An implementation of the paper "NeuralBO: A black-box optimization algorithm using deep neural networks".

Introduction

This is an official implementation of paper "NeuralBO: A black-box optimization algorithm using deep neural networks" published at Neurocomputing journal.

Dependency

This work was tested with PyTorch 2.0.1, CUDA 11.7, python 3.11.5

conda install pytorch cudatoolkit=11.7 -c pytorch

Run the algorithm

python main.py -cfg <path_to_config_file>

Customize your objective function

To define your new objective function, please consider the example Ackley function in utils/objective.py.

Plot and visualization

To plot the minimum values found by the algorithm, use utils/plot_min_values.py To plot the minimal points chosen at each optimization step, use utils/visualization.py

Citation

Please consider citing this work in your publications if it helps your research.

@article{phan2023neuralbo,
  title={NeuralBO: A black-box optimization algorithm using deep neural networks},
  author={Phan-Trong, Dat and Tran-The, Hung and Gupta, Sunil},
  journal={Neurocomputing},
  volume={559},
  pages={126776},
  year={2023},
  publisher={Elsevier}
}

phantrdat / neuralbo