ASLib

ASLib is a comprehensive benchmark library dedicated to algorithm selection research. It provides a collection of diverse datasets and experimental results aimed at facilitating the development, comparison, and evaluation of algorithm selection techniques across various problem domains. ASLib was originally developed to standardize benchmarking efforts and promote reproducibility in the field of algorithm selection.

The library contains data from multiple sources, including combinatorial problems, machine learning tasks, and optimization challenges. Each dataset in ASLib includes features describing problem instances, performance measurements of different algorithms, and associated metadata. This structured information allows researchers to analyze the effectiveness of different selection strategies and understand the characteristics influencing algorithm performance.

ASLib is designed to support researchers in selecting suitable algorithms for specific problem types by providing real-world and synthetic data. It enables the evaluation of new algorithm selection models, such as machine learning-based approaches, by offering a common ground for experimentation and comparison. The library is maintained and periodically updated by a community of researchers to incorporate new datasets and improve usability.

Overall, ASLib serves as a valuable resource within the artificial intelligence and computational optimization communities, fostering advances in automated algorithm selection and contributing to more efficient problem-solving methodologies. It is often referenced in academic research and is accessible through various online repositories and platforms dedicated to algorithm benchmarking.