Genetic Programming

Topics: Genetic algorithm, Genetic algorithms, Control theory Pages: 21 (3553 words) Published: April 3, 2013
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

+ 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}

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

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.

GP began as an attempt to discover how computers could
learn to solve problems without being explicitly
programmed to do so. The GP technique is an
evolutionary algorithm that bears a strong resemblance to
genetic algorithm's (GA's). The primary differences
between GA's and GP can be summarised as follows;

Since this initial work, interest in the field has grown,
with the first international conference on GP held at
Stanford University in 1996 (GP'96). While still
dominated by computer scientists, engineering
applications have begun to appear. Therefore, the
objective of this paper is to discuss these recent
engineering applications and provide an entry point to
this rapidly expanding field.

GP typically codes solutions as tree structured,
variable length chromosomes, while GA’s generally
make use of chromosomes of fixed length and
GP typically incorporates a domain specific syntax
arrangements of information on the chromosome.
For GA’s, the chromosomes are typically syntax free.
GP makes use of genetic operators that preserve the
syntax of its tree-structured chromosomes during
GP solutions are often coded in a manner that allows
the chromosomes to be executed directly using an
appropriate interpreter. GA’s are rarely coded in a
directly executable form.

The paper is organised as follows. First, GP is introduced.
Next, a survey of engineering applications within the GP
field is provided. Finally, the paper concludes with the
authors' perspective on future research directions.

The use of this flexible coding system allows the
algorithm to perform structural optimisation. This can be
useful for the solution of many engineering problems. For
instance, GP may be used to perform symbolic


BEAGLE - Biological Evolutionary
Generating Logical Expressions


The algorithm control parameters: This includes
the population...

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),
Grimes, C.A., (1995), 'Application of genetic techniques
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