TY  - CONF
T1  - Adaptive Genetic Programming Applied to New and Existing Simple Regression Problems
T2  - Springer Lecture Notes on Computer Science
Y1  - 2001
A1  - Eggermont, J.
A1  - van Hemert, J. I.
ED  - J. Miller
ED  - Tomassini, M.
ED  - P. L. Lanzi
ED  - C. Ryan
ED  - A. G. B. Tettamanzi
ED  - W. B. Langdon
KW  - data mining
AB  - In this paper we continue our study on adaptive genetic pro-gramming. We use Stepwise Adaptation of Weights to boost performance of a genetic programming algorithm on simple symbolic regression problems. We measure the performance of a standard GP and two variants of SAW extensions on two different symbolic regression prob-lems from literature. Also, we propose a model for randomly generating polynomials which we then use to further test all three GP variants.
JF  - Springer Lecture Notes on Computer Science
PB  - Springer-Verlag, Berlin
SN  - 9-783540-418993
ER  -