An empirical study of the efficiency of learning
boolean functions using a Cartesian Genetic Programming
approach
J. Miller. Proceedings of the Genetic and Evolutionary
Computation Conference, 2, page 1135--1142. Orlando, Florida, USA, Morgan Kaufmann, (13-17 July 1999)
Abstract
A new form of Genetic Programming (GP) called
Cartesian Genetic Programming (CGP) is proposed in
which programs are represented by linear integer
chromosomes in the form of connections and
functionalities of a rectangular array of primitive
functions. The effectiveness of this approach is
investigated for boolean even-parity functions (3,4,5),
and the 2-bit multiplier. The minimum number of
evaluations required to give a 0.99 probability of
evolving a target function is used to measure the
efficiency of the new approach. It is found that
extremely low populations are most effective. A simple
probabilistic hillclimber (PH) is devised which proves
to be even more effective. For these boolean functions
either method appears to be much more efficient than
the GP and Evolutionary Programming (EP) methods
reported. The efficacy of the PH suggests that boolean
function learning may not be an appropriate problem for
testing the effectiveness of GP and EP.
Proceedings of the Genetic and Evolutionary
Computation Conference
year
1999
month
13-17 July
pages
1135--1142
publisher
Morgan Kaufmann
volume
2
publisher_address
San Francisco, CA 94104, USA
isbn
1-55860-611-4
notes
GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)
%0 Conference Paper
%1 miller:1999:ACGP
%A Miller, Julian F.
%B Proceedings of the Genetic and Evolutionary
Computation Conference
%C Orlando, Florida, USA
%D 1999
%E Banzhaf, Wolfgang
%E Daida, Jason
%E Eiben, Agoston E.
%E Garzon, Max H.
%E Honavar, Vasant
%E Jakiela, Mark
%E Smith, Robert E.
%I Morgan Kaufmann
%K algorithms, and evolvable genetic hardware programming
%P 1135--1142
%T An empirical study of the efficiency of learning
boolean functions using a Cartesian Genetic Programming
approach
%U http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-411.ps
%V 2
%X A new form of Genetic Programming (GP) called
Cartesian Genetic Programming (CGP) is proposed in
which programs are represented by linear integer
chromosomes in the form of connections and
functionalities of a rectangular array of primitive
functions. The effectiveness of this approach is
investigated for boolean even-parity functions (3,4,5),
and the 2-bit multiplier. The minimum number of
evaluations required to give a 0.99 probability of
evolving a target function is used to measure the
efficiency of the new approach. It is found that
extremely low populations are most effective. A simple
probabilistic hillclimber (PH) is devised which proves
to be even more effective. For these boolean functions
either method appears to be much more efficient than
the GP and Evolutionary Programming (EP) methods
reported. The efficacy of the PH suggests that boolean
function learning may not be an appropriate problem for
testing the effectiveness of GP and EP.
%@ 1-55860-611-4
@inproceedings{miller:1999:ACGP,
abstract = {A new form of Genetic Programming (GP) called
Cartesian Genetic Programming (CGP) is proposed in
which programs are represented by linear integer
chromosomes in the form of connections and
functionalities of a rectangular array of primitive
functions. The effectiveness of this approach is
investigated for boolean even-parity functions (3,4,5),
and the 2-bit multiplier. The minimum number of
evaluations required to give a 0.99 probability of
evolving a target function is used to measure the
efficiency of the new approach. It is found that
extremely low populations are most effective. A simple
probabilistic hillclimber (PH) is devised which proves
to be even more effective. For these boolean functions
either method appears to be much more efficient than
the GP and Evolutionary Programming (EP) methods
reported. The efficacy of the PH suggests that boolean
function learning may not be an appropriate problem for
testing the effectiveness of GP and EP.},
added-at = {2008-06-19T17:46:40.000+0200},
address = {Orlando, Florida, USA},
author = {Miller, Julian F.},
biburl = {https://www.bibsonomy.org/bibtex/26f47fbed01bbf2b00d85011699a5549e/brazovayeye},
booktitle = {Proceedings of the Genetic and Evolutionary
Computation Conference},
editor = {Banzhaf, Wolfgang and Daida, Jason and Eiben, Agoston E. and Garzon, Max H. and Honavar, Vasant and Jakiela, Mark and Smith, Robert E.},
interhash = {0f4032643fef94d8f47467097dbabc26},
intrahash = {6f47fbed01bbf2b00d85011699a5549e},
isbn = {1-55860-611-4},
keywords = {algorithms, and evolvable genetic hardware programming},
month = {13-17 July},
notes = {GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)},
pages = {1135--1142},
publisher = {Morgan Kaufmann},
publisher_address = {San Francisco, CA 94104, USA},
timestamp = {2008-06-19T17:47:13.000+0200},
title = {An empirical study of the efficiency of learning
boolean functions using a Cartesian Genetic Programming
approach},
url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-411.ps},
volume = 2,
year = 1999
}