TY  - CONF
T1  - Adapting the Fitness Function in GP for Data Mining
T2  - Springer Lecture Notes on Computer Science
Y1  - 1999
A1  - Eggermont, J.
A1  - Eiben, A. E.
A1  - van Hemert, J. I.
ED  - R. Poli
ED  - P. Nordin
ED  - W. B. Langdon
ED  - T. C. Fogarty
KW  - data mining
KW  - genetic programming
AB  - 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.
JF  - Springer Lecture Notes on Computer Science
PB  - Springer-Verlag, Berlin
SN  - 3-540-65899-8
ER  -