In many real-world optimization problems, we have prior information about what objective function values are achievable. In this paper, we study the scenario that we have either exact knowledge of the minimum value or a, possibly inexact, lower bound on its value. We propose bound-aware Bayesian optimization (BABO), a Bayesian optimization method that uses a new surrogate model and acquisition function to utilize such prior information. We present SlogGP, a new surrogate model that incorporates bound information and adapts the Expected Improvement (EI) acquisition function accordingly. Empirical results on a variety of benchmarks demonstrate the benefit of taking prior information about the optimal value into account, and that the proposed approach significantly outperforms existing techniques. Furthermore, we notice that even in the absence of prior information on the bound, the proposed SlogGP surrogate model still performs better than the standard GP model in most cases, which we explain by its larger expressiveness.
Here is how to use this package:
git clone https://github.com/HanyangHenry-Wang/BABO.git && cd BABO
conda create --name babo-env python=3.9.16 -y
conda activate babo-env
pip install -r requirements.txt Then we can run the experiment:
python run_experiment.pyDetails can be adjusted in run_experiment.py.
We have also re-implemented the OBCGP algorithm, which handles Bayesian Optimization with a known bound. Here is the link: https://github.com/HanyangHenry-Wang/OBCGP_rewrite.git