Variable Selection in Industrial Datasets using Pareto Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InCollection{smits:2005:GPTP,

• author = "Guido Smits and Arthur Kordon and Katherine Vladislavleva and Elsa Jordaan and Mark Kotanchek",
• title = "Variable Selection in Industrial Datasets using Pareto Genetic Programming",
• booktitle = "Genetic Programming Theory and Practice {III}",
• year = "2005",
• editor = "Tina Yu and Rick L. Riolo and Bill Worzel",
• volume = "9",
• series = "Genetic Programming",
• chapter = "6",
• pages = "79--92",
• address = "Ann Arbor",
• month = "12-14 " # may,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, Symbolic Regression, Variable Selection, Pareto GP",
• ISBN = "0-387-28110-X",
• DOI = "doi:10.1007/0-387-28111-8_6",
• size = "14 pages",
• abstract = "This chapter gives an overview, based on the experience from the Dow Chemical Company, of the importance of variable selection to build robust models from industrial datasets. A quick review of variable selection schemes based on linear techniques is given. A relatively simple fitness inheritance scheme is proposed to do nonlinear sensitivity analysis that is especially effective when combined with Pareto GP. The method is applied to two industrial datasets with good results.",
• notes = "part of \cite{yu:2005:GPTP} Published Jan 2006 after the workshop",

}

Genetic Programming entries for Guido F Smits Arthur K Kordon Ekaterina (Katya) Vladislavleva Elsa Jordaan Mark Kotanchek

Citations