Neural Networks as Surrogate Models for Measurements in Optimization Algorithms
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F10%3A00345993" target="_blank" >RIV/67985807:_____/10:00345993 - 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
Neural Networks as Surrogate Models for Measurements in Optimization Algorithms
Original language description
The paper deals with surrogate modelling, a modern approach to the optimization of objective functions evaluated via measurements. The approach leads to a substantial decrease of time and costs of evaluation of the objective function, a property that isparticularly attractive in evolutionary optimization. The paper recalls common strategies for using surrogate models in evolutionary optimization, and proposes two extensions to those strategies - extension to boosted surrogate models and extension to using a set of models. These are currently being implemented, in connection with surrogate modelling based on feed-forward neural networks, in a software tool for problem-tailored evolutionary optimization of catalytic materials. The paper presents resultsof experimentally testing already implemented parts and comparing boosted surrogate models with models without boosting, which clearly confirms the usefulness of both proposed extensions.
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
<a href="/en/project/GA201%2F08%2F0802" target="_blank" >GA201/08/0802: Applications of Methods of Knowledge Engineering in Data Mining</a><br>
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Analytical and Stochastic Modeling Techniques and Applications
ISBN
978-3-642-13567-5
ISSN
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e-ISSN
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Number of pages
16
Pages from-to
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Publisher name
Springer
Place of publication
Berlin
Event location
Cardiff
Event date
Jun 14, 2010
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000279619100025