Boosted Surrogate Models in Evolutionary Optimization
Result description
The paper deals with 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, a property that is particularly attractive in evolutionary optimization. In the paper, an extension of surrogate modelling with regression boosting is proposed.
Keywords
evolutionary optimizationgenetic algorithmssurrogate modellingregression modelsboosting
The result's identifiers
Result code in IS VaVaI
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 Surrogate Models in Evolutionary Optimization
Original language description
The paper deals with 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, a property that is particularly attractive in evolutionary optimization. In the paper, an extension of surrogate modelling with regression boosting is proposed.
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
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
Information Technologies - Applications and Theory
ISBN
978-80-970179-2-7
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
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Publisher name
Pont
Place of publication
Seňa
Event location
Kráľova studňa
Event date
Sep 25, 2009
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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Basic information
Result type
D - Article in proceedings
CEP
IN - Informatics
Year of implementation
2009