Symbolic Regression Model Comparison Approach Using Transmitted Variation

Created by W.Langdon from gp-bibliography.bib Revision:1.7778

@InCollection{Castillo:2012:GPTP,

• author = "Flor A. Castillo and Carlos M. Villa and Arthur K. Kordon",
• title = "Symbolic Regression Model Comparison Approach Using Transmitted Variation",
• booktitle = "Genetic Programming Theory and Practice X",
• year = "2012",
• series = "Genetic and Evolutionary Computation",
• editor = "Rick Riolo and Ekaterina Vladislavleva and Marylyn D. Ritchie and Jason H. Moore",
• publisher = "Springer",
• chapter = "10",
• pages = "139--154",
• address = "Ann Arbor, USA",
• month = "12-14 " # may,
• keywords = "genetic algorithms, genetic programming, Symbolic regression, Model comparison, Transmitted variation, Pareto front, Interpolation, Monte Carlo",
• isbn13 = "978-1-4614-6845-5",
• URL = "http://dx.doi.org/10.1007/978-1-4614-6846-2_10",
• DOI = "doi:10.1007/978-1-4614-6846-2_10",
• 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.",
• 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