A comparison of genetic programming and neural networks; new formulations for electrical resistivity of Zn–Fe alloys

It is difficult to automatically solve a problem in a systematic method without using computers. In this study, a comparison between Neural Network (NN) and genetic programming (GEP) soft computing techniques as alternative tools for the formulation of electrical resistivity of zinc–iron (Zn–Fe) alloys for various compositions is proposed. Different formulations are supplied to control the verity and robustness of NN and GEP for the formulation to design composition and electrolyte conditions in certain ranges. The input parameters of the NN and GEP models are weight percentages of zinc and iron in the film and in the electrolyte, measurement temperature, and corrosion voltage of the films. The NN- and GEP-based formulation results are compared with experimental results and found to be quite reliable with a very high correlation (R2=0.998 for GEP and 0.999 for NN).

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