Fig. 1: Schematic of the Bayesian optimization framework with active learning of the design constraints.
From: Bayesian optimization with active learning of design constraints using an entropy-based approach
In every iteration of the framework, both Bayesian classification and Bayesian optimization loops run in parallel. The algorithm starts with Bayesian classification and switches to Bayesian optimization once the average reduction in entropy of all constraint models falls below a threshold. The framework switches back to Bayesian classification if a valuable experiment is suggested accordingly.
