Some of the data underlying the publication "Fighting the curse of dimensionality: A machine learning approach to finding global optima"
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
Datacite citation style:
Julian Schumann (2021): Some of the data underlying the publication "Fighting the curse of dimensionality: A machine learning approach to finding global optima". Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/17111648.v1Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
licenceCC BY 4.0
The data assembled here should allow the reproduction of the Figures 4 and 6 from the mentioned paper. The corresponding code can be found at https://github.com/julianschumann/ae-opt.
- 2021-12-03 first online, published, posted
organizationsTU Delft, Faculty of Mechanical, Maritime and Materials Engineering (3mE)