On the development of a practical Bayesian optimisation algorithm for expensive experiments and simulations with changing environmental conditions

Experiments in engineering are typically conducted in controlled environments where parameters can be set to any desired value. This assumes that the same applies in a real-world setting -- an assumption that is often incorrect as many experiments are influenced by uncontrollable environmental conditions such as temperature, humidity and wind speed. When optimising such experiments, the focus should lie on finding optimal values conditionally on these uncontrollable variables. This article extends Bayesian optimisation to the optimisation of systems in changing …

algorithm bayesian cs.lg development engineering environmental environments optimisation parameters practical set simulations stat.ml value world