Hybrid Adaptive Differential Evolution in Partitional Clustering
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F11%3AA12011VT" target="_blank" >RIV/61988987:17610/11:A12011VT - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Hybrid Adaptive Differential Evolution in Partitional Clustering
Original language description
The problem of optimal partitioning by minimizing pooled-within-variance of groups is addressed. Three state-of-the-art adaptive differential evolution algorithms are compared on four real-world data sets. A~novel hybrid differential evolution algorithm,including k-means algorithm for local search is proposed. The experimental comparison is done with either the plain adaptive differential evolution variants or the hybrid algorithms. Experimental results showed that hybrid algorithms are substantially better preforming when compared with plain differential evolution variants. Among hybrid variants, the competitive differential evolution appeared to be the most efficient.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
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Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2011
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
MENDEL 2011 17th International Conference on Soft Computing
ISBN
978-80-214-4302-0
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
1-8
Publisher name
University of Technology
Place of publication
Brno
Event location
Brno
Event date
Jun 15, 2011
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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