  This note provides a description of a procedure that is designed to
efficiently optimize expensive black-box functions. It uses the response
surface methodology by incorporating radial basis functions as the response
model. A simple method based on a Latin hypercube is used for initial sampling.
A modified version of CORS algorithm with space rescaling is used for the
subsequent sampling. The procedure is able to scale on multicore processors by
performing multiple function evaluations in parallel. The source code of the
procedure is written in Python.
