We present and evaluate a method for estimating the relevance and calibrating the values of parameters of an evolutionary algorithm. The 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 evolutionary algorithm to reach a high performance, while retaining a maximum of robustness and generalizability. We demonstrate the method on an agent-based application from evolutionary economics and show how the method helps to design an evolutionary algorithm that allows the agents to achieve a high welfare with a minimum of algorithmic complexity.
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