Cascaded GP models for data mining

The cascade architecture for incremental learning is demonstrated within the context of genetic programming. Such a scheme provides the basis for building steadily more complex models until a desired degree of accuracy is reached. The architecture is demonstrated for several data mining datasets. Efficient training on standard computing platforms is retained using the RSS-DSS algorithm for stochastically sampling datasets in proportion to exemplar ´difficulty´ and ´age´. Finally, the ensuing empirical study provides the basis for recommending the utility of sum square cost functions in the datasets considered.