|Title||Adapting the Fitness Function in GP for Data Mining|
|Publication Type||Conference Paper|
|Year of Publication||1999|
|Authors||Eggermont, J, Eiben, AE, van Hemert, JI|
|Conference Name||Springer Lecture Notes on Computer Science|
|Editor||Poli, R, Nordin, P, Langdon, WB, Fogarty, TC|
|Keywords||data mining; genetic programming|
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.