An Evolutionary Strategy for Surrogate-Based Multiobjective Optimization

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F12%3A10126810" target="_blank" >RIV/00216208:11320/12:10126810 - isvavai.cz</a>
Result on the web
<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6256450" target="_blank" >http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6256450</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CEC.2012.6256450" target="_blank" >10.1109/CEC.2012.6256450</a>

Result language
angličtina
Original language name
An Evolutionary Strategy for Surrogate-Based Multiobjective Optimization
Original language description
The paper presents a surrogate-based evolutionary strategy for multiobjective optimization. The evolutionary strategy uses distance based aggregate surrogate models in two ways as a part of memetic search and as way to pre-select individuals in order toavoid evaluation of bad individuals. The model predict the distance of individuals to the currently known Pareto set. The newly proposed algorithm is compared to other algorithms which use similar surrogate models on a set of benchmark functions.
Czech name
—
Czech description
—

Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2012 IEEE Congress on Evolutionary Computation (CEC)
ISBN
978-1-4673-1510-4
ISSN
—
e-ISSN
—
Number of pages
7
Pages from-to
1-7
Publisher name
IEEE Computer Society Press
Place of publication
Brisbane, Austrálie
Event location
Brisbane, Austrálie
Event date
Jun 10, 2012
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000312859302012

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