Stepwise Adaptation of Weights for Symbolic Regression with Genetic Programming

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

@InProceedings{eggermon:2000:bnaic,

• author = "J. Eggermont and J. I. {van Hemert}",
• title = "Stepwise Adaptation of Weights for Symbolic Regression with Genetic Programming",
• booktitle = "Proceedings of the Twelveth Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'00)",
• year = "2000",
• editor = "Antal {van den Bosch} and Hans Weigand",
• pages = "259--266",
• address = "De Efteling, Kaatsheuvel, Holland",
• month = "1-2 " # nov,
• organisation = "BNVKI, Dutch and the Belgian AI Association",
• keywords = "genetic algorithms, genetic programming, data mining",
• URL = "http://www.liacs.nl/~jeggermo/publications/bnaic00.ps.gz",
• URL = "http://www.vanhemert.co.uk/publications/bnaic00.Stepwise_Adaptation_of_Weights_for_Symbolic_Regression_with_Genetic_Programming.ps.gz",
• URL = "http://www.vanhemert.co.uk/publications/bnaic00.Stepwise_Adaptation_of_Weights_for_Symbolic_Regression_with_Genetic_Programming.pdf",
• URL = "http://citeseer.ist.psu.edu/374087.html",
• abstract = "In this paper we continue study on the Stepwise Adaptation of Weights (SAW) technique. Previous studies on constraint satisfaction and data classification have indicated that SAW is a promising technique to boost the performance of evolutionary algorithms. Here we use 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.",

}

Genetic Programming entries for Jeroen Eggermont Jano I van Hemert

Citations