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
The importance of landscape features for performance predic..:
, In:
Proceedings of the Genetic and Evolutionary Computation Conference
,
Kostovska, Ana
;
Vermetten, Diederick
;
Džeroski, Sašo
... - p. 648-656 , 2022
Link:
https://dl.acm.org/doi/10.1145/3512290.3528832
RT T1 <cTitle>Proceedings of the Genetic and Evolutionary Computation Conference</cTitle>: T1 <Title>The importance of landscape features for performance prediction of modular CMA-ES variants</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3528832&Exemplar=1&LAN=DE A1 Kostovska, Ana A1 Vermetten, Diederick A1 Džeroski, Sašo A1 Doerr, Carola A1 Korosec, Peter A1 Eftimov, Tome PB ACM YR 2022 K1 evolutionary computation K1 exploratory landscape analysis K1 modular CMA-ES K1 Computing methodologies K1 Machine learning K1 Theory of computation K1 Design and analysis of algorithms K1 Learning paradigms K1 Supervised learning K1 Supervised learning by regression K1 Machine learning approaches K1 Learning latent representations SP 648 OP 656 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3512290.3528832 DO https://dl.acm.org/doi/10.1145/3512290.3528832 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)
