- author = "Marco Virgolin",
- title = "Genetic Programming is Naturally Suited to Evolve Bagging Ensembles",
- booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference",
- year = "2021",
- editor = "Francisco Chicano and Alberto Tonda and Krzysztof Krawiec and Marde Helbig and Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and Luis Paquete and Gabriela Ochoa and Jaume Bacardit and Christian Gagne and Sanaz Mostaghim and Laetitia Jourdan and Oliver Schuetze and Petr Posik and Carlos Segura and Renato Tinos and Carlos Cotta and Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Fuyuki Ishikawa and Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton",
- pages = "830--839",
- address = "internet",
- series = "GECCO '21",
- month = jul # " 10-14",
- organisation = "SIGEVO",
- publisher = "Association for Computing Machinery",
- publisher_address = "New York, NY, USA",
- keywords = "genetic algorithms, genetic programming, Bagging, ensemble learning, machine learning, evolutionary algorithms",
- isbn13 = "9781450383509",
-
code_url = "
https://github.com/marcovirgolin/2SEGP",
-
DOI = "
10.1145/3449639.3459278",
- size = "10 page",
- abstract = "Learning ensembles by bagging can substantially improve the generalisation performance of low-bias, high-variance estimators, including those evolved by Genetic Programming (GP). To be efficient, modern GP algorithms for evolving (bagging) ensembles typically rely on several (often inter-connected) mechanisms and respective hyper-parameters, ultimately compromising ease of use. we provide experimentalevidence that such complexity might not be warranted. Weshow that minor changes to fitness evaluation and selectionare sufficient to make a simple and otherwise-traditional GP algorithm evolve ensembles efficiently. The key to our proposal is to exploit the way bagging works to compute, for eachindividual in the population, multiple fitness values (insteadof one) at a cost that is only marginally higher than the oneof a normal fitness evaluation. Experimental comparisons onclassification and regression tasks taken and reproduced fromprior studies show that our algorithm fares very well against state-of-the-art ensemble and non-ensemble GP algorithms.We further provide insights into the proposed approach by (i)scaling the ensemble size, (ii) ablating the changes to selection,(iii) observing the evolvability induced by traditional subtreevariation. Code: https://github.com/marcovirgolin/2SEGP.",
-
notes = "Chalmers University of Technology
GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)",
