- author = "Aurelie Boisbunon and Carlo Fanara and Ingrid Grenet and Jonathan Daeden and Alexis Vighi and Marc Schoenauer",
- title = "Zoetrope Genetic Programming for regression",
- 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 = "776--784",
- 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, ZGP, Symbolic regression, regression, Representation of mathematical functions, Supervised learning by regression, Learning linear models, Feature selection",
- isbn13 = "9781450383509",
- hal_id = "hal-03155694",
- hal_version = "v1",
-
URL = "
https://hal.archives-ouvertes.fr/hal-03155694/file/ZGP_regression_arxiv.pdf",
-
DOI = "
doi:10.1145/3449639.3459349",
-
code_url = "
https://gitlab.devenv.mydatamodels.com/publications/bench-zgp-symbolic-regression/-/tree/master/",
- size = "9 pages",
- abstract = "The Zoetrope Genetic Programming (ZGP) algorithm is based on an original representation for mathematical expressions, targetting evolutionary symbolic regression. The zoetropic representation uses repeated fusion operations between partial expressions, starting from the terminal set. Repeated fusions within an individual gradually generate more complex expressions, ending up in what can be viewed as new features. These features are then linearly combined to best fit the training data. ZGP individuals then undergo specific crossover and mutation operators, and selection takes place between parents and offspring. ZGP is validated using a large number of public domain regression datasets, and compared to other symbolic regression algorithms, as well as to traditional machine learning algorithms. ZGP reaches state-of-the-art performances with respect to both types of algorithms, and demonstrates a low computational time compared to other symbolic regression approaches.",
-
notes = "Supplemental Material
https://dl.acm.org/action/downloadSupplement?doi=10.1145%2F3449639.3459349&file=p776-boisbunon_suppl.pdf
MyDataModels, Sophia Antipolis, France
GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)",
