{"department":[{"_id":"34"},{"_id":"819"}],"citation":{"apa":"Seiler, M. V., Prager, R. P., Kerschke, P., &#38; Trautmann, H. (2022). A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 657–665. <a href=\"https://doi.org/10.1145/3512290.3528834\">https://doi.org/10.1145/3512290.3528834</a>","mla":"Seiler, Moritz Vinzent, et al. “A Collection of Deep Learning-Based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2022, pp. 657–665, doi:<a href=\"https://doi.org/10.1145/3512290.3528834\">10.1145/3512290.3528834</a>.","chicago":"Seiler, Moritz Vinzent, Raphael Patrick Prager, Pascal Kerschke, and Heike Trautmann. “A Collection of Deep Learning-Based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 657–665. New York, NY, USA: Association for Computing Machinery, 2022. <a href=\"https://doi.org/10.1145/3512290.3528834\">https://doi.org/10.1145/3512290.3528834</a>.","bibtex":"@inproceedings{Seiler_Prager_Kerschke_Trautmann_2022, place={New York, NY, USA}, title={A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes}, DOI={<a href=\"https://doi.org/10.1145/3512290.3528834\">10.1145/3512290.3528834</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Seiler, Moritz Vinzent and Prager, Raphael Patrick and Kerschke, Pascal and Trautmann, Heike}, year={2022}, pages={657–665} }","ieee":"M. V. Seiler, R. P. Prager, P. Kerschke, and H. Trautmann, “A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2022, pp. 657–665, doi: <a href=\"https://doi.org/10.1145/3512290.3528834\">10.1145/3512290.3528834</a>.","ama":"Seiler MV, Prager RP, Kerschke P, Trautmann H. A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. Association for Computing Machinery; 2022:657–665. doi:<a href=\"https://doi.org/10.1145/3512290.3528834\">10.1145/3512290.3528834</a>","short":"M.V. Seiler, R.P. Prager, P. Kerschke, H. Trautmann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2022, pp. 657–665."},"publisher":"Association for Computing Machinery","type":"conference","year":"2022","author":[{"first_name":"Moritz Vinzent","last_name":"Seiler","full_name":"Seiler, Moritz Vinzent"},{"full_name":"Prager, Raphael Patrick","last_name":"Prager","first_name":"Raphael Patrick"},{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"},{"orcid":"0000-0002-9788-8282","id":"100740","first_name":"Heike","last_name":"Trautmann","full_name":"Trautmann, Heike"}],"user_id":"15504","publication_identifier":{"isbn":["9781450392372"]},"_id":"46307","status":"public","doi":"10.1145/3512290.3528834","date_created":"2023-08-04T07:15:59Z","publication":"Proceedings of the Genetic and Evolutionary Computation Conference","abstract":[{"text":"Exploratory Landscape Analysis is a powerful technique for numerically characterizing landscapes of single-objective continuous optimization problems. Landscape insights are crucial both for problem understanding as well as for assessing benchmark set diversity and composition. Despite the irrefutable usefulness of these features, they suffer from their own ailments and downsides. Hence, in this work we provide a collection of different approaches to characterize optimization landscapes. Similar to conventional landscape features, we require a small initial sample. However, instead of computing features based on that sample, we develop alternative representations of the original sample. These range from point clouds to 2D images and, therefore, are entirely feature-free. We demonstrate and validate our devised methods on the BBOB testbed and predict, with the help of Deep Learning, the high-level, expert-based landscape properties such as the degree of multimodality and the existence of funnel structures. The quality of our approaches is on par with methods relying on the traditional landscape features. Thereby, we provide an exciting new perspective on every research area which utilizes problem information such as problem understanding and algorithm design as well as automated algorithm configuration and selection.","lang":"eng"}],"title":"A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes","date_updated":"2023-10-16T12:52:01Z","page":"657–665","language":[{"iso":"eng"}],"place":"New York, NY, USA"}