In the paper a genetic programming method for
efficient determination of accurate models for the
change of electrical conductivity of cold formed alloy
CuCrZr was presented. The main characteristic of
genetic programming method, which is one of
evolutionary methods for modelling, is its non-
deterministic way of computing. No assumptions about
the form and size of expressions were made in advance,
but they were left to the self organisation and
intelligence of evolutionary process. Only the best
models, gained by genetic programming were presented in
the paper. Accuracy of the best models was proved with
the testing data set. The comparison between deviation
of genetic models results and regression models results
concerning the experimental results has showed that
genetic models are much more precise and more varied
then regression model. The variety of genetic models
allows us, concerning the demands, to decide for an
optimal genetic model for mathematical description and
prediction of change of electrical conductivity in the
frame of experimental environment.
%0 Journal Article
%1 Gusel:2005:MT
%A Gusel, Leo
%A Brezocnik, Miran
%D 2005
%J Materials and technology
%K algorithms, alloys, bakrove cold conductivity, copper electrical elektricna forming, genetic genetsko hladno modeliranje, modelling, preoblikovanje, prevodnost, programiranje, programming, zlitine
%N 4
%P 107--111
%T Genetic modeling of electrical conductivity of formed
material
%U http://ctklj.ctk.uni-lj.si/kovine/izvodi/mit054/gusel.pdf
%V 39
%X In the paper a genetic programming method for
efficient determination of accurate models for the
change of electrical conductivity of cold formed alloy
CuCrZr was presented. The main characteristic of
genetic programming method, which is one of
evolutionary methods for modelling, is its non-
deterministic way of computing. No assumptions about
the form and size of expressions were made in advance,
but they were left to the self organisation and
intelligence of evolutionary process. Only the best
models, gained by genetic programming were presented in
the paper. Accuracy of the best models was proved with
the testing data set. The comparison between deviation
of genetic models results and regression models results
concerning the experimental results has showed that
genetic models are much more precise and more varied
then regression model. The variety of genetic models
allows us, concerning the demands, to decide for an
optimal genetic model for mathematical description and
prediction of change of electrical conductivity in the
frame of experimental environment.
@article{Gusel:2005:MT,
abstract = {In the paper a genetic programming method for
efficient determination of accurate models for the
change of electrical conductivity of cold formed alloy
CuCrZr was presented. The main characteristic of
genetic programming method, which is one of
evolutionary methods for modelling, is its non-
deterministic way of computing. No assumptions about
the form and size of expressions were made in advance,
but they were left to the self organisation and
intelligence of evolutionary process. Only the best
models, gained by genetic programming were presented in
the paper. Accuracy of the best models was proved with
the testing data set. The comparison between deviation
of genetic models results and regression models results
concerning the experimental results has showed that
genetic models are much more precise and more varied
then regression model. The variety of genetic models
allows us, concerning the demands, to decide for an
optimal genetic model for mathematical description and
prediction of change of electrical conductivity in the
frame of experimental environment.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Gusel, Leo and Brezocnik, Miran},
biburl = {https://www.bibsonomy.org/bibtex/26e812fbca3461fe4e7511f1fc6dd68a0/brazovayeye},
email = {mbrezocnik@uni-mb.si},
interhash = {52426783609b29d259dbb1f17d0ecc4f},
intrahash = {6e812fbca3461fe4e7511f1fc6dd68a0},
issn = {1580-2949},
journal = {Materials and technology},
keywords = {algorithms, alloys, bakrove cold conductivity, copper electrical elektricna forming, genetic genetsko hladno modeliranje, modelling, preoblikovanje, prevodnost, programiranje, programming, zlitine},
number = 4,
pages = {107--111},
size = {5 pages},
timestamp = {2008-06-19T17:40:45.000+0200},
title = {Genetic modeling of electrical conductivity of formed
material},
url = {http://ctklj.ctk.uni-lj.si/kovine/izvodi/mit054/gusel.pdf},
volume = 39,
year = 2005
}