SASBO: Self-Adapting Safe Bayesian Optimization
Stefano De Blasi, Alexander Gepperth · 2020
Optimizing an unknown objective function under uncertainty requires a balance between exploration (learn more about the objective) and exploitation (find the global optimum). Safe optimization aims to guarantee that safety requirements of the next observation to be performed are fulfilled before performing it. Common approaches are based on Gaussian process regression, also known as Kriging, as a surrogate model iteratively estimating the safety and selecting next observations by Bayesian optimization methods. The hyper-parameter setup usually requires a lot of domain-specific knowledge (if no data is available) or prior data to optimize the hyper-parameters. But it is precisely the lack of these two factors that is the main reason when safe optimization becomes interesting: If the system is unknown and random experiments to generate data are not allowed due to restrictions. We present a novel method for safe Bayesian optimization with self-adapting hyper-parameters, which requires only one safe initial observation and easily selectable initial hyper-parameters. By safely self-adapting the parameters, it is possible to find the global optimum with a reliability regarding safety requirements. Thus, the method can be used even with limited domain-specific expertise and covers a wide range of applications with a minimum of customization.