Deep generative architectures (DGE) have revolutionized numerous fields by generating realistic imagined data. To optimize the performance of these models, researchers are constantly exploring new optimization strategies. A common approach involves fine-tuning hyperparameters through Bayesian optimization, aiming to reduce the error metric. Other c