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Bayesian Optimisation for Black Box Functions

Project Overview

This project was part of a competition aimed at optimizing 8 black box functions using Bayesian Optimization over a period of 12 weeks. The code was submitted at the end of the competition, after the results had been evaluated.

The code revolves around utilizing scikit-learn's GaussianProcessRegressor, coupled with Optuna for hyperparameter tuning, and Dask for parallel processing across a cluster.

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  • This project is licensed under the MIT License - please see the license file for details.

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