Surrogate Model for Mixed-Variables Evolutionary Optimization Based on GLM and RBF Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F13%3A00389195" target="_blank" >RIV/67985807:_____/13:00389195 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11320/13:10132992
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 Mixed-Variables Evolutionary Optimization Based on GLM and RBF Networks
Original language description
Approximation of costly objective functions by surrogate models is an increasingly popular method in many engineering optimization tasks. Surrogate models can substantially decrease the number of expensive experiments or simulations needed to achieve anoptimal or near-optimal solution. In this paper, a novel surrogate model is presented. Compared to the most of the surrogate models reported in the literature, it has an advantage of explicitly dealing with mixed continuous and discrete variables. The model use radial basis function networks for continuous and clustering and a generalized linear model for the discrete covariates. The applicability of the model is shown on a benchmark problem, and the model?s regression performance is further measured ona dataset from a real-world application.
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
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
SOFSEM 2013. Theory and Practice of Computer Science
ISBN
978-3-642-35842-5
ISSN
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e-ISSN
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Number of pages
10
Pages from-to
481-490
Publisher name
Springer
Place of publication
Berlin
Event location
Špindlerův Mlýn
Event date
Jan 26, 2013
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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