Ensemble Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Rodrigues:2020:EuroGP,

• author = "Nuno M. Rodrigues and Joao E. Batista and Sara Silva",
• title = "Ensemble Genetic Programming",
• booktitle = "EuroGP 2020: Proceedings of the 23rd European Conference on Genetic Programming",
• year = "2020",
• month = "15-17 " # apr,
• editor = "Ting Hu and Nuno Lourenco and Eric Medvet",
• series = "LNCS",
• volume = "12101",
• publisher = "Springer Verlag",
• address = "Seville, Spain",
• pages = "151--166",
• organisation = "EvoStar, Species",
• keywords = "genetic algorithms, genetic programming, Ensemble learning, Binary classification, Machine Learning",
• isbn13 = "978-3-030-44093-0",
• video_url = "https://www.youtube.com/watch?v=7BZebUI9mzI",
• DOI = "doi:10.1007/978-3-030-44094-7_10",
• abstract = "Ensemble learning is a powerful paradigm that has been used in the top state-of-the-art machine learning methods like Random Forests and XGBoost. Inspired by the success of such methods, we have developed a new Genetic Programming method called Ensemble GP. The evolutionary cycle of 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 on eight binary classification problems, achieving results significantly better than standard GP, with much smaller models. Although other methods like M3GP and XGBoost were the best overall, Ensemble GP was able to achieve exceptionally good generalization results on a particularly hard problem where none of the other methods was able to succeed.",
• notes = "http://www.evostar.org/2020/cfp_eurogp.php Part of \cite{Hu:2020:GP} EuroGP'2020 held in conjunction with EvoCOP2020, EvoMusArt2020 and EvoApplications2020",

}

Genetic Programming entries for Nuno Miguel Rodrigues Domingos Joao E Batista Sara Silva

Citations