The increasing demand for specialized neural network architectures that cater to specific tasks has given rise to automated methods for architecture design, alleviating the need for manual, labor-intensive processes. Neural Architecture Search (NAS) has emerged as a key solution, enabling the discovery of optimized neural networks without human intervention. Among the various approaches to NAS, Evolutionary Algorithm-based NAS has proven to be particularly effective due to its ability to efficiently navigate the vast search space of neural architectures by employing biologically inspired optimization techniques.