A new genetic programming approach in symbolic
regression
S. Xiong, W. Wang, and F. Li. Proceedings 15th IEEE International Conference on
Tools with Artificial Intelligence, page 161--165. IEEE, (3-5 November 2003)
Abstract
Genetic programming (GP) has been applied to symbolic
regression problem for a long time. The symbolic
regression is to discover a function that can fit a
finite set of sample data. These sample data can be
guided by a simple function, which is continuous and
smooth, but in a complex system, the sample data can be
produced by a discontinuous or non-smooth function.
When conventional GP is applied to such complex
system's regression, it gets poor performance. This
paper proposed a new GP representation and algorithm
that can be applied to both continuous function's
regression and discontinuous function's regression. The
proposed approach is able to identify both the
sub-functions and the discontinuity points
simultaneously. The numerical experimental results show
that the new GP is able to obtain higher success rate,
higher convergence rate and better solutions than
conventional GP in such complex system's regression.
%0 Conference Paper
%1 Xiong:2003:TAI
%A Xiong, Shengwu
%A Wang, Weiwu
%A Li, Feng
%B Proceedings 15th IEEE International Conference on
Tools with Artificial Intelligence
%D 2003
%I IEEE
%K algorithms, genetic programming
%P 161--165
%T A new genetic programming approach in symbolic
regression
%U http://ieeexplore.ieee.org/iel5/8840/27974/01250185.pdf?tp=&arnumber=1250185&isnumber=27974
%X Genetic programming (GP) has been applied to symbolic
regression problem for a long time. The symbolic
regression is to discover a function that can fit a
finite set of sample data. These sample data can be
guided by a simple function, which is continuous and
smooth, but in a complex system, the sample data can be
produced by a discontinuous or non-smooth function.
When conventional GP is applied to such complex
system's regression, it gets poor performance. This
paper proposed a new GP representation and algorithm
that can be applied to both continuous function's
regression and discontinuous function's regression. The
proposed approach is able to identify both the
sub-functions and the discontinuity points
simultaneously. The numerical experimental results show
that the new GP is able to obtain higher success rate,
higher convergence rate and better solutions than
conventional GP in such complex system's regression.
@inproceedings{Xiong:2003:TAI,
abstract = {Genetic programming (GP) has been applied to symbolic
regression problem for a long time. The symbolic
regression is to discover a function that can fit a
finite set of sample data. These sample data can be
guided by a simple function, which is continuous and
smooth, but in a complex system, the sample data can be
produced by a discontinuous or non-smooth function.
When conventional GP is applied to such complex
system's regression, it gets poor performance. This
paper proposed a new GP representation and algorithm
that can be applied to both continuous function's
regression and discontinuous function's regression. The
proposed approach is able to identify both the
sub-functions and the discontinuity points
simultaneously. The numerical experimental results show
that the new GP is able to obtain higher success rate,
higher convergence rate and better solutions than
conventional GP in such complex system's regression.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Xiong, Shengwu and Wang, Weiwu and Li, Feng},
biburl = {https://www.bibsonomy.org/bibtex/2392097aa8f4bb7586577d2dc5d9e63bb/brazovayeye},
booktitle = {Proceedings 15th IEEE International Conference on
Tools with Artificial Intelligence},
interhash = {d9a95dc833dcfb7ac623ebf615b94599},
intrahash = {392097aa8f4bb7586577d2dc5d9e63bb},
issn = {1082-3409},
keywords = {algorithms, genetic programming},
month = {3-5 November},
notes = {Sch. of Comput. Sci. & Technol., Wuhan Univ. of
Technol., China},
pages = {161--165},
publisher = {IEEE},
timestamp = {2008-06-19T17:54:38.000+0200},
title = {A new genetic programming approach in symbolic
regression},
url = {http://ieeexplore.ieee.org/iel5/8840/27974/01250185.pdf?tp=&arnumber=1250185&isnumber=27974},
year = 2003
}