Adapting the Fitness Function in GP for Data Mining

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@InProceedings{eggermont:1999:affGPdm,

• author = "J. Eggermont and A. E. Eiben and J. I. {van Hemert}",
• title = "Adapting the Fitness Function in {GP} for Data Mining",
• booktitle = "Genetic Programming, Proceedings of EuroGP'99",
• year = "1999",
• editor = "Riccardo Poli and Peter Nordin and William B. Langdon and Terence C. Fogarty",
• volume = "1598",
• series = "LNCS",
• pages = "193--202",
• address = "Goteborg, Sweden",
• publisher_address = "Berlin",
• month = "26-27 " # may,
• organisation = "EvoNet",
• publisher = "Springer-Verlag",
• keywords = "genetic algorithms, genetic programming, data mining: Poster",
• ISBN = "3-540-65899-8",
• URL = "http://www.liacs.nl/~jeggermo/publications/eurogp99.ps.gz",
• URL = "http://www.vanhemert.co.uk/publications/eurogp99.Adapting_the_fitness_function_in_GP_for_data_mining.ps.gz",
• DOI = "doi:10.1007/3-540-48885-5_16",
• abstract = "We describe how the Stepwise Adaptation of Weights (SAW) technique can be applied in genetic programming. The SAW-ing mechanism has been originally developed for and successfully used in constraint satisfaction problems. Here we identify the very basic underlying ideas behind SAW-ing and point out how it can be used for different types of problems. In particular, SAW-ing is well suited for data mining task s where the fitness of a candidate solution is composed by `local scores' on data records. We evaluate the power of the SAW-ing mechanism on a number of benchmark classification data sets. The results indicate that extending the GP with the SAW-ing feature increases its performance when different types of misclassifications are not weighted differently, but leads to worse results when they are.",
• notes = "EuroGP'99, part of \cite{poli:1999:GP}",

}

Genetic Programming entries for Jeroen Eggermont Gusz Eiben Jano I van Hemert

Citations