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A collection of deep learning-based feature-free approaches..:
, In:
Proceedings of the Genetic and Evolutionary Computation Conference
,
Seiler, Moritz Vinzent
;
Prager, Raphael Patrick
;
Kerschke, Pascal
. - p. 657-665 , 2022
Link:
https://dl.acm.org/doi/10.1145/3512290.3528834
RT T1
Proceedings of the Genetic and Evolutionary Computation Conference
: T1
A collection of deep learning-based feature-free approaches for characterizing single-objective continuous fitness landscapes
UL https://suche.suub.uni-bremen.de/peid=acm-3528834&Exemplar=1&LAN=DE A1 Seiler, Moritz Vinzent A1 Prager, Raphael Patrick A1 Kerschke, Pascal A1 Trautmann, Heike PB ACM YR 2022 K1 continuous black-box optimization K1 deep learning K1 exploratory landscape analysis K1 fitness landscape K1 Computing methodologies K1 Machine learning K1 Machine learning approaches K1 Neural networks K1 Learning paradigms K1 Supervised learning K1 Supervised learning by regression K1 Artificial intelligence K1 Search methodologies K1 Continuous space search K1 Classification and regression trees K1 Kernel methods K1 Support vector machines SP 657 OP 665 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3512290.3528834 DO https://dl.acm.org/doi/10.1145/3512290.3528834 SF ELIB - SuUB Bremen
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