Recent analysis of the XCS classifier system have
shown that successful genetic learning strongly depends
on the amount of fitness pressure towards accurate
classifiers. Since the traditionally used proportionate
selection is dependent on fitness scaling and fitness
distribution, the resulting evolutionary fitness
pressure may be neither stable nor sufficiently strong.
Thus, we apply tournament selection to XCS. In
particular, we exhibit the weakness of proportionate
selection and suggest tournament selection as a more
reliable alternative. We show that tournament selection
results in a learning classifier system that is more
parameter independent, noise independent, and more
efficient in exploiting fitness guidance in single-step
problems as well as multistep problems. The evolving
population is more focused on promising subregions of
the problem space and thus finds the desired accurate,
maximally general representation faster and more
reliably.
%0 Journal Article
%1 butz:2005:GPEM
%A Butz, Martin V.
%A Sastry, Kumara
%A Goldberg, David E.
%D 2005
%J Genetic Programming and Evolvable Machines
%K LCS, XCS, algorithms, based classifier genetic genetics learning machine selection, systems, tournament
%N 1
%P 53--77
%R doi:10.1007/s10710-005-7619-9
%T Strong, Stable, and Reliable Fitness Pressure in XCS
due to Tournament Selection
%V 6
%X Recent analysis of the XCS classifier system have
shown that successful genetic learning strongly depends
on the amount of fitness pressure towards accurate
classifiers. Since the traditionally used proportionate
selection is dependent on fitness scaling and fitness
distribution, the resulting evolutionary fitness
pressure may be neither stable nor sufficiently strong.
Thus, we apply tournament selection to XCS. In
particular, we exhibit the weakness of proportionate
selection and suggest tournament selection as a more
reliable alternative. We show that tournament selection
results in a learning classifier system that is more
parameter independent, noise independent, and more
efficient in exploiting fitness guidance in single-step
problems as well as multistep problems. The evolving
population is more focused on promising subregions of
the problem space and thus finds the desired accurate,
maximally general representation faster and more
reliably.
@article{butz:2005:GPEM,
abstract = {Recent analysis of the XCS classifier system have
shown that successful genetic learning strongly depends
on the amount of fitness pressure towards accurate
classifiers. Since the traditionally used proportionate
selection is dependent on fitness scaling and fitness
distribution, the resulting evolutionary fitness
pressure may be neither stable nor sufficiently strong.
Thus, we apply tournament selection to XCS. In
particular, we exhibit the weakness of proportionate
selection and suggest tournament selection as a more
reliable alternative. We show that tournament selection
results in a learning classifier system that is more
parameter independent, noise independent, and more
efficient in exploiting fitness guidance in single-step
problems as well as multistep problems. The evolving
population is more focused on promising subregions of
the problem space and thus finds the desired accurate,
maximally general representation faster and more
reliably.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Butz, Martin V. and Sastry, Kumara and Goldberg, David E.},
biburl = {https://www.bibsonomy.org/bibtex/2d1547517b69441a4dc799ee14aec8e27/brazovayeye},
doi = {doi:10.1007/s10710-005-7619-9},
interhash = {3847c810b622c560a69bc610dbb38b23},
intrahash = {d1547517b69441a4dc799ee14aec8e27},
issn = {1389-2576},
journal = {Genetic Programming and Evolvable Machines},
keywords = {LCS, XCS, algorithms, based classifier genetic genetics learning machine selection, systems, tournament},
month = {March},
number = 1,
pages = {53--77},
timestamp = {2008-06-19T17:37:11.000+0200},
title = {Strong, Stable, and Reliable Fitness Pressure in {XCS}
due to Tournament Selection},
volume = 6,
year = 2005
}