ASM-MOMA: Multiobjective Memetic Algorithm with Aggregate Surrogate Model
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F11%3A10105975" target="_blank" >RIV/00216208:11320/11:10105975 - isvavai.cz</a>
Alternative codes found
RIV/67985807:_____/11:00375286
Result on the web
<a href="http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5949753" target="_blank" >http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5949753</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CEC.2011.5949753" target="_blank" >10.1109/CEC.2011.5949753</a>
Alternative languages
Result language
angličtina
Original language name
ASM-MOMA: Multiobjective Memetic Algorithm with Aggregate Surrogate Model
Original language description
Abstract-Evolutionary algorithms generally require a large number of objective function evaluations which can be costly in practice. These evaluations can be replaced by evaluations of a cheaper meta-model (surrogate model) of the objective functions. Inthis paper we present a novel distance based aggregate surrogate model for multiobjective optimization and describe a memetic multiobjective algorithm based on this model. Various variants of the models are tested and discussed and the algorithm is compared to standard multiobjective evolutionary algorithms. We show that our algorithm greatly reduces the number of required objective function evaluations.
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
<a href="/en/project/GAP202%2F11%2F1368" target="_blank" >GAP202/11/1368: Learning of functional relationships from high-dimensional data</a><br>
Continuities
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
Proceedings of 2011 IEEE Congress on Evolutionary Computation (CEC)
ISBN
978-1-4244-7834-7
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
1202-1208
Publisher name
IEEE Computer Society
Place of publication
New Orleans, USA
Event location
New Orleans, USA
Event date
Jun 5, 2011
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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