Genetic Algorithm-Optimized BP Neural Network Algorithm

Using genetic algorithms to obtain better initial network weights and thresholds represents an optimization methodology for neural network training. Genetic algorithms simulate natural evolutionary processes through selection, crossover, and mutation operations to search for optimal solutions. In implementation, the algorithm typically encodes weights and thresholds as chromosomes, using fitness functions like mean squared error to evaluate network performance. Key operations include: 1) Population initialization with random weight encoding 2) Fitness evaluation through forward propagation 3) Roulette wheel selection based on fitness scores 4) Crossover operations (e.g., single-point crossover) to exchange genetic material 5) Mutation with low probability to maintain diversity. This approach effectively optimizes BP neural networks by preventing local minima convergence through global search capabilities, making it particularly suitable for complex optimization problems where traditional gradient-based methods struggle.