Using Genetic Programming to Develop Inferential Estimation Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{mckay:1996:GPidea,

• author = "Ben McKay and Mark Willis and Gary Montague and Geoffrey W. Barton",
• title = "Using Genetic Programming to Develop Inferential Estimation Algorithms",
• booktitle = "Genetic Programming 1996: Proceedings of the First Annual Conference",
• editor = "John R. Koza and David E. Goldberg and David B. Fogel and Rick L. Riolo",
• year = "1996",
• month = "28--31 " # jul,
• keywords = "genetic algorithms, genetic programming",
• pages = "157--165",
• address = "Stanford University, CA, USA",
• publisher = "MIT Press",
• broken = "http://lorien.ncl.ac.uk/sorg/paper2.ps",
• size = "9 pages",
• abstract = "Genetic Programming (GP) is used to develop inferential estimation algorithms for two industrial chemical processes. Within this context, dynamic modelling procedures (as opposed to static or steady-state modelling) are often required if accurate inferential models are to be developed. Thus, a simple procedure is suggested so that the GP technique may be used for the development of dynamic process models. Using measurements from a vacuum distillation column and an industrial plasticating extrusion process, it is then demonstrated how the GP methodology can be used to develop reliable cost effective process models. A statistical analysis procedure is used to aid in the assessment of GP algorithm settings and to guide in the selection of the final model structure.",
• URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap19.pdf",
• URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
• notes = "GP-96, MSWord postscript not cmpatible with Unix",

}

Genetic Programming entries for Ben McKay Mark J Willis Gary A Montague Geoffrey W Barton

Citations