%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
}