Multi-Objectivising Acquisition Functions in Bayesian Optimisation

This repository contains the Python3 code for the MOEE method.

Reproduction of experiments

The python file optimizer.py provides a convenient way to reproduce all experimental evaluations carried out the paper.

> python optimizer.py

Training data

The initial training locations for each of the 30 sets of Latin hypercube samples are located in the training_data directory in this repository with the filename structure ProblemName_number, e.g. the first set of training locations for the Branin problem is stored in Branin_1.npz. To load and inspect these values use the following instructions:

> cd /egreedy
> python
>>> import numpy as np
>>> with np.load('training_data/Branin_1.npz') as data:
        Xtr = data['arr_0']
        Ytr = data['arr_1']
>>> Xtr.shape, Ytr.shape
((4, 2), (4, 1))

Acknowledgement

This repository has been developed based on the work from egreedy [De Ath et al., 2021]. We would like to express our gratitude to the original authors and contributors for their pioneering efforts and for making their code available. Their work has been instrumental in the development of this project.

[De Ath et al., 2021] George De Ath, Richard M. Everson, Alma A. M. Rahat, and Jonathan E. Fieldsend. 2021. Greed Is Good: Exploration and Exploitation Trade-offs in Bayesian Optimisation. ACM Trans. Evol. Learn. Optim. 1, 1, Article 1 (May 2021), 22 pages.