Boosted Neural Networks in Evolutionary Computation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F09%3A00333959" target="_blank" >RIV/67985807:_____/09:00333959 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Boosted Neural Networks in Evolutionary Computation
Original language description
The paper deals with a neural-network-based version of surrogate modelling, a modern approach to the optimization of empirical objective functions. The approach leads to a substantial decrease of time and costs of evaluation of the objective function, aproperty that is particularly attractive in evolutionary optimization. In the paper, an extension of surrogate modelling with regression boosting is proposed, which increases the accuracy of surrogate models, thus also the agreement between results obtained with the model and those obtained with the original objective function. The extension is illustrated on a case study in materials science. Presented case study results clearly confirm the usefulness of boosting for neural-network-based surrogate models.
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
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2009
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Neural Information Processing
ISBN
978-3-642-10682-8
ISSN
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e-ISSN
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Number of pages
10
Pages from-to
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Publisher name
Springer
Place of publication
Berlin
Event location
Bangkok
Event date
Dec 1, 2009
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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