by J Eggermont and van Hemert, J
Abstract:
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.
Reference:
Adaptive Genetic Programming Applied to New and Existing Simple Regression Problems (J Eggermont and van Hemert, J), In Genetic Programming (J Miller, M Tomassini, PL Lanzi, C Ryan, AGB Tettamanzi, WB Langdon, eds.), Springer, 2001.
Bibtex Entry:
@article{EH2001,
_day = {01},
abstract = {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.},
author = {J Eggermont and van Hemert, J},
journal = {Genetic Programming},
date-added = {2008-08-18 12:44:11 +0100},
date-modified = {2009-01-22 21:46:06 +0000},
editor = {J Miller and M Tomassini and PL Lanzi and C Ryan and AGB Tettamanzi and WB Langdon},
isbn = {9-783540-418993},
keywords = {data mining},
number = {2038},
pages = {23--35},
pdf = {http://www.vanhemert.co.uk/publications/eurogp2001.Adaptive_Genetic_Programming_Applied_to_New_and_Existing_Simple_Regression_Problems.pdf},
ps.gz = {http://www.vanhemert.co.uk/publications/eurogp2001.Adaptive_Genetic_Programming_Applied_to_New_and_Existing_Simple_Regression_Problems.ps.gz},
publisher = {Springer},
series = {{LNCS}},
title = {Adaptive Genetic Programming Applied to New and Existing Simple Regression Problems},
year = {2001}}