## 5 Conclusions  
This study presented a novel hybrid approach, GAGAN, which integrates Genetic Algorithms (GAs) with Deep Convolutional GANs (DCGANs) to address common challenges in GAN training, such as mode collapse and unstable convergence. The primary innovation of GAGAN lies in the evolutionary optimization of the discriminator's weights, enhancing its ability to distinguish real images from generated ones and providing more effective feedback to the generator. This optimization process combines the local search capabilities of gradient-based methods with the global exploration offered by GAs, resulting in improved training stability and output quality. The experimental evaluation, conducted using the CelebA dataset, demonstrated several advantages of the GAGAN framework over traditional DCGANs. GAGAN produced significantly better results in terms of FID, highlighting its ability to generate images that closely align with the distribution of real data. Although DCGAN achieved slightly higher IS means, GAGAN exhibited greater consistency, with lower variability in its scores, reflecting stable and reliable performance. These metrics confirm GAGAN's ability to generate realistic and high-quality images while maintaining robustness across multiple training runs. The analysis of generator and discriminator losses further supports GAGAN's effectiveness. GAGAN consistently achieved lower discriminator losses than DCGAN, demonstrating its superior capacity to distinguish real from synthetic images. While some GAGAN configurations showed higher generator losses, reflecting challenges in deceiving the discriminator, others achieved strong performance, highlighting the framework's adaptability. Visual comparisons supported these findings, with GAGAN-generated images displaying enhanced textures, sharper details, and greater variability, while DCGAN outputs often exhibited repetitive patterns and artifacts. The integration of GAs also enhanced the overall stability of the training process. Despite initial computational constraints, including long training times on Google Colab, the transition to a more robust computational setup using an A100 GPU facilitated faster convergence. By optimizing key parameters such as population size and the number of generations, the GAGAN framework achieved a balance between processing efficiency and image quality. This iterative fine-tuning process reduced training time while maintaining or enhancing the fidelity of the generated images. These findings established a strong foundation for future research and improvement in GAGAN's design and application. Key areas for advancement include optimizing both the generator and discriminator simultaneously, to further refine their interactions, and developing more sophisticated genetic operators to enhance the evolutionary process. Additionally, incorporating perceptual metrics into the fitness evaluation could provide a more detailed measure of output quality, aligning the discriminator's optimization with human perceptual standards. By pursuing these enhancements, we anticipate that further advancements in both the efficiency and capability of GAGAN models will emerge, expanding the boundaries of generative modeling and setting new standards for quality and diversity in synthetic image generation. Conceptualization, D.K., P.Z. and C.P. ; methodology, D.K., P.Z. and H.C.L. ; validation, H.C.L. and C.P. ; investigation, P.Z. and M.P. ; data curation, D.K. ; writing--original draft preparation, D.K., P.Z. and M.P. ; writing--review and editing, D.K., M.P. and H.C.L. ; supervision, P.Z. and C.P. All authors have read and agreed to the published version of the manuscript. This research received no external funding. **Data Availability Statement:** Original data presented in the study are openly available at [[[https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html](https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html)]([https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html](https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html))] (accessed on 28 October 2024).