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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References
Al-Roomi, A.R., El-Hawary, M.E.: Universal functions originator. Appl. Soft Comput. 94, 106417 (2020)
Ashok, D., Scott, J., Wetzel, S.J., Panju, M., Ganesh, V.: Logic guided genetic algorithms. CoRR abs/2010.11328 (2020)
Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Hidalgo, J.I., Maqueda, E., Risco-Martin, J.L., Cuesta-Infante, A., Colmenar, J.M., Nobel, J.: glucmodel: a monitoring and modeling system for chronic diseases applied to diabetes. J. Biomed. Inform. 48, 183–192 (2014)
Hidalgo, J.I., Colmenar, J.M., Kronberger, G., Winkler, S.M., Garnica, O., Lanchares, J.: Data based prediction of blood glucose concentrations using evolutionary methods. J. Med. Syst. 41(9), 142 (2017)
Jin, Y., Fu, W., Kang, J., Guo, J., Guo, J.: Bayesian symbolic regression (2020)
Kommenda, M., Kronberger, G., Wagner, S., Winkler, S., Affenzeller, M.: On the architecture and implementation of tree-based genetic programming in heuristiclab. In: Proceedings of the 14th Annual Conference Companion on Genetic and Evolutionary Computation, New York, NY, USA , pp. 101–108. GECCO 2012, ACM (2012)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge (1992)
Lourenço, N., Colmenar, J.M., Hidalgo, J.I., Garnica, Ó.: Structured grammatical evolution for glucose prediction in diabetic patients. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1250–1257. ACM (2019)
Lourenço, N., Pereira, F.B., Costa, E.: Unveiling the properties of structured grammatical evolution. Genet. Program Evol. Mach. 17(3), 251–289 (2016). https://doi.org/10.1007/s10710-015-9262-4
Oliveira, L.O.V.B., Martins, J.F.B.S., Miranda, L.F., Pappa, G.L.: Analysing symbolic regression benchmarks under a meta-learning approach. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion. GECCO 2018, New York, NY, USA, pp. 1342–1349. Association for Computing Machinery (2018)
Petersen, B.K., Larma, M.L., Mundhenk, T.N., Santiago, C.P., Kim, S.K., Kim, J.T.: Deep symbolic regression: recovering mathematical expressions from data via risk-seeking policy gradients (2021)
Ryan, C., Nicolau, M., O’Neill, M.: Genetic algorithms using grammatical evolution. In: Foster, J.A., Lutton, E., Miller, J., Ryan, C., Tettamanzi, A. (eds.) EuroGP 2002. LNCS, vol. 2278, pp. 278–287. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45984-7_27
Ryan, Conor, O’Neill, Michael, Collins, J.J. (eds.): Handbook of Grammatical Evolution. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-78717-6
Schapire, R.E., Freund, Y.: Boosting: Foundations and Algorithms. The MIT Press, Cambridge (2012)
Schmidt, M., Lipson, H.: Distilling free-form natural laws from experimental data. Science 324(5923), 81–85 (2009)
Udrescu, S.M., Tegmark, M.: Ai feynman: a physics-inspired method for symbolic regression. Sci. Adv. 6(16), 2631 (2020)
Velasco, J.M., Garnica, O., Lanchares, J., Botella, M., Hidalgo, J.I.: Combining data augmentation, EDAS and grammatical evolution for blood glucose forecasting. Memetic Comput. 10(3), 267–277 (2018)
Zhou, Z.H.: Ensemble Learning, pp. 411–416. Springer, Boston (2015). https://doi.org/10.1007/978-1-4899-7488-4_293
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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