## 2 Related Studies
### Identified Gaps in the Literature  
In our review of related studies, we identified a noticeable gap in the literature regarding the application of NAS to GANs. Given the rapid advancements in computer hardware capabilities and the increasing number of publications in the field, there is a compelling need for an up-to-date comprehensive review. Current surveys often focus on narrow aspects of NAS applied to GANs, such as specific datasets or image generation techniques. This paper aims to address these gaps by providing a broader analysis of NAS techniques and their applications to GANs, highlighting limitations and future research opportunities. To systematically differentiate our work from existing surveys, Table 2 compares our review with two foundational prior studies by Ganepola & Wirasingha (2021) [15] and [PERSON] & Wirasingha (2021) [16]. ### Identified Gaps in the Literature  
In our review of related studies, we identified a noticeable gap in the literature regarding the application of NAS to GANs. Given the rapid advancements in computer hardware capabilities and the increasing number of publications in the field, there is a compelling need for an up-to-date comprehensive review. Current surveys often focus on narrow aspects of NAS applied to GANs, such as specific datasets or image generation techniques. This paper aims to address these gaps by providing a broader analysis of NAS techniques and their applications to GANs, highlighting limitations and future research opportunities. To systematically differentiate our work from existing surveys, Table 2 compares our review with two foundational prior studies by Ganepola & Wirasingha (2021) [15] and Buthgamumudalige & Wirasingha (2021) [16]. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Review Paper** & **Year** & **Scope** & **Limitations** & **This Review’s Improvements** \\ \hline [PERSON] \& [PERSON] [15] & 2021 & Image generation, GAN compression; analysis of RL, EA, and gradient-based strategies & Focuses on pre-2021 works with limited detail on mutation/operator design & Extends coverage to 2021–2025 with additional technical insights \\ \hline [PERSON] \& [PERSON] [16] & 2021 & Transferability, supervised learning in NAS-GAN; evaluates IS and FID on CIFAR-10, STL-10 & Limited dataset diversity and minimal discussion of evolutionary operators & Expands evaluation to diverse datasets (CelebA, LSUN) and provides deeper operator-level analysis \\ \hline [PERSON] et al. [18] & 2023 & NAS in computer vision tasks & Superficial NAS-GAN coverage & Provides in-depth NAS-GAN analysis \\ \hline [PERSON] et al. [19] & 2023 & Broad NAS survey (1000+ papers) & Minimal focus on GANs & Emphasizes GAN-specific techniques \\ \hline [PERSON] et al. [17] & 2024 & EC in GANs: architecture search, parameter tuning, loss function adaptation, and synchronization strategies & Broad scope reduces NAS-specific depth; less focus on discrete architecture search & Goes beyond traditional EC-based methods, offering a holistic technical perspective and comprehensive analysis of diverse approaches. \\ \hline This Review & 2025 & Comprehensive NAS-GAN analysis & N/A & Synthesizes 2021–2025 works, addressing reproducibility, dataset diversity, and technical gaps \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of NAS-GAN Review Papers.