Evolving Input-Output Models of Chemical Process Systems Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7906

@InProceedings{mckay:1996:eiocps,

• author = "Ben McKay and Justin Elsey and Mark J. Willis and Geoffrey W. Barton",
• title = "Evolving Input-Output Models of Chemical Process Systems Using Genetic Programming",
• booktitle = "IFAC '96",
• year = "1996",
• volume = "1",
• address = "San-Fransisco",
• keywords = "genetic algorithms, genetic programming",
• broken = "http://lorien.ncl.ac.uk/sorg/paper3.ps",
• size = "7 pages",
• abstract = "Complex processes are often modelled using input-output data from experimental tests. Regression and neural network modelling techniques are commonly used for this purpose. Unfortunately, these methods provide minimal structural insight into process characteristics. In this contribution, we propose the use of Genetic Programming (GP) as a method for developing input-output process models from experimental data. GP performs symbolic regression, determining both the structure and the complexity of the model during its evolution. This has the advantage that no a priori modelling assumptions have to be made. Moreover, the technique can discriminate between relevant and irrelevant process inputs, yielding parsimonious model structures that accurately represent process characteristics. Two examples are used to demonstrate the utility of the GP technique as a process modelling tool.",
• notes = "MSWord postscript not compatible with unix",

}

Genetic Programming entries for Ben McKay Justin Elsey Mark J Willis Geoffrey W Barton

Citations