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HEBO (Heteroscedastic and Evolutionary Bayesian Optimisation)

HEBO addresses the problem of noisy and heterogeneous objective functions by using a heteroscedastic Gaussian process and an evolutionary algorithm.

Class or Function Names

  • HEBOSampler

Installation

pip install -r https://hub.optuna.org/samplers/hebo/requirements.txt
git clone git@github.com:huawei-noah/HEBO.git
cd HEBO/HEBO
pip install -e .

Example

search_space = {
    "x": FloatDistribution(-10, 10),
    "y": IntDistribution(0, 10),

}
sampler = HEBOSampler(search_space)
study = optuna.create_study(sampler=sampler)

See example.py for a full example. History Plot

Others

HEBO is the winning submission to the NeurIPS 2020 Black-Box Optimisation Challenge. Please refer to the official repository of HEBO for more details.

Reference

Cowen-Rivers, Alexander I., et al. “An Empirical Study of Assumptions in Bayesian Optimisation.” arXiv preprint arXiv:2012.03826 (2021).

Package
samplers/hebo
Author
HideakiImamura
License
MIT License
Verified Optuna version
  • 3.6.1
Last update
2024-11-14