Abstract
Genetic algorithms (GA) has given rise to two new
fields of research where (global) optimisation is of
crucial importance: 'genetic based machine learning'
(GBML) and 'genetic programming' (GP). An introduction
by the authors to GA and GBML was given in two previous
papers (Eng. Appl. Artif. Intell. 9(6) (1996) 681; Eng.
Appl. Artif. Intell. 13(4) (2000) 381). the last domain
(GP) will be introduced, thereby making up a trilogy
which gives a general overview of the whole field. In
this third part, an overview will be given of the basic
concepts of GP as defined by Koza. A first
(educational) example of GP is given by solving a
simple symbolic regression of a sinus function.
Finally, a more complex application is presented in
which GP is used to construct the mathematical
equations for an industrial process. To this end, the
case study 'fibre-to-yarn production process' is
introduced. The goal of this study is the automatic
development of mathematical equations for the
prediction of spinnability and (possible) resulting
yarn strength. It is shown that (relatively) simple
equations can be obtained which describe accurately 90%
of the fibre-to-yarn database.
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