Adapting the Fitness Function in GP for Data Mining
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{eggermont:1999:affGPdm,
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author = "J. Eggermont and A. E. Eiben and J. I. {van Hemert}",
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title = "Adapting the Fitness Function in {GP} for Data
Mining",
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booktitle = "Genetic Programming, Proceedings of EuroGP'99",
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year = "1999",
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editor = "Riccardo Poli and Peter Nordin and
William B. Langdon and Terence C. Fogarty",
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volume = "1598",
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series = "LNCS",
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pages = "193--202",
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address = "Goteborg, Sweden",
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publisher_address = "Berlin",
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month = "26-27 " # may,
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organisation = "EvoNet",
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publisher = "Springer-Verlag",
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keywords = "genetic algorithms, genetic programming, data mining:
Poster",
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ISBN = "3-540-65899-8",
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URL = "http://www.liacs.nl/~jeggermo/publications/eurogp99.ps.gz",
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URL = "http://www.vanhemert.co.uk/publications/eurogp99.Adapting_the_fitness_function_in_GP_for_data_mining.ps.gz",
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DOI = "doi:10.1007/3-540-48885-5_16",
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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.",
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notes = "EuroGP'99, part of \cite{poli:1999:GP}",
- }
Genetic Programming entries for
Jeroen Eggermont
Gusz Eiben
Jano I van Hemert
Citations