Predicting problem difficulty for genetic programming applied to data classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Trujillo:2011:GECCO,
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author = "Leonardo Trujillo and Yuliana Martinez and
Edgar Galvan-Lopez and Pierrick Legrand",
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title = "Predicting problem difficulty for genetic programming
applied to data classification",
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booktitle = "GECCO '11: Proceedings of the 13th annual conference
on Genetic and evolutionary computation",
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year = "2011",
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editor = "Natalio Krasnogor and Pier Luca Lanzi and
Andries Engelbrecht and David Pelta and Carlos Gershenson and
Giovanni Squillero and Alex Freitas and
Marylyn Ritchie and Mike Preuss and Christian Gagne and
Yew Soon Ong and Guenther Raidl and Marcus Gallager and
Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and
Nikolaus Hansen and Silja Meyer-Nieberg and
Jim Smith and Gus Eiben and Ester Bernado-Mansilla and
Will Browne and Lee Spector and Tina Yu and Jeff Clune and
Greg Hornby and Man-Leung Wong and Pierre Collet and
Steve Gustafson and Jean-Paul Watson and
Moshe Sipper and Simon Poulding and Gabriela Ochoa and
Marc Schoenauer and Carsten Witt and Anne Auger",
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isbn13 = "978-1-4503-0557-0",
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pages = "1355--1362",
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keywords = "genetic algorithms, genetic programming",
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month = "12-16 " # jul,
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organisation = "SIGEVO",
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address = "Dublin, Ireland",
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DOI = "doi:10.1145/2001576.2001759",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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abstract = "During the development of applied systems, an
important problem that must be addressed is that of
choosing the correct tools for a given domain or
scenario. This general task has been addressed by the
genetic programming (GP) community by attempting to
determine the intrinsic difficulty that a problem poses
for a GP search. This paper presents an approach to
predict the performance of GP applied to data
classification, one of the most common problems in
computer science. The novelty of the proposal is to
extract statistical descriptors and complexity
descriptors of the problem data, and from these
estimate the expected performance of a GP classifier.
We derive two types of predictive models: linear
regression models and symbolic regression models
evolved with GP. The experimental results show that
both approaches provide good estimates of classifier
performance, using synthetic and real-world problems
for validation. In conclusion, this paper shows that it
is possible to accurately predict the expected
performance of a GP classifier using a set of
descriptors that characterize the problem data.",
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notes = "Also known as \cite{2001759} GECCO-2011 A joint
meeting of the twentieth international conference on
genetic algorithms (ICGA-2011) and the sixteenth annual
genetic programming conference (GP-2011)",
- }
Genetic Programming entries for
Leonardo Trujillo
Yuliana Martinez
Edgar Galvan Lopez
Pierrick Legrand
Citations