Symbolic Regression Model Comparison Approach Using Transmitted Variation
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InCollection{Castillo:2012:GPTP,
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author = "Flor A. Castillo and Carlos M. Villa and
Arthur K. Kordon",
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title = "Symbolic Regression Model Comparison Approach Using
Transmitted Variation",
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booktitle = "Genetic Programming Theory and Practice X",
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year = "2012",
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series = "Genetic and Evolutionary Computation",
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editor = "Rick Riolo and Ekaterina Vladislavleva and
Marylyn D. Ritchie and Jason H. Moore",
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publisher = "Springer",
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chapter = "10",
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pages = "139--154",
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address = "Ann Arbor, USA",
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month = "12-14 " # may,
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keywords = "genetic algorithms, genetic programming, Symbolic
regression, Model comparison, Transmitted variation,
Pareto front, Interpolation, Monte Carlo",
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isbn13 = "978-1-4614-6845-5",
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URL = "http://dx.doi.org/10.1007/978-1-4614-6846-2_10",
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DOI = "doi:10.1007/978-1-4614-6846-2_10",
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abstract = "Model evaluation in symbolic regression generated by
GP is of critical importance for successful industrial
applications. Typically this model evaluation is
achieved by a tradeoff between model complexity and R
squared. The chapter introduces a model comparison
approach based on the transmission of variation from
the inputs to the output. The approach is illustrated
with three different data sets from real industrial
applications.",
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notes = "part of \cite{Riolo:2012:GPTP} published after the
workshop in 2013",
- }
Genetic Programming entries for
Flor A Castillo
Carlos Villa
Arthur K Kordon
Citations