CGP visits the Santa Fe trail: effects of heuristics
on GP
C. Janikow, and C. Mann. GECCO 2005: Proceedings of the 2005 conference on
Genetic and evolutionary computation, 2, page 1697--1704. Washington DC, USA, ACM Press, (25-29 June 2005)
Abstract
GP uses trees to represent chromosomes. The user
defines the representation space by defining the set of
functions and terminals to label the nodes in the
trees, and GP searches the space. Previous research and
experimentation show that the choice of the
function/terminal set, choice of the initial
population, and some other explicit and implicit
"design" factors have great influence on both the
quality and the speed of the evolution. Such heuristics
are valuable simply because they improve GP's
performance, or because they enforce some desired
properties on the solutions. In this paper, we evaluate
the effect of heuristics on GP solving the Santa Fe
trail. We concentrate on improving the solution
quality, but we also look at efficiency. Various
heuristics are tried and mixed by hand, while evaluated
with the help of the CGP system. Results show that some
heuristics result in very substantial performance
improvements, that complex heuristics are usually not
decomposable, and that the heuristics generalize to
apply to other similar problems, but the applicability
reduces with the complexity of the heuristics and the
dissimilarity of the new problem to the old one. We
also compare such user-mixed heuristics with those
generated by the ACGP system which automatically
extracts heuristics improving GP performance.
GECCO 2005: Proceedings of the 2005 conference on
Genetic and evolutionary computation
year
2005
month
25-29 June
pages
1697--1704
publisher
ACM Press
volume
2
organisation
ACM SIGEVO (formerly ISGEC)
publisher_address
New York, NY, 10286-1405, USA
isbn
1-59593-010-8
notes
GECCO-2005 A joint meeting of the fourteenth
international conference on genetic algorithms
(ICGA-2005) and the tenth annual genetic programming
conference (GP-2005).
ACM Order Number 910052
%0 Conference Paper
%1 1068293
%A Janikow, Cezary Z.
%A Mann, Christopher J.
%B GECCO 2005: Proceedings of the 2005 conference on
Genetic and evolutionary computation
%C Washington DC, USA
%D 2005
%E Beyer, Hans-Georg
%E O'Reilly, Una-May
%E Arnold, Dirk V.
%E Banzhaf, Wolfgang
%E Blum, Christian
%E Bonabeau, Eric W.
%E Cantu-Paz, Erick
%E Dasgupta, Dipankar
%E Deb, Kalyanmoy
%E Foster, James A.
%E de
Jong, Edwin D.
%E Lipson, Hod
%E Llora, Xavier
%E Mancoridis, Spiros
%E Pelikan, Martin
%E Raidl, Guenther R.
%E Soule, Terence
%E Tyrrell, Andy M.
%E Watson, Jean-Paul
%E Zitzler, Eckart
%I ACM Press
%K algorithms, computation, design, evolutionary experimentation, genetic heuristics programming,
%P 1697--1704
%T CGP visits the Santa Fe trail: effects of heuristics
on GP
%U http://doi.acm.org/10.1145/1068009.1068293
%V 2
%X GP uses trees to represent chromosomes. The user
defines the representation space by defining the set of
functions and terminals to label the nodes in the
trees, and GP searches the space. Previous research and
experimentation show that the choice of the
function/terminal set, choice of the initial
population, and some other explicit and implicit
"design" factors have great influence on both the
quality and the speed of the evolution. Such heuristics
are valuable simply because they improve GP's
performance, or because they enforce some desired
properties on the solutions. In this paper, we evaluate
the effect of heuristics on GP solving the Santa Fe
trail. We concentrate on improving the solution
quality, but we also look at efficiency. Various
heuristics are tried and mixed by hand, while evaluated
with the help of the CGP system. Results show that some
heuristics result in very substantial performance
improvements, that complex heuristics are usually not
decomposable, and that the heuristics generalize to
apply to other similar problems, but the applicability
reduces with the complexity of the heuristics and the
dissimilarity of the new problem to the old one. We
also compare such user-mixed heuristics with those
generated by the ACGP system which automatically
extracts heuristics improving GP performance.
%@ 1-59593-010-8
@inproceedings{1068293,
abstract = {GP uses trees to represent chromosomes. The user
defines the representation space by defining the set of
functions and terminals to label the nodes in the
trees, and GP searches the space. Previous research and
experimentation show that the choice of the
function/terminal set, choice of the initial
population, and some other explicit and implicit
{"}design{"} factors have great influence on both the
quality and the speed of the evolution. Such heuristics
are valuable simply because they improve GP's
performance, or because they enforce some desired
properties on the solutions. In this paper, we evaluate
the effect of heuristics on GP solving the Santa Fe
trail. We concentrate on improving the solution
quality, but we also look at efficiency. Various
heuristics are tried and mixed by hand, while evaluated
with the help of the CGP system. Results show that some
heuristics result in very substantial performance
improvements, that complex heuristics are usually not
decomposable, and that the heuristics generalize to
apply to other similar problems, but the applicability
reduces with the complexity of the heuristics and the
dissimilarity of the new problem to the old one. We
also compare such user-mixed heuristics with those
generated by the ACGP system which automatically
extracts heuristics improving GP performance.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Washington DC, USA},
author = {Janikow, Cezary Z. and Mann, Christopher J.},
biburl = {https://www.bibsonomy.org/bibtex/27aa6f0440a0566dc1d738f5714be565e/brazovayeye},
booktitle = {{GECCO 2005}: Proceedings of the 2005 conference on
Genetic and evolutionary computation},
editor = {Beyer, Hans-Georg and O'Reilly, Una-May and Arnold, Dirk V. and Banzhaf, Wolfgang and Blum, Christian and Bonabeau, Eric W. and Cantu-Paz, Erick and Dasgupta, Dipankar and Deb, Kalyanmoy and Foster, James A. and {de
Jong}, Edwin D. and Lipson, Hod and Llora, Xavier and Mancoridis, Spiros and Pelikan, Martin and Raidl, Guenther R. and Soule, Terence and Tyrrell, Andy M. and Watson, Jean-Paul and Zitzler, Eckart},
interhash = {1b272e541f9bc6f12c633cbdde1926ec},
intrahash = {7aa6f0440a0566dc1d738f5714be565e},
isbn = {1-59593-010-8},
keywords = {algorithms, computation, design, evolutionary experimentation, genetic heuristics programming,},
month = {25-29 June},
notes = {GECCO-2005 A joint meeting of the fourteenth
international conference on genetic algorithms
(ICGA-2005) and the tenth annual genetic programming
conference (GP-2005).
ACM Order Number 910052},
organisation = {ACM SIGEVO (formerly ISGEC)},
pages = {1697--1704},
publisher = {ACM Press},
publisher_address = {New York, NY, 10286-1405, USA},
timestamp = {2008-06-19T17:42:22.000+0200},
title = {{CGP} visits the Santa Fe trail: effects of heuristics
on {GP}},
url = {http://doi.acm.org/10.1145/1068009.1068293},
volume = 2,
year = 2005
}