Summary of On the Development Of a Practical Bayesian Optimisation Algorithm For Expensive Experiments and Simulations with Changing Environmental Conditions, by Mike Diessner et al.
On the development of a practical Bayesian optimisation algorithm for expensive experiments and simulations with changing environmental conditions
by Mike Diessner, Kevin J. Wilson, Richard D. Whalley
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers extend Bayesian optimization to optimize systems in changing environments with both controllable and uncontrollable parameters. They propose a method that fits a global surrogate model over all variables but optimizes only the controllable parameters conditional on measurements of the uncontrollable variables. The algorithm, called ENVBO, is validated on two synthetic test functions and then applied to a wind farm simulator. Results show that ENVBO finds solutions for the full domain of the environmental variable outperforming other optimization algorithms in most cases while using a fraction of their evaluation budget. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to make better decisions when things are changing around us. Normally, scientists design experiments to control all the factors they can’t control, but this isn’t always possible. The researchers developed a new way to optimize systems by considering both what we can control and what we can’t. They tested their idea on two made-up problems and then applied it to a real-world problem – a wind farm simulator. Their method worked really well and could be used in many different situations where things are changing. |
Keywords
* Artificial intelligence * Optimization
