Search Based

Search-based methods are increasingly used to solve complex problems across diverse scientific domains, primarily aiming to efficiently explore vast search spaces and optimize solutions based on defined objectives. Current research focuses on integrating search algorithms (like genetic algorithms, evolutionary strategies, and tree search) with machine learning models (especially large language models) to enhance performance and understandability in areas such as software testing, automated code generation, and hyperparameter optimization. This approach holds significant promise for improving the efficiency and effectiveness of various tasks, ranging from generating high-coverage unit tests and improving software architectures to optimizing AI model parameters and enhancing the safety and reliability of autonomous systems.