Combining the Properties of Random Forest with Grammatical Evolution to Construct Ensemble Models.

Random Forest algorithm is a prediction technique where a set of tree predictors are combined to construct an ensemble model. If a set of conditions are satisfied, we can affirm that random forest avoids overfitting and converges. On the other hand, grammatical evolution, the popular variant of genetic programming where solutions are built following a grammar, has been successfully applied to a plethora of different problems. Among them, symbolic regression is one of the hits of grammatical evolution. Although encoded in codons and decoded by a grammar, solutions in grammatical evolution are trees that represent mathematical expressions. In this paper, we investigate the convenience of combining the best of both approaches, and we propose Random Structured Grammatical Evolution as an adaptation of Random Forest to a symbolic regression problem. Using structured Grammatical Evolution, a set of weak predictors are built and combined on an ensemble model for prediction.