Solving real-valued optimisation problems using
cartesian genetic programming
J. Walker, and J. Miller. GECCO '07: Proceedings of the 9th annual conference on
Genetic and evolutionary computation, 2, page 1724--1730. London, ACM Press, (7-11 July 2007)
Abstract
Classical Evolutionary Programming (CEP) and Fast
Evolutionary Programming (FEP) have been applied to
realvalued function optimisation. Both of these
techniques directly evolve the real-values that are the
arguments of the real-valued function. In this paper we
have applied a form of genetic programming called
Cartesian Genetic Programming (CGP) to a number of
real-valued optimisation benchmark problems. The
approach we have taken is to evolve a computer program
that controls a writing-head, which moves along and
interacts with a finite set of symbols that are
interpreted as real numbers, instead of manipulating
the real numbers directly. In other studies, CGP has
already been shown to benefit from a high degree of
neutrality. We hope to exploit this for real-valued
function optimisation problems to avoid being trapped
on local optima. We have also used an extended form of
CGP called Embedded CGP (ECGP) which allows the
acquisition, evolution and re-use of modules. The
effectiveness of CGP and ECGP are compared and
contrasted with CEP and FEP on the benchmark problems.
Results show that the new techniques are very
effective.
GECCO '07: Proceedings of the 9th annual conference on
Genetic and evolutionary computation
year
2007
month
7-11 July
pages
1724--1730
publisher
ACM Press
volume
2
organisation
ACM SIGEVO (formerly ISGEC)
publisher_address
New York, NY, USA
isbn13
978-1-59593-697-4
notes
GECCO-2007 A joint meeting of the sixteenth
international conference on genetic algorithms
(ICGA-2007) and the twelfth annual genetic programming
conference (GP-2007).
ACM Order Number 910071
%0 Conference Paper
%1 1277295
%A Walker, James Alfred
%A Miller, Julian Francis
%B GECCO '07: Proceedings of the 9th annual conference on
Genetic and evolutionary computation
%C London
%D 2007
%E Thierens, Dirk
%E Beyer, Hans-Georg
%E Bongard, Josh
%E Branke, Jurgen
%E Clark, John Andrew
%E Cliff, Dave
%E Congdon, Clare Bates
%E Deb, Kalyanmoy
%E Doerr, Benjamin
%E Kovacs, Tim
%E Kumar, Sanjeev
%E Miller, Julian F.
%E Moore, Jason
%E Neumann, Frank
%E Pelikan, Martin
%E Poli, Riccardo
%E Sastry, Kumara
%E Stanley, Kenneth Owen
%E Stutzle, Thomas
%E Watson, Richard A
%E Wegener, Ingo
%I ACM Press
%K Cartesian Genetic Programming, algorithms, embedded evolutionary function genetic modules, optimisation programming, real valued
%P 1724--1730
%T Solving real-valued optimisation problems using
cartesian genetic programming
%U http://doi.acm.org/10.1145/1276958.1277295
%V 2
%X Classical Evolutionary Programming (CEP) and Fast
Evolutionary Programming (FEP) have been applied to
realvalued function optimisation. Both of these
techniques directly evolve the real-values that are the
arguments of the real-valued function. In this paper we
have applied a form of genetic programming called
Cartesian Genetic Programming (CGP) to a number of
real-valued optimisation benchmark problems. The
approach we have taken is to evolve a computer program
that controls a writing-head, which moves along and
interacts with a finite set of symbols that are
interpreted as real numbers, instead of manipulating
the real numbers directly. In other studies, CGP has
already been shown to benefit from a high degree of
neutrality. We hope to exploit this for real-valued
function optimisation problems to avoid being trapped
on local optima. We have also used an extended form of
CGP called Embedded CGP (ECGP) which allows the
acquisition, evolution and re-use of modules. The
effectiveness of CGP and ECGP are compared and
contrasted with CEP and FEP on the benchmark problems.
Results show that the new techniques are very
effective.
@inproceedings{1277295,
abstract = {Classical Evolutionary Programming (CEP) and Fast
Evolutionary Programming (FEP) have been applied to
realvalued function optimisation. Both of these
techniques directly evolve the real-values that are the
arguments of the real-valued function. In this paper we
have applied a form of genetic programming called
Cartesian Genetic Programming (CGP) to a number of
real-valued optimisation benchmark problems. The
approach we have taken is to evolve a computer program
that controls a writing-head, which moves along and
interacts with a finite set of symbols that are
interpreted as real numbers, instead of manipulating
the real numbers directly. In other studies, CGP has
already been shown to benefit from a high degree of
neutrality. We hope to exploit this for real-valued
function optimisation problems to avoid being trapped
on local optima. We have also used an extended form of
CGP called Embedded CGP (ECGP) which allows the
acquisition, evolution and re-use of modules. The
effectiveness of CGP and ECGP are compared and
contrasted with CEP and FEP on the benchmark problems.
Results show that the new techniques are very
effective.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {London},
author = {Walker, James Alfred and Miller, Julian Francis},
biburl = {https://www.bibsonomy.org/bibtex/2a70aad8aaafba241778311f76e0e4482/brazovayeye},
booktitle = {GECCO '07: Proceedings of the 9th annual conference on
Genetic and evolutionary computation},
editor = {Thierens, Dirk and Beyer, Hans-Georg and Bongard, Josh and Branke, Jurgen and Clark, John Andrew and Cliff, Dave and Congdon, Clare Bates and Deb, Kalyanmoy and Doerr, Benjamin and Kovacs, Tim and Kumar, Sanjeev and Miller, Julian F. and Moore, Jason and Neumann, Frank and Pelikan, Martin and Poli, Riccardo and Sastry, Kumara and Stanley, Kenneth Owen and Stutzle, Thomas and Watson, Richard A and Wegener, Ingo},
interhash = {9bf8d05043f8a4d7407c21ab23984c07},
intrahash = {a70aad8aaafba241778311f76e0e4482},
isbn13 = {978-1-59593-697-4},
keywords = {Cartesian Genetic Programming, algorithms, embedded evolutionary function genetic modules, optimisation programming, real valued},
month = {7-11 July},
notes = {GECCO-2007 A joint meeting of the sixteenth
international conference on genetic algorithms
(ICGA-2007) and the twelfth annual genetic programming
conference (GP-2007).
ACM Order Number 910071},
organisation = {ACM SIGEVO (formerly ISGEC)},
pages = {1724--1730},
publisher = {ACM Press},
publisher_address = {New York, NY, USA},
timestamp = {2008-06-19T17:53:48.000+0200},
title = {Solving real-valued optimisation problems using
cartesian genetic programming},
url = {http://doi.acm.org/10.1145/1276958.1277295},
volume = 2,
year = 2007
}