@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}}