English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/159898
Share/Impact:
Statistics
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:

Title

A comparison of active set method and genetic algorithm approaches for learning weighting vectors in some aggregation operators

AuthorsNettleton, David; Torra, Vicenç
KeywordsActive set method
Aggregation operators
Genetic algorithms
Issue Date2001
PublisherJohn Wiley & Sons
CitationInternational Journal of Intelligent Systems 16: 1069- 1083 (2001)
AbstractIn this article we compare two contrasting methods, active set method (ASM) and genetic algorithms, for learning the weights in aggregation operators, such as weighted mean (WM), ordered weighted average (OWA), and weighted ordered weighted average (WOWA). We give the formal definitions for each of the aggregation operators, explain the two learning methods, give results of processing for each of the methods and operators with simple test datasets, and contrast the approaches and results.
URIhttp://hdl.handle.net/10261/159898
Identifiersdoi: 10.1002/int.1050
issn: 0884-8173
Appears in Collections:(IIIA) Artículos
Files in This Item:
File Description SizeFormat 
accesoRestringido.pdf15,38 kBAdobe PDFThumbnail
View/Open
Show full item record
Review this work
 

Related articles:


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.