Genetic programming: principles and applications

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). In this paper. 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. (C) 2002 Elsevier Science Ltd. All rights reserved.