Genetic Programming Transforms in Linear Regression Situations

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

@InCollection{Castillo:2010:GPTP,

• author = "Flor Castillo and Arthur Kordon and Carlos Villa",
• title = "Genetic Programming Transforms in Linear Regression Situations",
• booktitle = "Genetic Programming Theory and Practice VIII",
• year = "2010",
• editor = "Rick Riolo and Trent McConaghy and Ekaterina Vladislavleva",
• series = "Genetic and Evolutionary Computation",
• volume = "8",
• address = "Ann Arbor, USA",
• month = "20-22 " # may,
• publisher = "Springer",
• chapter = "11",
• pages = "175--194",
• keywords = "genetic algorithms, genetic programming, Multiple Linear Regression, multicollinearity, soft sensor",
• isbn13 = "978-1-4419-7746-5",
• URL = "http://www.springer.com/computer/ai/book/978-1-4419-7746-5",
• DOI = "doi:10.1007/978-1-4419-7747-2_11",
• abstract = "The chapter summarizes the use of Genetic Programming (GP) inMultiple Linear Regression (MLR) to address multicollinearity and Lack of Fit (LOF). The basis of the proposed method is applying appropriate input transforms (model respecification) that deal with these issues while preserving the information content of the original variables. The transforms are selected from symbolic regression models with optimal trade-off between accuracy of prediction and expressional complexity, generated by multiobjective Pareto-front GP. The chapter includes a comparative study of the GP-generated transforms with Ridge Regression, a variant of ordinary Multiple Linear Regression, which has been a useful and commonly employed approach for reducing multicollinearity. The advantages of GP-generated model respecification are clearly defined and demonstrated. Some recommendations for transforms selection are given as well. The application benefits of the proposed approach are illustrated with a real industrial application in one of the broadest empirical modeling areas in manufacturing - robust inferential sensors. The chapter contributes to increasing the awareness of the potential of GP in statistical model building by MLR.",
• notes = "part of \cite{Riolo:2010:GPTP}",

}

Genetic Programming entries for Flor A Castillo Arthur K Kordon Carlos Villa

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