  We develop the first Bayesian Optimization algorithm, BLOSSOM, which selects
between multiple alternative acquisition functions and traditional local
optimization at each step. This is combined with a novel stopping condition
based on expected regret. This pairing allows us to obtain the best
characteristics of both local and Bayesian optimization, making efficient use
of function evaluations while yielding superior convergence to the global
minimum on a selection of optimization problems, and also halting optimization
once a principled and intuitive stopping condition has been fulfilled.
