Evolutionary Partitioning Regression with Function Stacks
Submitted to CEC 2016
Daniel Ashlock with Joseph Brown

Abstract PDF eprint

Partitioning regression is the simultaneous fitting of multiple models to a set of data and partition 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 with two or three models on simple bivariate data sets. Possible generalizations to the general case of clustering are outlined.