Ensemble Genetic Programming

• URL: http://arxiv.org/abs/2001.07553v1
• Date: Tue, 21 Jan 2020 14:10:37 GMT
• Title: Ensemble Genetic Programming
• Authors: Nuno M. Rodrigues, Jo\~ao E. Batista, Sara Silva
• Abstract summary: Ensemble GP follows the same steps as other Genetic Programming systems, but with differences in the population structure,fitness evaluation and genetic operators. We have tested this method oneight binary classification problems, achieving results significantly betterthan standard GP, with much smaller models.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ensemble learning is a powerful paradigm that has been usedin the top state-of-the-art machine learning methods like Random Forestsand XGBoost. Inspired by the success of such methods, we have devel-oped a new Genetic Programming method called Ensemble GP. The evo-lutionary cycle of Ensemble GP follows the same steps as other GeneticProgramming systems, but with differences in the population structure,fitness evaluation and genetic operators. We have tested this method oneight binary classification problems, achieving results significantly betterthan standard GP, with much smaller models. Although other methodslike M3GP and XGBoost were the best overall, Ensemble GP was able toachieve exceptionally good generalization results on a particularly hardproblem where none of the other methods was able to succeed.

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