%0 Conference Paper %1 Seiler2022-sr %A Seiler, Moritz Vinzent %A Prager, Raphael Patrick %A Kerschke, Pascal %A Trautmann, Heike %B Proceedings of the Genetic and Evolutionary Computation Conference %C New York, NY, USA %D 2022 %I ACM %K topic_engineering topic_lifescience %T A collection of deep learning-based feature-free approaches for characterizing single-objective continuous fitness landscapes

@inproceedings{Seiler2022-sr, added-at = {2024-09-10T11:56:37.000+0200}, address = {New York, NY, USA}, author = {Seiler, Moritz Vinzent and Prager, Raphael Patrick and Kerschke, Pascal and Trautmann, Heike}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2e3879bd3385364645644a911c3bc49c5/scadsfct}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference}, conference = {GECCO '22: Genetic and Evolutionary Computation Conference}, interhash = {68d4c6d6f7c8813cd48324003279d23c}, intrahash = {e3879bd3385364645644a911c3bc49c5}, keywords = {topic_engineering topic_lifescience}, location = {Boston Massachusetts}, month = jul, publisher = {ACM}, timestamp = {2024-11-22T15:45:33.000+0100}, title = {A collection of deep learning-based feature-free approaches for characterizing single-objective continuous fitness landscapes}, year = 2022 }