Multi-Information Source Bayesian Optimization over Combinatorial Structures

This repository contains the code for the experiments reported in the following paper:

Sabbatella, A., Ponti, A., Candelieri, A., & Archetti, F. Bayesian Optimization using simulation based multiple information sources over combinatorial structures.

Python dependencies

Use the requirements.txt file as reference.
You can automatically install all the dependencies using the following command.

pip install -r requirements.txt

How to use the code

There are two main entrypoints to run the experiments with the AGP algorithm:

• run_agp_bqp.py: run the experiments using the AGP model and acquisition function on the Binary Quadratic Programming test problem.
• run_agp_osp.py: run the experiments using the AGP model and acquisition function on the Optimal Sensors Placement problem.

There are two main entrypoints to run the experiments with the MES and GIBBON algorithm:

• run_mes_bqp.py: run the experiments using the MES or GIBBON model and acquisition function on the Binary Quadratic Programming test problem.
• run_mes_osp.py: run the experiments using the MES or GIBBON model and acquisition function on the Optimal Sensors Placement problem.

In all the scripts, it is possible to modify the algorithm configurations and parameters related to the problems.