Analyzing results of impedance spectroscopy using novel evolutionary programming techniques

Abstract: This paper discusses the application of evolutionary programming methods to the problem of analyzing impedance spectroscopy results. The basic approach is a “direct-problem” one, i.e., to find a time constant distribution function that would create similar impedance results as the measured ones, within experimental error. Two complementary methods have been applied and are discussed here: Genetic Algorithm (GA) and Genetic Programming (GP). A GA can be applied when a known (or desired) model exists, whereas GP can be used to create new models where the only a-priori knowledge is their smoothness and their non-negativity. GP is tuned to prefer relatively non-complex models through penalization of unnecessary complexity.

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