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Proceedings Article, Paper
@InProceedings
Beitrag in Tagungsband, Workshop

Author, Editor
Author(s):
Happ, Edda
Johannsen, Daniel
Klein, Christian
Neumann, Frank
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Editor(s):
Ryan, Conor
Keijzer, Maarten
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Not MPII Editor(s):
Ryan, Conor
Keijzer, Maarten
BibTeX cite key*:
HappJohannsenKleinNeumann2008a
Title, Booktitle
Title*:
Rigorous Analyses of Fitness-Proportional Selection for Optimizing Linear Functions
HappJohannsenKleinNeumann_2008_RigorousAnalysesOfFitness-ProportionalSelectionForOptimizingLinearFunctions.pdf (229.06 KB)
Booktitle*:
Genetic and Evolutionary Computation Conference 2008
Event, URLs
Conference URL::
http://www.sigevo.org/gecco-2008/index.html
Downloading URL:
http://doi.acm.org/10.1145/1389095.1389277
Event Address*:
Atlanta, USA
Language:
English
Event Date*
(no longer used):
Organization:
Association for Computing Machinery (ACM)
Event Start Date:
12 July 2008
Event End Date:
16 July 2008
Publisher
Name*:
ACM
URL:
http://www.acm.org
Address*:
New York, USA
Type:
Vol, No, Year, pp.
Series:
Volume:
Number:
Month:
Pages:
953-960
Year*:
2008
VG Wort Pages:
8
ISBN/ISSN:
978-1-60558-130-9
Sequence Number:
DOI:
http://doi.acm.org/10.1145/1389095.1389277
Note, Abstract, ©
(LaTeX) Abstract:
Rigorous runtime analyses of evolutionary algorithms (EAs) mainly investigate algorithms that use elitist selection methods. Two algorithms commonly studied are Randomized Local Search (RLS) and the (1+1)~EA and it is well known that both optimize any linear pseudo-Boolean function on $n$ bits within an expected number of $\ensuremath{{O}}(n \log n)$ fitness evaluations. In this paper, we analyze variants of these algorithms that use fitness proportional selection.

A well-known method in analyzing the local changes in the solutions of RLS is a reduction to the gambler's ruin problem. We extend this method in order to analyze the global changes imposed by the (1+1)~EA. By applying this new technique we show that with high probability using fitness proportional selection
leads to an exponential optimization time for any linear pseudo-Boolean function with non-zero weights. Even worse, all solutions of the algorithms during an exponential number of fitness evaluations differ with high probability in linearly many bits from the optimal solution.

Our theoretical studies are complemented by experimental investigations which confirm the asymptotic results on realistic input sizes.
URL for the Abstract:
http://doi.acm.org/10.1145/1389095.1389277
Keywords:
Running Time Analysis, Selection, Theory
Download
Access Level:
Public

Correlation
MPG Unit:
Max-Planck-Institut für Informatik
MPG Subunit:
Algorithms and Complexity Group
Audience:
experts only
Appearance:
MPII WWW Server, MPII FTP Server, MPG publications list, university publications list, working group publication list, Fachbeirat, VG Wort



BibTeX Entry:

@INPROCEEDINGS{HappJohannsenKleinNeumann2008a,
AUTHOR = {Happ, Edda and Johannsen, Daniel and Klein, Christian and Neumann, Frank},
EDITOR = {Ryan, Conor and Keijzer, Maarten},
TITLE = {Rigorous Analyses of Fitness-Proportional Selection for Optimizing Linear Functions},
BOOKTITLE = {Genetic and Evolutionary Computation Conference 2008},
PUBLISHER = {ACM},
YEAR = {2008},
ORGANIZATION = {Association for Computing Machinery (ACM)},
PAGES = {953--960},
ADDRESS = {Atlanta, USA},
ISBN = {978-1-60558-130-9},
DOI = {http://doi.acm.org/10.1145/1389095.1389277},
}


Entry last modified by Daniel Johannsen, 03/03/2009
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Editor(s)
Edda Happ
Created
01/19/2009 11:33:31 AM
Revisions
2.
1.
0.

Editor(s)
Daniel Johannsen
Edda Happ
Edda Happ

Edit Dates
02/15/2009 08:18:06 PM
01/19/2009 12:58:46 PM
01/19/2009 11:33:31 AM


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