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This note provides a description of a procedure that is designed toefficiently optimize expensive black-box functions. It uses the responsesurface methodology by incorporating radial basis functions as the responsemodel. 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 thesubsequent sampling. The procedure is able to scale on multicore processors byperforming multiple function evaluations in parallel. The source code of theprocedure is written in Python.

Blackbox: A procedure for parallel optimization of expensive black-box functions