## 1 Introduction
By fostering diversity among model candidates, evolutionary algorithms offer a broader search strategy that supports the development of more robust and adaptable GAN models. In [22], the authors proposed a structure tuning method for DCGANs using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). Their approach optimizes both the network's architecture and hyperparameters, aiming to improve GAN performance and robustness. Experiments demonstrated that this approach enhanced the quality and accuracy of generated images. In [23], the authors employed Genetic Algorithms to optimize GAN hyperparameters dynamically during training, achieving improved model stability and convergence. The results indicate that hybrid approaches, combining GAs and GANs, can improve training efficiency and robustness. This finding suggests that such integrated models effectively exploit the strengths of both GAs and GANs, offering the potential to develop more efficient generative models. The authors in [24] proposed the Evolution-GAN, which uses multiple generators trained with an evolutionary strategy to address the mode collapse problem common in standard GANs. By incorporating mutations and an additional classifier network, Evolution-GAN promotes generator diversity, leading to improved image quality and variety in synthetic outputs. Their experiments demonstrated superior performance over traditional GANs and DCGANs. The authors in [25] propose a novel method for embedding hidden data into images while simultaneously generating adversarial samples. Using a combination of Genetic Algorithms and Least Significant Bit (LSB) encoding, the approach ensures high image quality and effective concealment of information. This work highlights the potential of combining generative modeling with data hiding techniques to enhance image security and privacy, illustrating the versatility of GAN-based frameworks in multimedia applications. The work in [26] introduces a novel bi-level evolutionary framework aimed at optimizing both the generator and discriminator in GANs simultaneously, to achieve better training stability. By applying evolutionary strategies at two distinct levels, the method enhances model convergence and robustness. The main objective of this work is to address common GAN issues, such as mode collapse and unstable training dynamics, through the integration of evolutionary optimization. This aligns with the broader exploration of hybrid models, showcasing the potential of evolutionary algorithms to improve GAN performance in generating high-quality synthetic data. The work in [27] proposes a method for reconstructing facial images from feature-based inputs by employing a GAN generator as a distribution constraint to ensure realistic outputs. By integrating Genetic Algorithms (GAs) to optimize the reconstruction process, the study demonstrates how evolutionary strategies can enhance the quality and realism of generated images. The experimental results demonstrate the effectiveness of this approach in producing high-quality facial reconstructions that closely align with the original input features. The authors in [28] introduced a Multi-Scale Evolutionary GAN (MEvo-GAN) designed specifically for enhancing underwater imagery by integrating multi-scale convolutional layers with evolutionary algorithms. This approach effectively reduces common underwater image issues, such as low contrast and color distortion, while stabilizing GAN training through genetic selection. Despite these innovations, MEvo-GAN's design is specifically adapted to underwater conditions, highlighting the need for more generalized frameworks for broader applications in image enhancement and generation. Recent advances in deep learning have demonstrated the versatility of neural networks in a wide range of applications, including medical imaging and data security [29; 30]. While these studies focus on distinct domains, they share a common theme with GAN-based frameworks: optimizing learning through innovative techniques.