A Vectorial Approach to Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Azzali:2019:EuroGP,
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author = "Irene Azzali and Leonardo Vanneschi and Sara Silva and
Illya Bakurov and Mario Giacobini",
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title = "A Vectorial Approach to Genetic Programming",
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booktitle = "EuroGP 2019: Proceedings of the 22nd European
Conference on Genetic Programming",
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year = "2019",
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month = "24-26 " # apr,
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editor = "Lukas Sekanina and Ting Hu and Nuno Lourenco",
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series = "LNCS",
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volume = "11451",
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publisher = "Springer Verlag",
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address = "Leipzig, Germany",
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pages = "213--227",
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organisation = "EvoStar, Species",
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keywords = "genetic algorithms, genetic programming, Vector-based
representation, Panel Data regression: Poster",
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isbn13 = "978-3-030-16669-4",
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URL = "https://hdl.handle.net/2318/1725688",
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URL = "https://iris.unito.it/retrieve/e27ce42f-33ca-2581-e053-d805fe0acbaa/Azzali.pdf",
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URL = "https://www.springer.com/us/book/9783030166694",
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DOI = "doi:10.1007/978-3-030-16670-0_14",
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size = "16 pages",
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abstract = "Among the various typologies of problems to which
Genetic Programming (GP) has been applied since its
origins, symbolic regression is one of the most
popular. A common situation consists in the prediction
of a target time series based on scalar features and
other time series variables collected from multiple
subjects. To manage this problem with GP data needs a
panel representation where each observation corresponds
to a collection on a subject at a precise time instant.
However, representing data in this form may imply a
loss of information: for instance, the algorithm may
not be able to recognize observations belonging to the
same subject and their recording order. To maintain the
source of knowledge supplied by ordered sequences as
time series, we propose a new approach to GP that keeps
instances of the same observation together in a vector,
introducing vectorial variables as terminals. This new
representation allows aggregate functions in the
primitive GP set, included with the purpose of
describing the behaviour of vectorial variables. In
this work, we perform a comparative analysis of
vectorial GP (VE-GP) against standard GP (ST-GP).
Experiments are conducted on different benchmark
problems to highlight the advantages of this new
approach.",
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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
Irene Azzali
Leonardo Vanneschi
Sara Silva
Illya Bakurov
Mario Giacobini
Citations