Source Code for Genetic Algorithm Optimization of BP Neural Networks

Reposted source code demonstrating initialization function implementation for genetic algorithm-optimized BP neural networks. During genetic algorithm optimization, we can consider introducing additional mutation operations and crossover operations to expand the search space. The implementation typically involves defining mutation rates and crossover probabilities through parameters like ga_mutation_rate and ga_crossover_prob. Furthermore, we can experiment with different genetic algorithm parameter configurations, such as population size (population_size), iteration count (max_generations), and selection methods to achieve better optimization results. When coding the initialization function, we should employ random number generators (e.g., rand() or numpy.random) to ensure initial network weights possess sufficient randomness, which promotes algorithm convergence and population diversity. The weight initialization might use methods like Xavier initialization or He initialization for better training performance.