The basic principle of Gene expression programming
(GEP) is introduced in this paper. An improved GEP
algorithm called IGEP based on dynamic mutation
operator which dealing with the inverse problem of
parameter identification of complex function is
presented, the algorithm complexity of the IGEP was
given in the paper, furthermore, many simulation
results show that the models set up by the paper are
better than the models set up by classic GEP. A future
study will consider the effects of applying IGEP to the
inverse problem which sensitive to the time period.
%0 Conference Paper
%1 Zhang:2006:WCICA
%A Zhang, Kejun
%A Hu, Yuxia
%A Liu, Gang
%B The Sixth World Congress on Intelligent Control and
Automation, WCICA 2006
%D 2006
%I IEEE
%K Gene algorithms, expression genetic programming programming,
%P 3371--3375
%R doi:10.1109/WCICA.2006.1712993
%T An Improved Gene Expression Programming for Solving
Inverse Problem
%V 1
%X The basic principle of Gene expression programming
(GEP) is introduced in this paper. An improved GEP
algorithm called IGEP based on dynamic mutation
operator which dealing with the inverse problem of
parameter identification of complex function is
presented, the algorithm complexity of the IGEP was
given in the paper, furthermore, many simulation
results show that the models set up by the paper are
better than the models set up by classic GEP. A future
study will consider the effects of applying IGEP to the
inverse problem which sensitive to the time period.
%@ 1-4244-0332-4
@inproceedings{Zhang:2006:WCICA,
abstract = {The basic principle of Gene expression programming
(GEP) is introduced in this paper. An improved GEP
algorithm called IGEP based on dynamic mutation
operator which dealing with the inverse problem of
parameter identification of complex function is
presented, the algorithm complexity of the IGEP was
given in the paper, furthermore, many simulation
results show that the models set up by the paper are
better than the models set up by classic GEP. A future
study will consider the effects of applying IGEP to the
inverse problem which sensitive to the time period.},
added-at = {2008-06-19T17:46:40.000+0200},
author = {Zhang, Kejun and Hu, Yuxia and Liu, Gang},
biburl = {https://www.bibsonomy.org/bibtex/28b6862e6e662f0ea537da8a66f46c583/brazovayeye},
booktitle = {The Sixth World Congress on Intelligent Control and
Automation, WCICA 2006},
doi = {doi:10.1109/WCICA.2006.1712993},
interhash = {c7bda27f0ba56396a2d08803c5e27b06},
intrahash = {8b6862e6e662f0ea537da8a66f46c583},
isbn = {1-4244-0332-4},
keywords = {Gene algorithms, expression genetic programming programming,},
month = {21-23 June},
pages = {3371--3375},
publisher = {IEEE},
timestamp = {2008-06-19T17:55:26.000+0200},
title = {An Improved Gene Expression Programming for Solving
Inverse Problem},
volume = 1,
year = 2006
}