Adapting the Fitness Function in GP for Data Mining

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 wellsuited 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.