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Seventh International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC'05)
Symbolic Regression on Noisy Data with Genetic and Gene Expression Programming
Timisoara, Romania
September 25-September 29
ISBN: 0-7695-2453-2
ASCII Text
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Elena Băutu, Andrei Băutu, Henri Luchian, "Symbolic Regression on Noisy Data with Genetic and Gene Expression Programming," Symbolic and Numeric Algorithms for Scientific Computing, International Symposium on, pp. 321-324, Seventh International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC'05), 2005.
 
 
BibTex
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@article{ 10.1109/SYNASC.2005.70,
author = {Elena Băutu and Andrei Băutu and Henri Luchian},
title = {Symbolic Regression on Noisy Data with Genetic and Gene Expression Programming},
journal ={Symbolic and Numeric Algorithms for Scientific Computing, International Symposium on},
volume = {0},
year = {2005},
isbn = {0-7695-2453-2},
pages = {321-324},
doi = {http://doi.ieeecomputersociety.org/10.1109/SYNASC.2005.70},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Symbolic and Numeric Algorithms for Scientific Computing, International Symposium on
TI - Symbolic Regression on Noisy Data with Genetic and Gene Expression Programming
SN - 0-7695-2453-2
SP321
EP324
A1 - Elena Băutu,
A1 - Andrei Băutu,
A1 - Henri Luchian,
PY - 2005
KW - null
VL - 0
JA - Symbolic and Numeric Algorithms for Scientific Computing, International Symposium on
ER -
 
Elena Băutu, "Ovidius" University
Andrei Băutu, "Mircea cel Bătrân" Naval Academy
Henri Luchian, "Al. I. Cuza" University
This paper presents a novel method to perform regression on a finite sample of noisy data. The purpose is to obtain a mathematical model for data which is both reliable and valid, yet the analytical expression is not restricted to any particular form. To obtain a statistical model of the noisy data set we use symbolic regression with pseudo-random number generators. We begin by describing symbolic regression and our implementation of this technique using genetic programming (GP) and gene expression programming (GEP). We present some results for symbolic regression on computer generated and real financial data sets in the final part of this paper.
Citation:
Elena Băutu, Andrei Băutu, Henri Luchian, "Symbolic Regression on Noisy Data with Genetic and Gene Expression Programming," synasc, pp.321-324, Seventh International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC'05), 2005
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