BP Neural Network Optimization Algorithm Based on Genetic Algorithm

BP networks are a type of multi-layer feedforward neural network, named after the error backpropagation learning algorithm used to adjust network weights during training. Proposed by Rumelhart et al. in 1986, BP neural networks feature simple architecture, numerous adjustable parameters, diverse training algorithms, and strong operability, leading to widespread adoption. Approximately 80%–90% of neural network models utilize BP networks or their variants. While BP networks form the core of forward networks and represent the most refined part of neural networks, they suffer from limitations such as slow learning convergence.