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The REVAC method provides an information theoretic measure on how sensitive a parameter is to the choice of its value. This can be used to estimate the relevance of parameters, to choose between different possible sets of parameters, and to allocate resources to the calibration of relevant parameters. The method calibrates the parameters of a model to fulfill the given constraints while retaining a maximum of robustness and generalizability. While it was originally designed to select and calibrate the parameters of an evolutionary process, it can in fact be applied to any complex modelling task.