neural Networks Using Traditional Genetic Algorithm

Traditional Genetic Algorithm (GA) serves as a widely-adopted optimization methodology for refining weight and threshold parameters in Backpropagation (BP) neural networks. By implementing traditional GA optimization, we achieve enhanced convergence properties that significantly improve the performance and accuracy of BP networks. The algorithm operates by simulating biological evolutionary mechanisms—including selection, crossover, and mutation operations—to iteratively optimize network parameters over successive generations. This approach facilitates better understanding and refinement of BP network training and learning processes. Key implementation aspects involve chromosome encoding of weight/threshold matrices, fitness evaluation using mean squared error, and genetic operators applied to population evolution. Consequently, employing traditional genetic algorithms to optimize BP network parameters represents an effective and practical methodology for neural network enhancement.