I agree that this site is using cookies. You can find further informations
here
.
X
Login
Merkliste (
0
)
Home
About us
Home About us
Our history
Profile
Press & public relations
Friends
The library in figures
Exhibitions
Projects
Training, internships, careers
Films
Services & Information
Home Services & Information
Lending and interlibrary loans
Returns and renewals
Training and library tours
My Account
Library cards
New to the library?
Download Information
Opening hours
Learning spaces
PC, WLAN, copy, scan and print
Catalogs and collections
Home Catalogs and Collections
Rare books and manuscripts
Digital collections
Subject Areas
Our sites
Home Our sites
Central Library
Law Library (Juridicum)
BB Business and Economics (BB11)
BB Physics and Electrical Engineering
TB Engineering and Social Sciences
TB Economics and Nautical Sciences
TB Music
TB Art & Design
TB Bremerhaven
Contact the library
Home Contact the library
Staff Directory
Open access & publishing
Home Open access & publishing
Reference management: Citavi & RefWorks
Publishing documents
Open Access in Bremen
zur Desktop-Version
Toggle navigation
Merkliste
1 Ergebnisse
1
ARGEN + AREPO : mixing the artificial genetic engineerin..:
, In:
Proceedings of the 7th annual conference on Genetic and evolutionary computation
,
León-Barranco, Agustín
;
Barajas, Sandra E.
;
Reyes, Carlos A.
- p. 1597-1598 , 2005
Link:
https://dl.acm.org/doi/10.1145/1068009.1068277
RT T1
Proceedings of the 7th annual conference on Genetic and evolutionary computation
: T1
ARGEN + AREPO : mixing the artificial genetic engineering and artificial evolution of populations to improve the search process
UL https://suche.suub.uni-bremen.de/peid=acm-1068277&Exemplar=1&LAN=DE A1 León-Barranco, Agustín A1 Barajas, Sandra E. A1 Reyes, Carlos A. PB ACM YR 2005 K1 Hill-Climbers K1 artificial evolution of populations K1 artificial genetic engineering K1 classification K1 data selection K1 genetic algorithms K1 infant cry K1 nearest neighbors K1 wrapper model K1 Computing methodologies K1 Machine learning K1 Machine learning algorithms K1 Feature selection SP 1597 OP 1598 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/1068009.1068277 DO https://dl.acm.org/doi/10.1145/1068009.1068277 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)