Automatic analytical modeling of EIS data by evolutive programming based on cultural algorithms

Efficiency and accuracy problems in state-of-the-art analytical modeling of electrochemical phenomena through impedance spectroscopy are faced by a Cultural Hybrid Evolutionary Modeling Algorithm (CHEMA). Automatic model definition is improved by an evolutionary program exploiting a solution-search strategy based on a cultural mechanism: information on search advance is transmitted to all potential solutions, rather than only to a small inheriting subset, such as in traditional genetic approach. Experimental results of the proposed approach application to electrochemical impedance spectroscopy for biomedical purposes are presented.