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Theory-Guided Online Algorithm Selection: Benchmarking Algorithm Switching for Pseudo-Boolean Optimization – Reproducibility Files
# Reproducibility files and additional results ## Additional Material The addional results mentioned in the paper can be found in the highlighted pdf file. ## Content: - data_collection: contains al
File Size352.9 MB
Data TypeDataset
Published2026
Licensecc-by-4.0
Total Views73
Total Downloads45
# Reproducibility files and additional results
## Additional Material
The addional results mentioned in the paper can be found in the highlighted pdf file.
## Content:
– data_collection: contains all algorithms and code to collect performance data on the selected benchmarks. In the main function, there are a set of experiments to collect data, each of which should be run if all experiments from the paper should be reproduced.
– Data_raw.zip: Split up in the same way as the experiments in data_collection.py, this contains the IOH-logs from the different algorithm-benchmark combinations.
– Visualize.ipynb: Notebook which takes the performance data and creates all figures (both the ones in the paper + the additional figures provided here)
– Figures.zip: The full set of figures generated
– Supplementary_materials.pdf: The pdf with the figures and additional explanations on results which did not fit into the main body of the paper.
# Reproducibility instructions
## Data collection
For the data collection, the only required package is iohexperimenter (ioh on pip). Then, the data_collection script can be run to get the data for a given experiment by modifying the used functions, algorithms and dimensionalities.
The code has 4 interfaces for running algorithms:
– run_algorithm: works for the base algorithms with flexible population size. Argument order [algname, function_id, dim, lambda_, override]
– run_algorithm_switch: works for the switching algorithm with flexible population size and stagnation threshold. Argument order [algname, function_id, dim, lambda_, stagnation_threshold, override]
– run_alg_fixedpop: same as run_algorithm, but without the population size variability
– run_sw_alg_fixedpop: same as run_algorithm_switch, but without the population size variability
The data provided is split into 5 separate folders for different parts of the experiments (separating the base data for stagnation threshold, the SAT problems and the fixed population size experiments)
## Visualization
Self-contained notebook. Can be run on the provided data or the rerun data from data_Collection.py.
This notebook relies on the iohinspector package for data processing, and on seaborn for plotting.
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Theory-Guided Online Algorithm Selection: Benchmarking Algorithm Switching for… (Full Dataset)352.9 MB
⬇Files are hosted on the source repository. Click download to access the full dataset.
Antipov, Denis (2026). Theory-Guided Online Algorithm Selection: Benchmarking Algorithm Switching for Pseudo-Boolean Optimization – Reproducibility Files. https://doi.org/10.5281/zenodo.19595087