Integration of Genetic Algorithm and BP Neural Network: A Mathematical Modeling Approach

The integration of Genetic Algorithm and BP Neural Network was the focus of my paper published during a mathematical modeling competition. This paper provides detailed explanations of the fundamental principles and applications of both Genetic Algorithm (optimizing solutions through selection, crossover, and mutation operations) and Backpropagation Neural Network (utilizing gradient descent for weight adjustments). It discusses their respective advantages and limitations in solving practical problems, with particular emphasis on how Genetic Algorithm can optimize BP neural network's initial weights and architecture to overcome local minima issues. Through case studies from mathematical modeling competitions, the paper demonstrates the effectiveness and feasibility of this hybrid approach in real-world problem solving. The implementation typically involves coding genetic operators for population evolution and designing neural network structures with customizable layers and activation functions. This work aims to provide researchers and mathematical modeling enthusiasts with a comprehensive reference to deepen their understanding of integrated GA-BP applications, enabling them to implement and refine these techniques in their own research and practical projects.