@inproceedings{EEH99a,
	_day = {26},
	abstract = {In this paper we describe how the Stepwise Adaptation of Weights (SAW) technique can be applied in genetic programming. The SAW-ing mechanism has been originally developed for and successfully used in EAs for constraint satisfaction problems. Here we identify the very basic underlying ideas behind SAW-ing and point out how it can be used for different types of problems. In particular, SAW-ing is well suited for data mining tasks where the fitness of a candidate solution is composed by `local scores' on data records. We evaluate the power of the SAW-ing mechanism on a number of benchmark classification data sets. The results indicate that extending the GP with the SAW-ing feature increases its performance when different types of misclassifications are not weighted differently, but leads to worse results when they are.},
	author = {J Eggermont and AE Eiben and van Hemert, J},
	booktitle = {Genetic Programming},
	date-added = {2008-08-18 12:44:11 +0100},
	date-modified = {2009-01-22 21:45:48 +0000},
	editor = {R Poli and P Nordin and WB Langdon and TC Fogarty},
	isbn = {3-540-65899-8},
	keywords = {data mining; evolutionary computation},
	number = {1598},
	pages = {195--204},
	ps.gz = {http://www.vanhemert.co.uk/publications/eurogp99.Adapting_the_fitness_function_in_GP_for_data_mining.ps.gz},
	publisher = {Springer},
	series = {{LNCS}},
	title = {Adapting the Fitness Function in {GP} for Data Mining},
	year = {1999}}