Automated machine learning (AutoML) is a new, rapidly growing area of machine learning (ML) that aims to automate the creation of machine learning pipelines, including the design, training and deployment of machine learning models, model selection and hyperparameter tuning. Neural Architecture Search (NAS) is a sub-field of AutoML that focuses specifically on automating the design of neural network architectures. NAS has become increasingly popular due to its potential to significantly improve the performance of deep learning models by discovering optimal neural network architectures. However, the search process can be computationally expensive as it requires training and evaluating a large number of architectures.