Polynomial modeling for manufacturing processes using a backward elimination based genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Chan:2010:cec2,

• author = "Kit Yan Chan and Tharam Singh Dillon and Che Kit Kwong",
• title = "Polynomial modeling for manufacturing processes using a backward elimination based genetic programming",
• booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
• year = "2010",
• address = "Barcelona, Spain",
• month = "18-23 " # jul,
• publisher = "IEEE Press",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-1-4244-6910-9",
• abstract = "Even if genetic programming (GP) has rich literature in development of polynomial models for manufacturing processes, the polynomial models may contain redundant terms which may cause the overfitted models. In other words, those models have good accuracy on training data sets but poor accuracy on untrained data sets. In this paper, a mechanism which aims at avoiding overfitting is proposed based on a statistical method, backward elimination, which intends to eliminate insignificant terms in polynomial models. By modeling a solder paste dispenser for electronic manufacturing, results show that the insignificant terms in the polynomial model can be eliminated by the proposed mechanism. Results also show that the polynomial model generated by the proposed GP can achieve better predictions than the existing methods.",
• DOI = "doi:10.1109/CEC.2010.5586309",
• notes = "WCCI 2010. Also known as \cite{5586309}",

}

Genetic Programming entries for Kit Yan Chan Tharam S Dillon Che Kit Kwong

Citations