Adaptive Bayesian Optimization for Organic Material Screening

Abstract

Bayesian optimization (BO) is an efficient black-box optimization method which utilizes the power of statistical models built upon previously searched points. The efficacy of BO largely depends on the choice of the statistical model, but it is usually difficult to determine beforehand which model would yield the best optimization performance for a given task. This thesis investigates a modified problem setting for BO where multiple candidate surrogate functions are available, and experiments two novel strategies based on multi-armed bandit algorithms. The proposed strategies attempt to discriminate among the candidate models, and therefore referred as adaptive BO's. The strategies are tested on optimization test bed functions, and the chemical screening scheduling problem where the issue of selecting a surrogate function to use become particularly salient. Surprisingly, it is discovered that the baseline strategy which blends multiple candidate functions uniform-randomly performs non-trivial performance. The results presented in the thesis shows that the relaxation of the number of surrogate functions in BO yields interesting dynamics.