Solving Symbolic Regression Problems Using Incremental Evaluation In Genetic Programming

Author

Tuan-Hao, Hoang ; McKay, R.I. ; Essam, Daryl ; Hoai, Nguyen Xuan

Author_Institution

New South Wales Univ., Canberra

fYear

0

fDate

0-0 0

Firstpage

2134

Lastpage

2141

Abstract

In this paper, we show some experimental results using incremental evaluation with tree adjoining grammar guided genetic programming (DEVTAG) on two symbolic regression problems, a benchmark polynomial fitting problem in genetic programming, and a Fourier series problem (sawtooth problem). In our pilot study, we compare results with standard genetic programming (GP) and the original tree adjoining grammar guided genetic programming (TAG3P). Our results on the two problems are good, outperforming both standard GP and the original TAG3P.

Keywords

Fourier series; genetic algorithms; learning (artificial intelligence); regression analysis; trees (mathematics); Fourier series problem; benchmark polynomial fitting problem; incremental evaluation; symbolic regression problems; tree adjoining grammar guided genetic programming; Australia; Biological system modeling; Digital circuits; Evolution (biology); Fourier series; Genetic mutations; Genetic programming; Polynomials; Regression tree analysis; Technical Activities Guide -TAG;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2006. CEC 2006. IEEE Congress on

Conference_Location

Vancouver, BC

Print_ISBN

0-7803-9487-9

Type

conf

DOI

10.1109/CEC.2006.1688570

Filename

1688570