Model-assisted evolutionary optimization with fixed evaluation batch size

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F12%3A00200871" target="_blank" >RIV/68407700:21340/12:00200871 - isvavai.cz</a>
Result on the web
<a href="http://km.fjfi.cvut.cz/ddny/" target="_blank" >http://km.fjfi.cvut.cz/ddny/</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Model-assisted evolutionary optimization with fixed evaluation batch size
Original language description
Some black-box optimization problems involve long-running simulations or expensive experiments as the goal function. To enable use of evolutionary algorithms, surrogate models are used to reduce the number of function evaluations. In adaptive model building strategies, some individuals are selected for true function evaluation in order to improve the model. When the experiment or simulation requires a fixed size batch of solutions to evaluate, traditional selection strategies either cannot be used or couple the batch size with the EA generation size. We propose a queue based method for model-assisted optimization using active learning of a kriging model, where individuals are selected based on the model predictor error estimate. The method was tested on standard benchmark problems and the effects of batch size was studied. Results indicate that the proposed method significantly reduces the number of true fitness evaluation compared to a traditional EA.
Czech name
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Czech description
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Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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