A comparison of predictive measures of problem difficulty for classification with Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{ERA_2012_NSC,
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author = "Leonardo Trujillo and Yuliana Martinez and
Edgar Galvan-Lopez and Pierrick Legrand",
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title = "A comparison of predictive measures of problem
difficulty for classification with Genetic
Programming",
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booktitle = "ERA 2012",
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year = "2012",
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address = "Tijuana, Mexico",
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month = nov # " 14-16",
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organisation = "El Centro de Investigacion y Desarrollo de Tecnologia
Digital, CITEDI, Research and Development Center
Digital Technology",
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keywords = "genetic algorithms, genetic programming, Performance
prediction, Classification",
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URL = "http://hal.inria.fr/hal-00757363",
-
URL = "http://hal.inria.fr/docs/00/75/73/63/PDF/ERA_2012_NSC.pdf",
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bibsource = "OAI-PMH server at hal.archives-ouvertes.fr",
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language = "ENG",
-
oai = "oai:hal.inria.fr:hal-00757363",
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type = "conference proceeding",
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size = "12 pages",
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abstract = "In the field of Genetic Programming (GP) a question
exists that is difficult to solve; how can problem
difficulty be determined? In this paper the overall
goal is to develop predictive tools that estimate how
difficult a problem is for GP to solve. Here we analyse
two groups of methods. We call the first group
Evolvability Indicators (EI), measures that capture how
amendable the fitness landscape is to a GP search. The
second are Predictors of Expected Performance (PEP),
models that take as input a set of descriptive
attributes of a problem and predict the expected
performance of a GP system. These predictive variables
are domain specific thus problems are described in the
context of the problem domain. This paper compares an
EI, the Negative Slope Coefficient, and a PEP model for
a GP classifier. Results suggest that the EI does not
correlate with the performance of GP classifiers.
Conversely, the PEP models show a high correlation with
GP performance. It appears that while an EI estimates
the difficulty of a search, it does not necessarily
capture the difficulty of the underlying problem.
However, while PEP models treat GP as a computational
black-box, they can produce accurate performance
predictions.",
-
notes = "http://era.citedi.mx/site/",
- }
Genetic Programming entries for
Leonardo Trujillo
Yuliana Martinez
Edgar Galvan Lopez
Pierrick Legrand
Citations