Evolutionary partitioning regression with function stacks

Partitioning regression is the simultaneous fitting of multiple models to a set of data and partitioning of that data into easily modelled classes. The key to partitioning regression with evolution is minimum error assignment during fitness evaluation. Assigning a point to the model for which it has the least error while using evolution to minimize total model error encourages the evolution of models that cleanly partition data. This study demonstrates the efficacy of partitioning regression using two or three models on simple bivariate data sets. Two novel multi-model representations, a simple evolutionary parameters setting algorithm and one based on using directed acyclic graphs representations for genetic programming are used. Possible generalizations to the general case of clustering are outlined.