Stepwise Adaptation of Weights for Symbolic Regression with Genetic Programming (bibtex)
@inproceedings{EH00,
	_day = {01},
	abstract = {In this paper we continue study on the Stepwise Adaptation of Weights (SAW) technique. Previous studies on constraint satisfaction and data clas-sification have indicated that SAW is a promising technique to boost the performance of evolutionary algorithms. Here we use SAW to boost per-formance 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 problems.},
	author = {J Eggermont and van Hemert, J},
	booktitle = {Proceedings of the Twelfth Belgium/Netherlands Conference on Artificial Intelligence},
	date-added = {2008-08-18 12:44:11 +0100},
	date-modified = {2008-08-18 12:44:11 +0100},
	editor = {van den Bosch, A and H Weigand},
	keywords = {data mining; evolutionary computation},
	organization = {{BNVKI}, Dutch and the Belgian {AI} Association},
	pages = {259--266},
	pdf = {http://www.vanhemert.co.uk/publications/bnaic00.Stepwise_Adaptation_of_Weights_for_Symbolic_Regression_with_Genetic_Programming.pdf},
	ps.gz = {http://www.vanhemert.co.uk/publications/bnaic00.Stepwise_Adaptation_of_Weights_for_Symbolic_Regression_with_Genetic_Programming.ps.gz},
	title = {Stepwise Adaptation of Weights for Symbolic Regression with Genetic Programming},
	year = {2000}}
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