Ensemble Genetic Programming
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gp-bibliography.bib Revision:1.8098
- @InProceedings{Rodrigues:2020:EuroGP,
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author = "Nuno M. Rodrigues and Joao E. Batista and Sara Silva",
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title = "Ensemble Genetic Programming",
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booktitle = "EuroGP 2020: Proceedings of the 23rd European
Conference on Genetic Programming",
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year = "2020",
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month = "15-17 " # apr,
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editor = "Ting Hu and Nuno Lourenco and Eric Medvet",
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series = "LNCS",
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volume = "12101",
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publisher = "Springer Verlag",
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address = "Seville, Spain",
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pages = "151--166",
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organisation = "EvoStar, Species",
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keywords = "genetic algorithms, genetic programming, Ensemble
learning, Binary classification, Machine Learning",
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isbn13 = "978-3-030-44093-0",
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video_url = "https://www.youtube.com/watch?v=7BZebUI9mzI",
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DOI = "doi:10.1007/978-3-030-44094-7_10",
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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.",
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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