@inproceedings{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},
	booktitle = {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}}