Surrogate model selection for evolutionary multiobjective optimization

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F13%3A00423839" target="_blank" >RIV/67985807:_____/13:00423839 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/CEC.2013.6557786" target="_blank" >http://dx.doi.org/10.1109/CEC.2013.6557786</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CEC.2013.6557786" target="_blank" >10.1109/CEC.2013.6557786</a>

Result language
angličtina
Original language name
Surrogate model selection for evolutionary multiobjective optimization
Original language description
In surrogate evolutionary algorithms, usually the type of surrogate model is chosen beforehand, and it is never changed during the run of the evolution. Moreover, the reasoning why a particular type of model was chosen is often missing. In this paper, wepresent a framework which in each generation selects the most suitable surrogate from a set of models based on some pre-defined criteria. The results based on different types of model selectors are compared, and the dynamics of the evolution together with the change of the selected model type during the run of the evolutionary algorithm are discussed.
Czech name
—
Czech description
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Project
<a href="/en/project/GAP202%2F11%2F1368" target="_blank" >GAP202/11/1368: Learning of functional relationships from high-dimensional data</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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