A Comparison of Genetic Programming Variants for Data Classification
In this paper we report the results of a comparative study on different variations of genetic programming applied on binary data classiffication problems. The ffirst genetic programming variant is weighting data records for calculating the classiffication error and modifying the weights during the run. Hereby the algorithm is deffining its own ffitness function in an on-line fashion giving higher weights to ‘hard’ records. Another novel feature we study is the atomic representation, where ‘Booleanization’ of data is not performed at the root, but at the leafs of the trees and only Boolean functions are used in the trees’ body. As a third aspect we look at generational and steady-state models in combination of both features.
KeywordsGenetic Program Boolean Function Constraint Satisfaction Problem Atomic Representation Pima Indian Diabetes
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