## 2 Related Studies  
### Overview of NAS-GAN Reviews  
Despite the growing interest in applying Neural Architecture Search (NAS) to Generative Adversarial Networks (GANs), there have been limited comprehensive reviews on this specific topic. To date, only two papers have been published that specifically review NAS in GANs, and these are essentially one work and its continuation.  
[PERSON] and [PERSON] (2021) [15] conducted a comprehensive review of NAS techniques applied to GANs. The authors analyzed various approaches based on key components of NAS: search space, search strategy, and performance estimation strategy. They identified cell-based and chain/entire structure as the primary search space types, with reinforcement learning, gradient-based, and evolutionary algorithms as the main search strategies. The review focused on image generation and GAN model compression tasks, comparing different methods using metrics such as Inception Score (IS) and Frechet Inception Distance (FID). The authors also discussed limitations and future directions for NAS in GANs, including potential applications in semantic image segmentation and high-resolution image synthesis. However, this survey was published in 2021 and may not include the most recent advancements in the field.  
[PERSON] and [PERSON] (2021) [16], published in the same year, provides a comprehensive review of NAS techniques applied to GANs for image generation tasks. The authors analyze various approaches using multiple criteria, including search space design, search strategy implementation, and performance evaluation methods. They examine systems that weren't discussed in the previous paper, comparing them across several key dimensions: image generation quality, computational efficiency, transferability to different datasets, and support for supervised vs. unsupervised learning. Performance is evaluated using metrics like IS and FID on datasets such as CIFAR-10 and STL-10. The review also considers practical aspects such as GPU costs and training times. The paper highlights the progress made in automating GAN architecture design, noting improvements in image quality and search efficiency. However, it also identifies limitations in existing work, such as the narrow focus on specific datasets and image generation types. The review concludes by suggesting potential areas for future research, emphasizing the need for more comprehensive evaluation criteria, including performance on diverse datasets and exploration of conditional and semi-supervised image generation tasks.  
In contrast, the survey by [PERSON] et al. (2024) [17] presents an extensive analysis of evolutionary computation (EC) applied to GANs. It delves into technical aspects such as the design of mutation operators, the formulation of fitness functions based on metrics like inverted generational distance (IGD), and strategies to alleviate mode collapse through Pareto front approximations. The survey also examines scalability--demonstrating how evolved architectures can transfer from low-dimensional (n = 10) to high-dimensional (n = 784) settings--and discusses integrating EC with gradient-based methods to enhance convergence and robustness. Although its scope is broad, covering NAS, parameter tuning, loss function adaptation, and synchronization strategies, the survey is best categorized within the wider NAS reviews rather than a strictly NAS-GAN-focused review.