Abstract
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.
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Acknowledgments
Work financed by the Community of Madrid and co-financed by the EU Structural Funds through the Community of Madrid projects B2017/BMD3773 (GenObIA-CM) and Y2018/NMT-4668 (Micro-Stress - MAP-CM). Also financed by the PhD project IND2020/TIC-17435 and Spanish Ministry of Economy and Competitiveness with number RTI2018-095180-B-I00.
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Parra, D., Gutiérrez, A., Velasco, JM., Garnica, O., Hidalgo, J.I. (2022). Combining the Properties of Random Forest with Grammatical Evolution to Construct Ensemble Models. In: Jiménez Laredo, J.L., Hidalgo, J.I., Babaagba, K.O. (eds) Applications of Evolutionary Computation. EvoApplications 2022. Lecture Notes in Computer Science, vol 13224. Springer, Cham. https://doi.org/10.1007/978-3-031-02462-7_5
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