## 1 Introduction
Similarly, GAGAN combines Genetic Algorithms with GANs to enhance image generation by refining discriminator training, contributing to the exploration of hybrid models in deep learning. Notably, these advancements necessitates considerable computational resources, as demonstrated in real-world applications in medical image analysis and data augmentation. The use of advanced hardware is essential not only for the successful training of GAGAN but also for ensuring efficient, high-quality image generation in practical applications where performance and accuracy are critical. Expanding on this research direction, the present study proposes a novel hybrid approach, termed GAGAN, which combines GAs with DCGANs to address key GAN training challenges. Specifically, the GAGAN framework utilizes GAs to optimize the discriminator's weights, enhancing its effectiveness at distinguishing real from generated images, thereby improving the generator's performance. By combining the strengths of evolutionary computation with deep learning, GAGAN aims to stabilize convergence, reduce generator loss, and ultimately produce higher-quality images. Experimental evaluations on the CelebA dataset demonstrate that the GAGAN model outperforms traditional DCGANs in both image fidelity and stability of training. The findings suggest that incorporating evolutionary techniques into GAN training introduces a valuable complement to deep learning, paving the way for more efficient and resilient generative models. This study, therefore, contributes to the evolving field of generative AI by proposing a method that balances local and global search mechanisms, offering new insights into GAN optimization and the potential of hybrid approaches in machine learning. The innovation of this work is summarized around the following key points:  
1. Hybrid Approach: This study integrates Genetic Algorithms (GAs) with Deep Convolutional GANs (DCGANs), establishing a novel hybrid model that utilizes the global search capabilities of GAs alongside traditional gradient-based optimization. Discriminator Optimization: The primary innovation lies in using GAs to optimize the weights of the discriminator, enhancing its ability to differentiate between real and synthetic images, which improves the generator's feedback loop. 3. Enhanced Training Stability: By optimizing the discriminator's weights using evolutionary processes, the proposed model improves training stability compared to traditional GAN methods, reducing issues such as mode collapse and oscillatory convergence common in GAN training. 4. Improved Image Fidelity: Through experiments on the CelebA dataset, the GAGAN framework demonstrates enhanced image quality and realism compared to traditional DCGAN models, producing outputs with better texture, clarity, and variety. The paper is organized as follows: Section 2 provides a concise overview of the kinematics of GA and GAN architectures, essential for understanding the subsequent development of the proposed approach. Section 3 introduces the integration of GAs with GANs, crucial for establishing a hybrid model. Section 4 presents the experimental setup and the results after evaluating the effectiveness of GAGAN in image generation based on a dataset including 2000 images. Finally, conclusions and directions for future advancements are presented in Section 5.