Scalability Analysis of Genetic Programming Classifiers
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Hunt:2012:CEC,
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title = "Scalability Analysis of Genetic Programming
Classifiers",
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author = "Rachel Hunt and Kourosh Neshatian and Mengjie Zhang",
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pages = "509--516",
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booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary
Computation",
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year = "2012",
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editor = "Xiaodong Li",
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month = "10-15 " # jun,
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DOI = "doi:10.1109/CEC.2012.6256520",
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address = "Brisbane, Australia",
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ISBN = "0-7803-8515-2",
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keywords = "genetic algorithms, genetic programming, Complex
Networks and Evolutionary Computation",
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abstract = "Genetic programming (GP) has been used extensively for
classification due to its flexibility, interpretability
and implicit feature manipulation. There are also
disadvantages to the use of GP for classification,
including computational cost, bloating and parameter
determination. This work analyses how GP-based
classifier learning scales with respect to the number
of examples in the classification training data set as
the number of examples grows, and with respect to the
number of features in the classification training data
set as the number of features grows. The scalability of
GP with respect to the number of examples is studied
analytically. The results show that GP scales very well
(in linear or close to linear order) with the number of
examples in the data set and the upper bound on testing
error decreases. The scalability of GP with respect to
the number of features is tested experimentally, with
results showing that the computations increase
exponentially with the number of features.",
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notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the
EPS and the IET.",
- }
Genetic Programming entries for
Rachel Hunt
Kourosh Neshatian
Mengjie Zhang
Citations