Latent Variable Symbolic Regression for High-Dimensional Inputs

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InCollection{McConaghy:2009:GPTP,

• author = "Trent McConaghy",
• title = "Latent Variable Symbolic Regression for High-Dimensional Inputs",
• booktitle = "Genetic Programming Theory and Practice {VII}",
• year = "2009",
• editor = "Rick L. Riolo and Una-May O'Reilly and Trent McConaghy",
• series = "Genetic and Evolutionary Computation",
• address = "Ann Arbor",
• month = "14-16 " # may,
• publisher = "Springer",
• chapter = "7",
• pages = "103--118",
• keywords = "genetic algorithms, genetic programming, symbolic regression, latent variables, latent variable regression, LVR, analog, integrated circuits",
• isbn13 = "978-1-4419-1653-2",
• DOI = "doi:10.1007/978-1-4419-1626-6_7",
• URL = "http://trent.st/content/2009-GPTP-caffeine_lvsr.pdf",
• size = "17 pages",
• abstract = "This paper explores symbolic regression when there are hundreds of input variables, and the variables have similar influence which means that variable pruning (a priori, or on-the-fly) will be ineffective. For this problem, traditional genetic programming and many other regression approaches do poorly. We develop a technique based on latent variables, nonlinear sensitivity analysis, and genetic programming designed to manage the challenge. The technique handles 340- input variable problems in minutes, with promise to scale well to even higher dimensions. The technique is successfully verified on 24 real-world circuit modelling problems",
• notes = "part of \cite{Riolo:2009:GPTP}",

}

Genetic Programming entries for Trent McConaghy

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