Systems Modelling Using Genetic Programming

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

@Article{willis:1997:smGP,

• author = "Mark Willis and Hugo Hiden and Mark Hinchliffe and Ben McKay and Geoffrey W. Barton",
• title = "Systems Modelling Using Genetic Programming",
• journal = "Computers in Chemical Engineering",
• year = "1997",
• volume = "21",
• pages = "S1161--S1166",
• note = "Supplemental",
• keywords = "genetic algorithms, genetic programming",
• URL = "http://www.sciencedirect.com/science/article/B6TFT-48B0PBD-6X/2/4f9adb20577e51ae4eb7446eca52b1c2",
• DOI = "doi:10.1016/S0098-1354(97)87659-4",
• size = "5 pages",
• abstract = "In this contribution, a Genetic Programming (GP) algorithm is used to develop empirical models of chemical process systems. GP performs symbolic regression, determining both the structure and the complexity of a model. Initially, steady-state model development using a GP algorithm is considered, next the methodology is extended to the development of dynamic input-output models. The usefulness of the technique is demonstrated by the development of inferential estimation models for two typical processes: a vacuum distillation column and a twin screw cooking extruder.",
• notes = "GP empirical model of vacuum distillation column and a twin screw extruder for processing corn flour. Comparison of artifical neural network and GP",

}

Genetic Programming entries for Mark J Willis Hugo Hiden Mark P Hinchliffe Ben McKay Geoffrey W Barton

Citations