CasGP: building cascaded hierarchical models using niching

A cascaded model is introduced for mining large datasets using genetic programming without recourse to specialist hardware. Such an algorithm satisfies the seeming conflicting requirements of scalability and accuracy on large datasets by incrementally building GP classifiers through the use of a hierarchical dynamic subset selection algorithm. Models are built incrementally with each layer of the cascade receiving as input the original feature vector, plus the output from the previous layer(s). In order to encourage each layer to explicitly solve new aspects of the problem a combination of sum square error and niching is utilized. Thus, previous layers of the model are considered a niche, and the cost function is a shared error metric.