Symbolic Optimization

Symbolic optimization aims to automatically discover mathematical expressions that accurately model data, often focusing on physical systems or complex optimization problems. Current research emphasizes integrating deep learning techniques, such as recurrent neural networks and graph neural networks, with evolutionary algorithms and symbolic regression methods to efficiently search the vast space of possible equations, often incorporating constraints like dimensional analysis or physical priors to improve efficiency and accuracy. This field is significant for automating scientific discovery, accelerating the development of efficient algorithms for various applications, and providing more interpretable models compared to purely black-box machine learning approaches.