Enhancing BP Neural Network Models Using Genetic Algorithms

Utilizing genetic algorithms to enhance BP neural network models plays a significant role in solving optimization and prediction problems. Genetic algorithms are optimization techniques based on natural selection and genetic mechanisms, which simulate biological evolution processes to discover global optimal solutions. When integrated into BP neural networks, genetic algorithms optimize key parameters like weights and biases through selection, crossover, and mutation operations coded using fitness functions that evaluate network performance. This integration enhances the network's learning capability and generalization power, enabling better handling of complex problems. Through iterative optimization processes where genetic algorithms evolve population chromosomes representing network parameters, BP neural networks achieve more accurate predictions on unseen data and higher prediction accuracy rates. The combined approach typically implements genetic algorithm operations using roulette wheel selection, uniform crossover, and Gaussian mutation, while the BP network employs backpropagation with gradient descent minimization. Therefore, combining genetic algorithms with BP neural network models delivers superior long-term optimization effects when solving various real-world problems.