In the context of the rapid development of artificial intelligence, optimizing the BP neural network model has become a key task to improve prediction performance. This paper studies the application of genetic algorithm in BP neural network optimization, which significantly improves the convergence speed and prediction accuracy of the model. On the test data, compared with the Standard BP Neural Network, the fitting effect and regression curve are significantly improved. The GA-BP model achieves a higher R-squared value (0.96804 vs. 0.84895), which verifies the effectiveness of this method. This study not only solves the limitations of Standard BP Neural Network such as slow convergence speed and easy to fall into local optimality, but also provides a practical solution for the optimization of neural networks in the context of big data and complex problem solving, which is of great significance to improving the performance and scalability of BP neural networks in practical applications, particularly in data-intensive applications.

Huaitian Zhang. Genetic Algorithm Optimization of BP Neural Network. Academic Journal of Computing & Information Science (2025), Vol. 8, Issue 4: 92-97. https://doi.org/10.25236/AJCIS.2025.080411.

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