Using Kernel Principal Component Analysis in Evolutionary Algorithms as an Efficient Multi-Parent Crossover Operator
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F04%3A03099362" target="_blank" >RIV/68407700:21230/04:03099362 - 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
Using Kernel Principal Component Analysis in Evolutionary Algorithms as an Efficient Multi-Parent Crossover Operator
Original language description
The article describes a new method for parental crossover based on KPCA. From the parent population non-linear features are extracted, the population is transformed to a new space; new individuals are randomly sampled in this space and are transformed back to original space. This approach seems to be verz efficient for small groups of highly dependant variables.
Czech name
Není k dispozici
Czech description
Není k dispozici
Classification
Type
D - Article in proceedings
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GA102%2F02%2F0132" target="_blank" >GA102/02/0132: Use of genetic principles in evolutionary algorithms</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2004
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
IEEE 4th International Conference on Intelligent Systems Design and Application
ISBN
963-7154-29-9
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
25-30
Publisher name
IEEE
Place of publication
Piscataway
Event location
Budapest
Event date
Aug 26, 2004
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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