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A toolbox for detecting structural bias in continuous optimization heuristics.
This package requires an R-installation to be present.
The R packages will be installed automatically upon first importing BIAS.
Install the BIAS toolbox using pip:
pip install struct-bias
This installs the following R packages:
python -m venv env
env/Scripts/Activate.ps1
)pip install -r requirements.txt
example.py
to start using the BIAS toolbox.#example of using the BIAS toolbox to test a DE algorithm
from scipy.optimize import differential_evolution
import numpy as np
from BIAS import BIAS, f0
bounds = [(0,1), (0, 1), (0, 1), (0, 1), (0, 1)]
#do 30 independent runs (5 dimensions)
samples = []
print("Performing optimization method 30 times of f0.")
for i in np.arange(30):
result = differential_evolution(f0, bounds, maxiter=100)
samples.append(result.x)
samples = np.array(samples)
test = BIAS()
print(test.predict(samples, show_figure=True))
y, preds = test.predict_deep(samples)
test.explain(samples, preds, filename="explanation.png")
Note: The code for generating the RF used to predict the type of bias is included, but the full RF is not. These can be found on zenodo: https://doi.org/10.6084/m9.figshare.16546041. The RF models will be downloaded automatically the first time the predict function requires them.
If you use the BIAS toolbox in a scientific publication, we would appreciate using the following citations:
@ARTICLE{9828803,
author={Vermetten, Diederick and van Stein, Bas and Caraffini, Fabio and Minku, Leandro L. and Kononova, Anna V.},
journal={IEEE Transactions on Evolutionary Computation},
title={BIAS: A Toolbox for Benchmarking Structural Bias in the Continuous Domain},
year={2022},
volume={26},
number={6},
pages={1380-1393},
doi={10.1109/TEVC.2022.3189848}
}
@software{niki_van_stein_2023_7803623,
author = {Niki van Stein and
Diederick Vermetten},
title = {Basvanstein/BIAS: v1.1 Deep-BIAS Toolbox},
month = apr,
year = 2023,
publisher = {Zenodo},
version = {v1.1},
doi = {10.5281/zenodo.7803623},
url = {https://doi.org/10.5281/zenodo.7803623}
}