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Adapting the Fitness Function in GP for Data Mining

TitleAdapting the Fitness Function in GP for Data Mining
Publication TypeConference Paper
Year of Publication1999
AuthorsEggermont, J, Eiben, AE, van Hemert, JI
Conference NameSpringer Lecture Notes on Computer Science
PublisherSpringer-Verlag, Berlin
EditorPoli, R, Nordin, P, Langdon, WB, Fogarty, TC
ISBN Number3-540-65899-8
Keywordsdata 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.

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