Genetic Programming with Boosting for Ambiguities in Regression Problems
Created by W.Langdon from
gp-bibliography.bib Revision:1.8178
- @InProceedings{paris03,
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author = "Gregory Paris and Denis Robilliard and Cyril Fonlupt",
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title = "Genetic Programming with Boosting for Ambiguities in
Regression Problems",
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booktitle = "Genetic Programming, Proceedings of EuroGP'2003",
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year = "2003",
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editor = "Conor Ryan and Terence Soule and Maarten Keijzer and
Edward Tsang and Riccardo Poli and Ernesto Costa",
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volume = "2610",
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series = "LNCS",
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pages = "183--193",
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address = "Essex",
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publisher_address = "Berlin",
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month = "14-16 " # apr,
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organisation = "EvoNet",
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publisher = "Springer-Verlag",
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keywords = "genetic algorithms, genetic programming",
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ISBN = "3-540-00971-X",
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DOI = "doi:10.1007/3-540-36599-0_17",
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abstract = "Facing ambiguities in regression problems is a
challenge. There exists many powerful evolutionary
schemes to deal with regression, however, these
techniques do not usually take into account ambiguities
(i.e. the existence of 2 or more solutions for some or
all points in the domain). Nonetheless ambiguities are
present in some real world inverse problems, and it is
interesting in such cases to provide the user with a
choice of possible solutions. We propose in this
article an approach based on boosted genetic
programming in order to propose several solutions when
ambiguities are detected.",
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notes = "EuroGP'2003 held in conjunction with EvoWorkshops
2003",
- }
Genetic Programming entries for
Gregory Paris
Denis Robilliard
Cyril Fonlupt
Citations