Genetic Programming

GENETIC PROGRAMMING: AN INTRODUCTION AND SURVEY OF APPLICATIONS
M.J. Willis*, H.G Hiden*, P. Marenbach+, B. McKay* and G.A. Montague*
* Symbolic Optimisation Research Group (SORG)
Dept. of Chemical and Process Engineering
University of Newcastle upon Tyne
NE1 7RU, UK

+ Institute of Control Engineering
Darmstadt University of Technology
Landgraf-Georg-Strasse 4
D-64283 Darmstadt, Germany

{Mark.Willis, H.G.Hiden, Ben.McKay, Gary.Montague}
@ncl.ac.uk
http://lorien.ncl.ac.uk/sorg

[e-mail]

Keywords: genetic programming, survey regression. While conventional regression seeks to optimise the parameters for a pre-specified model structure, with symbolic regression, the model structure and parameters are determined simultaneously. Similarly, the evolution of control algorithms, scheduling programs, structural design and signal processing algorithms can be viewed as structural optimisation problems suitable for
GP.

Abstract
The aim of this paper is to provide an introduction to the rapidly developing field of genetic programming (GP).
Particular emphasis is placed on the application of GP to engineering problem solving. First, the basic methodology is introduced. This is followed by a review of applications in the areas of systems modelling, control, optimisation and scheduling, design and signal processing. The paper concludes by suggesting potential avenues of research.

Cramer (1985) developed one of the first tree structured
GA’s for basic symbolic regression. Another early development was the BEAGLE1 algorithm of Forsyth,
(1986), which generated classification rules using a tree structured GA. However, it was Koza (1992 and 1994) who was largely responsible for the popularisation of GP within the field of computer science. His GP algorithm
(coded in LISP) was applied to a wide range of problems including symbolic regression, control, robotics, games and classification.

Introduction
GP began as an attempt to discover how computers could

References: Alba, E., Cotta, C. and Troyo, J.J., (1996), ‘Type constrained genetic programming for rule based Bettenhausen, K.D. and Marenbach, P., (1995), ‘Selforganising modelling of biotechnological batch and fed-batch fermentations’, Proc Cramer, N.L., (1985), 'A representation for the adaptive generation of simple sequential programs ' Elsey, J., Riepenhausen, J., McKay, B., Barton G.W. and Willis M.J., (1997), ‘Modelling and control of a Forsyth, R., (1986), 'Evolutionary learning strategies ', Forsyth, R Ghanea-Herrock, R. and Fraser, A.P., (1994), ‘Evolution of autonomous robot control architectures’, Signal Processing GP has also been used by Sharman et al.(1995) and Sharman and Esparcia-Alcazar (1996) to evolve the structure and parameters of adaptive digital signal structural optimisation. For instance, the structural annealing algorithm of O’Reilly and Oppacher (1994), 4 Grimes, C.A., (1995), 'Application of genetic techniques