## 4 Experimental Evaluation of the GAGAN Model
### Numerical Analysis  
In this section, we present a detailed analysis for numerical performance metrics of the DCGAN and GAGAN models. By examining the final losses for multiple iterations of both models, we aim to provide insights into their relative effectiveness in generating high-quality synthetic images. In GAGAN, the discriminator loss measures how well the discriminator distinguishes real data from generated data, while the generator loss reflects the generator's ability to create realistic samples. These losses are competitive, with each model improving against the other in an adversarial cycle that enhances the realism of generated data and the performance of the GAN. Balancing these losses is key to optimizing the GAN's realism and guiding the GA in evolving effective discriminator parameters. The discriminator loss quantifies the discriminator's ability to classify real samples as \"real\" and generated samples as \"fake\", typically using binary cross-entropy to penalize misclassifications. The generator loss, also often based on binary cross-entropy, measures how well the generator fools the discriminator into misclassifying generated samples as real. These losses are competitive: as one model improves, it forces the other to adapt in an adversarial cycle, enhancing the generator's realism and discriminator's accuracy. Balancing these losses is critical for maximizing GAN realism and guiding the GA's optimization of effective discriminator parameters. Figure 7 illustrates the generator loss (g_loss) and discriminator loss (d_loss) values of the GAGAN model across different training iterations. The plot highlights the convergence dynamics of both the generator and discriminator during training. The results indicate relatively stable d_loss values, suggesting consistent discriminator performance in differentiating real from generated images, while the g_loss values fluctuate more prominently, reflecting the generator's ongoing adjustments to improve the realism of synthetic outputs. This comparison provides insights into the model's training stability and the adversarial balance achieved by the GAGAN framework. In the zero-sum game between the generator and discriminator in GANs, achieving equilibrium between their respective losses is crucial for optimal training. As illustrated in Figure 7, the d_loss in GAGAN remains remarkably stable throughout the training process, indicating its effectiveness in consistently distinguishing real from generated images with minimal fluctuation. In contrast, the g_loss fluctuates more prominently, reflecting the generator's continuous efforts to enhance the realism of its outputs. The notable difference in  
Figure 7: Comparison of generator and discriminator losses for GAGAN across individuals. magnitude between the two losses--where d_loss is less than half of g_loss--demonstrates the enhanced stability achieved by GAGAN. While the generator continues refining its outputs, it is challenged by a more stable and well-optimized discriminator. This suggests that GAGAN maintains a balanced adversarial dynamic, with the discriminator providing stable feedback that guides the generator's learning without the instability often seen in traditional GANs. The stability of the discriminator loss highlights the improved training dynamics enabled by the integration of genetic algorithms. To evaluate the performance of both DCGAN and GAGAN models, we present the recorded losses of the final results. These loss values serve as critical indicators of how well each model has optimized the generator and discriminator components. Table 2 displays the final recorded generator (g_loss) and discriminator (d_loss) losses of the DCGAN and GAGAN models at the last epoch, following 50 epochs of training. Each row corresponds to a specific run of the algorithm for each model (e.g., DCGAN/1, GAGAN/2), indicated in the Model/Training run No. column.