Fine-tuning Generative Adversarial Networks Using Genetic Algorithm

Generative Adversarial Networks (GANs) are a major innovation in the field of deep learning. It has been widely used in image generation and text-to-image conversion tasks. However, it is challenging for GANs to find a balance for competition between generators and discriminators. In this paper, we investigate the optimization effect of combining the Adam optimization algorithm and genetic algorithm on the GANs training process. In the experiment, we first use the Adam algorithm to pre-train GANs and then apply the genetic algorithm (GA) to further optimize the pre-trained model. The experimental results show that this combination method not only reduces the number of iterations required by the genetic algorithm through pre-training, thus saving the training time but also improves the performance of the training model. This shows that the use of a hybrid optimization strategy in complex network training is very effective and improves training efficiency.