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
Improving the Performance of EA-based Multi-population Mode..:
, In:
Proceedings of the Genetic and Evolutionary Computation Conference Companion
,
Gómez-López, Juan Carlos
;
Castillo-Secilla, Daniel
;
González, Jesús
- p. 335-338 , 2024
Link:
https://dl.acm.org/doi/10.1145/3638530.3654424
RT T1
Proceedings of the Genetic and Evolutionary Computation Conference Companion
: T1
Improving the Performance of EA-based Multi-population Models for Feature Selection Problems by Reducing the Individual Size in the Initial Population
UL https://suche.suub.uni-bremen.de/peid=acm-3654424&Exemplar=1&LAN=DE A1 Gómez-López, Juan Carlos A1 Castillo-Secilla, Daniel A1 González, Jesús PB ACM YR 2024 K1 evolutionary procedures K1 energy-aware computing K1 feature selection K1 high-dimensional data K1 EEG classification K1 multi-population models K1 Computing methodologies K1 Machine learning K1 Machine learning algorithms K1 Feature selection K1 Machine learning approaches K1 Bio-inspired approaches K1 Genetic algorithms K1 Parallel computing methodologies K1 Parallel algorithms K1 Applied computing K1 Life and medical sciences K1 Bioinformatics SP 335 OP 338 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3638530.3654424 DO https://dl.acm.org/doi/10.1145/3638530.3654424 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)