SPOT is a (open source) toolbox for surrogate model based optimization. SPOT uses Design of Experiment methods, various statistical modeling techniques, classical optimization algorithms and meta-heuristics to solve expensive (ressource/time-consuming) optimization problems. The sequential parameter optimization (SPO) concept was originally developed by Thomas Bartz-Beielstein for the purpose of algorithm tuning, but has since been applied to a large variety of problems.

The R-version combines contributions of several people, see: external link: https://cran.r-project.org/package=SPOT.