Can Genetic Programming Do Manifold Learning Too?

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Lensen:2019:EuroGP,

• author = "Andrew Lensen and Bing Xue and Mengjie Zhang",
• title = "Can Genetic Programming Do Manifold Learning Too?",
• booktitle = "EuroGP 2019: Proceedings of the 22nd European Conference on Genetic Programming",
• year = "2019",
• month = "24-26 " # apr,
• editor = "Lukas Sekanina and Ting Hu and Nuno Lourenco",
• series = "LNCS",
• volume = "11451",
• publisher = "Springer Verlag",
• address = "Leipzig, Germany",
• pages = "114--130",
• organisation = "EvoStar, Species",
• note = "Best paper",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-030-16669-4",
• URL = "https://www.springer.com/us/book/9783030166694",
• DOI = "doi:10.1007/978-3-030-16670-0_8",
• size = "16 pages",
• abstract = "Exploratory data analysis is a fundamental aspect of knowledge discovery that aims to find the main characteristics of a dataset. Dimensionality reduction, such as manifold learning, is often used to reduce the number of features in a dataset to a manageable level for human interpretation. Despite this, most manifold learning techniques do not explain anything about the original features nor the true characteristics of a dataset. In this paper, we propose a genetic programming approach to manifold learning called GP-MaL which evolves functional mappings from a high-dimensional space to a lower dimensional space through the use of interpretable trees. We show that GP-MaL is competitive with existing manifold learning algorithms, while producing models that can be interpreted and re-used on unseen data. A number of promising future directions of research are found in the process.",
• notes = "http://www.evostar.org/2019/cfp_eurogp.php#abstracts Part of \cite{Sekanina:2019:GP} EuroGP'2019 held in conjunction with EvoCOP2019, EvoMusArt2019 and EvoApplications2019",

}

Genetic Programming entries for Andrew Lensen Bing Xue Mengjie Zhang

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