An Evolutionary Strategy for Surrogate-Based Multiobjective Optimization

Kód výsledku v 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>
Výsledek na webu
<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>

Jazyk výsledku
angličtina
Název v původním jazyce
An Evolutionary Strategy for Surrogate-Based Multiobjective Optimization
Popis výsledku v původním jazyce
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.
Název v anglickém jazyce
An Evolutionary Strategy for Surrogate-Based Multiobjective Optimization
Popis výsledku anglicky
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.

Rok uplatnění
2012
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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