Algorithm Selection Framework (ASF)

ASF is a lightweight yet powerful Python library for algorithm selection and empirical performance prediction. It implements various algorithm selection methods, along with algorithm pre-selection, pre-solving schedules and more features to easily create algorithm selection pipeline. ASF is a modular framework that allows easy extensions to tailor made an algorithm selector for every use-case. While ASF includes several built-in machine learning models through scikit-learn and XGBoost, it supports every model that complies with the scikit-learn API. ASF also implements empirical performance prediction, allowing to use different performance scalings.

ASF is written in Python 3 and is intended to use with Python 3.10+. It requires only scikit-learn, NumPy as Pandas as basic requirements. More advanced features (such as hyperparameter optimisation) requires additional dependencies.

Cite Us

If you use ASF, please cite the Zenodo DOI. We are currently working on publishing a paper on ASF, but by then a Zenodo citation will do it.

@software{ASF,
    author = {Hadar Shavit and Holger Hoos},
    doi = {10.5281/zenodo.15288151},
    title = {ASF: Algorithm Selection Framework},
    url = {https://doi.org/10.5281/zenodo.15288151},
    year = 2025,
}