Surrogate Model for Continuous and Discrete Genetic Optimization Based on RBF Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F10%3A00347773" target="_blank" >RIV/67985807:_____/10:00347773 - 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
Surrogate Model for Continuous and Discrete Genetic Optimization Based on RBF Networks
Original language description
Surrogate modelling has become a successful method improving the optimization of costly objective functions. It brings less accurate, but much faster means of evaluating candidate solutions. This paper describes a model based on radial basis function networks which takes into account both continuous and discrete variables. It shows the applicability of our surrogate model to the optimization of empirical objective functions for which mixing of discrete and continuous dimensions is typical. Results of testing with a genetic algorithm confirm considerably faster convergence in terms of the number of the original empirical fitness evaluations.
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/GD201%2F09%2FH057" target="_blank" >GD201/09/H057: Res Informatica</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
Intelligent Data Engineering and Automated Learning - IDEAL 2010
ISBN
978-3-642-15380-8
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
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Publisher name
Springer-Verlag
Place of publication
Berlin
Event location
Paisley
Event date
Sep 1, 2010
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000284820400031