POO (Parallel Optimistic Optimization)
This is a black-box function optimization toolkit that finds the global optimum of a function given a finite budget of noisy evaluations. The algorithm does not require the knowledge of the function’s smoothness. It works for a larger class of functions than what was previously considered, especially for functions that are difficult to optimize, in a precise sense.
Reference: Jean-Bastien Grill, Michal Valko, Rémi Munos: Black-box optimization of noisy functions with unknown smoothness, in Neural Information Processing Systems (NIPS 2015)