Algorithm Configuration

Algorithm configuration (AC) focuses on automatically finding the optimal parameter settings for algorithms to solve specific problems, improving efficiency and performance. Current research emphasizes dynamic algorithm configuration (DAC), which adapts parameters during execution, often employing reinforcement learning and neural networks, including deep neural networks, to learn effective parameter control policies. This field is significant because effective AC and DAC techniques can drastically improve the performance of various algorithms across diverse applications, ranging from optimization problems to robotics and machine learning. Benchmarking and the development of robust, generalizable methods remain key challenges.