Symbolic Regression on Noisy Data with Genetic and Gene Expression Programming

Authors:
Elena Bautu;Andrei Bautu;Henri Luchian
Affiliations:
"Ovidius" University;"Mircea cel Bătrân" Naval Academy;"Al. I. Cuza" University
Venue:
SYNASC '05 Proceedings of the Seventh International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
Year:
2005

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Abstract

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.