Enhancing the performance of a GA through visualisation.

Hart, Emma and Ross, Peter (2000) Enhancing the performance of a GA through visualisation. In: Proceedings of GECCO-2000. Morgan Kaufmann, Las Vegas. ISBN 1-55860-708-0

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This article describes a new tool for visualising
genetic algorithms, (GAs) which is designed
in order to allow the implicit mechanisms
of the GA | i.e. crossover and mutation
| to be thoroughly analysed. This
allows the user to determine whether these
mechanisms are essential to a GAs performance,
and if so, to provide a principled
means of setting the parameters associated
with them, based on a sound understanding
of their e�ects. The use of the tool is illustrated
by applying to the analysis of a jobshop
scheduling problem, in order to choose
e�ective operators, and to determine appropriate
settings for them. We show that by
analysing two crossover operators and a mutation
operator, we can re�ne the choice and
settings of these parameters in order to improve
the performance of the GA on the particular
problem chosen. When the new operators
are applied to a wider range of problems
of the same type, a similar improvement
in performance is observed.

Item Type: Book Section
ISBN: 1-55860-708-0
Uncontrolled Keywords: genetic algorithms; visualisation; crossover; mutation; parameters; jobshop scheduling; performance improvement;
University Divisions/Research Centres: Faculty of Engineering, Computing and Creative Industries > School of Computing
Dewey Decimal Subjects: 000 Computer science, information & general works > 000 Computer science, knowledge & systems > 006 Special Computer Methods > 006.3 Artificial intelligence
Library of Congress Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Item ID: 3171
Depositing User: Computing Research
Date Deposited: 06 Sep 2010 11:01
Last Modified: 17 Mar 2014 12:02

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