Analyzing results of impedance spectroscopy using novel evolutionary programming techniques

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.