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Author, Editor

Author(s):

Doerr, Carola
De Rainville, François-Michel

dblp
dblp

Not MPG Author(s):

De Rainville, François-Michel

Editor(s):





BibTeX cite key*:

DoerrDR2013

Title, Booktitle

Title*:

Constructing Low Star Discrepancy Point Sets with Genetic Algorithms

Booktitle*:

Proc. of Genetic and Evolutionary Computation Conference (GECCO 2013)

Event, URLs

URL of the conference:


URL for downloading the paper:


Event Address*:

Amsterdam, Netherlands

Language:

English

Event Date*
(no longer used):


Organization:


Event Start Date:

14 January 2014

Event End Date:

14 January 2014

Publisher

Name*:

ACM

URL:


Address*:

New York, USA

Type:


Vol, No, Year, pp.

Series:


Volume:


Number:


Month:


Pages:

789-796

Year*:

2013

VG Wort Pages:


ISBN/ISSN:


Sequence Number:


DOI:




Note, Abstract, ©


(LaTeX) Abstract:

The recently active research area of black-box complexity revealed that for many optimization problems the best possible black-box optimization algorithm is significantly faster than all known evolutionary approaches. While it is not to be expected that a general-purpose heuristic competes with a problem-tailored algorithm, it still makes sense to look for the reasons for this discrepancy.

In this work, we exhibit one possible reason---most optimal black-box algorithms profit also from solutions that are inferior to the previous-best one, whereas evolutionary approaches guided by the ``survival of the fittest'' paradigm often ignore such solutions. Trying to overcome this shortcoming, we design a simple genetic algorithm that first creates $\lambda$ offspring from a single parent by mutation with a mutation probability that is $k$ times larger than the usual one. From the best of these offspring (which often is worse than the parent) and the parent itself, we generate further offspring via a uniform crossover operator that takes bits from the winner offspring with probability $1/k$ only.

A rigorous runtime analysis proves that our new algorithm for suitable parameter choices on the \onemax test function class is asymptotically faster (in terms of the number of fitness evaluations) than what has been shown for $(\mu \stackrel{+}{,} \lambda)$ EAs. This is the first time that crossover is shown to give an advantage for the $\onemax$ class that is
larger than a constant factor. Using a fitness-dependent choice of $k$ and~$\lambda$, the optimization time can be reduced further to linear in~$n$.

Our experimental analysis on several test function classes shows advantages already for small problem sizes and broad parameter ranges. Also, a simple self-adaptive choice of these parameters gives surprisingly good results.



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Access Level:

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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{DoerrDR2013,
AUTHOR = {Doerr, Carola and De Rainville, François-Michel},
TITLE = {Constructing Low Star Discrepancy Point Sets with Genetic Algorithms},
BOOKTITLE = {Proc. of Genetic and Evolutionary Computation Conference (GECCO 2013)},
PUBLISHER = {ACM},
YEAR = {2013},
PAGES = {789--796},
ADDRESS = {Amsterdam, Netherlands},
}


Entry last modified by Carola Winzen, 02/17/2014
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Editor(s)
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Created
01/14/2014 04:02:07 PM
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Editor
Carola Winzen



Edit Date
01/14/2014 04:02:07 PM