Time series prediction using committee machines of evolutionary neural trees

Evolutionary neural trees (ENTs) are tree-structured neural networks constructed by evolutionary algorithms. We use ENTs to build predictive models of time series data. Time series data are typically characterized by dynamics of the underlying process and thus the robustness of predictions is crucial. We describe a method for making more robust predictions by building committees of ENTs, i.e. CENTs. The method extends the concept of mixing genetic programming (MGP) which makes use of the fact that evolutionary computation produces multiple models as output instead of just one best. Experiments have been performed on the laser time series in which the CENTs outperformed the single best ENTs. We also discuss a theoretical foundation of CENTs using the Bayesian framework for evolutionary computation.