Genetic Programming with Boosting for Ambiguities in Regression Problems

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{paris03,

• author = "Gregory Paris and Denis Robilliard and Cyril Fonlupt",
• title = "Genetic Programming with Boosting for Ambiguities in Regression Problems",
• booktitle = "Genetic Programming, Proceedings of EuroGP'2003",
• year = "2003",
• editor = "Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa",
• volume = "2610",
• series = "LNCS",
• pages = "183--193",
• address = "Essex",
• publisher_address = "Berlin",
• month = "14-16 " # apr,
• organisation = "EvoNet",
• publisher = "Springer-Verlag",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "3-540-00971-X",
• DOI = "doi:10.1007/3-540-36599-0_17",
• 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.",
• notes = "EuroGP'2003 held in conjunction with EvoWorkshops 2003",

}

Genetic Programming entries for Gregory Paris Denis Robilliard Cyril Fonlupt

Citations