Harnessing evolutionary programming for impedance spectroscopy analysis: A case study of mixed ionic-electronic conductors

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Hershkovitz2011104,
  author =       "Shany Hershkovitz and Sioma Baltianski and Yoed Tsur",
  title =        "Harnessing evolutionary programming for impedance
                 spectroscopy analysis: A case study of mixed
                 ionic-electronic conductors",
  journal =      "Solid State Ionics",
  volume =       "188",
  number =       "1",
  pages =        "104--109",
  year =         "2011",
  note =         "9th International Symposium on Systems with Fast Ionic
                 Transport",
  ISSN =         "0167-2738",
  DOI =          "doi:10.1016/j.ssi.2010.10.004",
  URL =          "http://www.sciencedirect.com/science/article/B6TY4-51D5RFW-2/2/78396a47420bfca2e3d664e88b21c461",
  keywords =     "genetic algorithms, genetic programming, Impedance
                 spectroscopy, Warburg elements, Parametric analysis",
  abstract =     "A modified Genetic Programming (GP) method has been
                 developed for the analysis of impedance spectroscopy
                 data. It gives a functional form of the distribution
                 function of relaxation times (DFRT) in the sample. The
                 evolution force is composed of lowering the discrepancy
                 between the model's prediction and the measured data,
                 while keeping the model simple in terms of the number
                 of free parameters. The DFRT that the program seeks for
                 has the form of a peak or a sum of several peaks. All
                 the peaks are known mathematical functions (e.g.,
                 Gaussians). The user can let the program search for
                 many types of peaks or to limit the search. Finding a
                 functional form of the underlying DFRT has two main
                 assets. (a) DFRT is unique and (b) a functional form
                 makes it possible to develop a physical model and
                 compare it to the function. In addition, if more than
                 one peak is present and each peak can be related to a
                 different phenomenon, the peaks can be directly
                 separated for further analysis. The analysis method is
                 demonstrated using synthetic data as well as
                 experimental data of Gd0.1Ce0.9O1.95 (GDC).",
}

Genetic Programming entries for Shani Herskovici Sioma Baltianski Yoed Tsur

Citations