- author = "B. McKay and M. J. Willis and H. G. Hiden and G. A. Montague and G. W. Barton",
- title = "Identification of Industrial Processes using Genetic Programming",
- booktitle = "Identification in Engineering Systems",
- year = "1996",
- editor = "M. I. Friswell and J. E. Mottershead",
- volume = "1",
- address = "Swansea, UK",
- month = mar,
- publisher = "The Cromwell Press Ltd",
- note = "Proceedings of the International Conference, ICIES",
- keywords = "genetic algorithms, genetic programming",
- ISBN = "0-86076-136-3",
- broken = "http://lorien.ncl.ac.uk/sorg/paper4.ps",
-
URL = "
http://michael.friswell.com/ies96.html",
- size = "10 pages",
- abstract = "Complex processes are often modelled using input-output data from experimental tests. Regression and neural network modelling techniques address this problem to some extent and are being increasingly used to develop optimisation or model-based control algorithms. Unfortunately, the latter methods provide no physical insight into the underlying structural relationships inherent within the data. Genetic Programming (GP) is currently finding application in the modelling of processes from experimental data. The nature of GP-based modelling is that solutions are `evolved' from a set of potential solutions in an environment which mimics Darwinian `survival of the fittest'. GP performs symbolic regression, determining both the structure and the complexity of the model during its evolution. In this contribution two examples are used to demonstrate the utility of the GP technique as a process modelling tool. It is concluded that GP techniques may have further applications in the modelling and identification of complex processes from experimental input-output data.",
-
notes = "MSWord postscript not compatible with unix
cited by \cite{yeun_2004_tec}",
