Towards Efficient Training on Large Datasets for Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{currey:2004:CSCSI,
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author = "Robert Curry and Malcolm I. Heywood",
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title = "Towards Efficient Training on Large Datasets for
Genetic Programming",
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booktitle = "17th Conference of the Canadian Society for
Computational Studies of Intelligence",
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year = "2004",
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editor = "Ahmed Y. Tawfik and Scott D. Goodwin",
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volume = "3060",
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series = "LNAI",
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pages = "161--174",
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address = "London, Ontario, Canada",
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month = "17-19 " # may,
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publisher = "Springer-Verlag",
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email = "mheywood@cs.dal.ca",
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keywords = "genetic algorithms, genetic programming",
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ISBN = "3-540-22004-6",
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URL = "http://users.cs.dal.ca/~mheywood/X-files/Publications/robert-CaAI04.pdf",
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DOI = "doi:10.1007/b97823",
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abstract = "Genetic programming (GP) has the potential to provide
unique solutions to a wide range of supervised learning
problems. The technique, however, does suffer from a
widely acknowledged computational overhead. As a
consequence applications of GP are often confined to
datasets consisting of hundreds of training exemplars
as opposed to tens of thousands of exemplars, thus
limiting the widespread applicability of the approach.
In this work we propose and thoroughly investigate a
data sub-sampling algorithm hierarchical dynamic subset
selection that filters the initial training dataset in
parallel with the learning process. The motivation
being to focus the GP training on the most difficult or
least recently visited exemplars. To do so, we build on
the dynamic sub-set selection algorithm of Gathercole
\cite{ga94aGathercole} and extend it into a hierarchy
of subset selections, thus matching the concept of a
memory hierarchy supported in modern computers. Such an
approach provides for the training of GP solutions to
data sets with hundreds of thousands of exemplars in
tens of minutes whilst matching the classification
accuracies of more classical approaches.",
- }
Genetic Programming entries for
Robert Curry
Malcolm Heywood
Citations