Bayesian Optimization for Dynamic Problems
This work addresses dynamic optimization problems, which are incremental improvements to existing Bayesian optimization methods.
The authors tackled the problem of optimizing dynamic objective functions by extending Bayesian optimization with spatiotemporal Gaussian process priors to track evolving minima, and they evaluated the technique on synthetic and real-world problems.
We propose practical extensions to Bayesian optimization for solving dynamic problems. We model dynamic objective functions using spatiotemporal Gaussian process priors which capture all the instances of the functions over time. Our extensions to Bayesian optimization use the information learnt from this model to guide the tracking of a temporally evolving minimum. By exploiting temporal correlations, the proposed method also determines when to make evaluations, how fast to make those evaluations, and it induces an appropriate budget of steps based on the available information. Lastly, we evaluate our technique on synthetic and real-world problems.