Grid Search

Grid search is a hyperparameter optimization technique used to find the best configuration for machine learning models by exhaustively testing a predefined set of parameter combinations. Current research focuses on improving grid search efficiency, particularly for computationally expensive tasks like deep learning and large language model quantization, through methods such as adaptive grids, heuristic three-stage mechanisms, and loss-error-aware quantization grids. These advancements are crucial for enhancing the performance and scalability of various applications, including medical diagnosis, fraud detection, and renewable energy grid optimization, by enabling the discovery of superior model configurations.