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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