Adaptive Genetic Programming Applied to New and Existing Simple Regression Problems

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@InProceedings{eggermont_adaptive:2001:EuroGP,

• author = "Jeroen Eggermont and Jano I. {van Hemert}",
• title = "Adaptive Genetic Programming Applied to New and Existing Simple Regression Problems",
• booktitle = "Genetic Programming, Proceedings of EuroGP'2001",
• year = "2001",
• editor = "Julian F. Miller and Marco Tomassini and Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and William B. Langdon",
• volume = "2038",
• series = "LNCS",
• pages = "23--35",
• address = "Lake Como, Italy",
• publisher_address = "Berlin",
• month = "18-20 " # apr,
• organisation = "EvoNET",
• publisher = "Springer-Verlag",
• keywords = "genetic algorithms, genetic programming, Adaptation, Symbolic Regression, Problem Generator, Program Trees, data mining",
• ISBN = "3-540-41899-7",
• URL = "http://www.liacs.nl/~jeggermo/publications/eurogp2001-symreg.ps.gz",
• URL = "http://www.vanhemert.co.uk/publications/eurogp2001.Adaptive_Genetic_Programming_Applied_to_New_and_Existing_Simple_Regression_Problems.ps.gz",
• URL = "http://www.vanhemert.co.uk/publications/eurogp2001.Adaptive_Genetic_Programming_Applied_to_New_and_Existing_Simple_Regression_Problems.pdf",
• DOI = "doi:10.1007/3-540-45355-5_3",
• size = "13 pages",
• abstract = "In this paper we continue our study on adaptive genetic programming. We use Stepwise Adaptation of Weights (SAW) to boost performance of a genetic programming algorithm on simple symbolic regression problems. We measure the performance of a standard GP and two variants of SAW extensions on two different symbolic regression problems from literature. Also, we propose a model for randomly generating polynomials which we then use to further test all three GP variants.",
• notes = "EuroGP'2001, part of \cite{miller:2001:gp}",

}

Genetic Programming entries for Jeroen Eggermont Jano I van Hemert

Citations