Evolving rule induction algorithms with multi-objective grammar-based genetic programming
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gp-bibliography.bib Revision:1.8098
- @Article{Pappa:2009:KIS,
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author = "Gisele L. Pappa and Alex A. Freitas",
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title = "Evolving rule induction algorithms with
multi-objective grammar-based genetic programming",
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journal = "Knowledge and Information Systems",
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year = "2009",
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volume = "19",
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number = "3",
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pages = "283--309",
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month = jun,
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publisher = "Springer",
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address = "London",
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keywords = "genetic algorithms, genetic programming, Grammar-based
genetic programming, Pareto optimisation, Rule
induction algorithms, Data mining, Classification",
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ISSN = "0219-1377",
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DOI = "doi:10.1007/s10115-008-0171-1",
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size = "27 pages",
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abstract = "Multi-objective optimisation has played a major role
in solving problems where two or more conflicting
objectives need to be simultaneously optimised. This
paper presents a Multi-Objective grammar-based genetic
programming (MOGGP) system that automatically evolves
complete rule induction algorithms, which in turn
produce both accurate and compact rule models. The
system was compared with a single objective GGP and
three other rule induction algorithms. In total, 20 UCI
data sets were used to generate and test generic rule
induction algorithms, which can be now applied to any
classification data set. Experiments showed that, in
general, the proposed MOGGP finds rule induction
algorithms with competitive predictive accuracies and
more compact models than the algorithms it was compared
with.",
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notes = "Hyper-Heuristic",
- }
Genetic Programming entries for
Gisele L Pappa
Alex Alves Freitas
Citations