{"publication_identifier":{"isbn":["978-3-031-14714-2"]},"user_id":"15504","author":[{"full_name":"Prager, Raphael Patrick","last_name":"Prager","first_name":"Raphael Patrick"},{"first_name":"Moritz Vinzent","full_name":"Seiler, Moritz Vinzent","last_name":"Seiler"},{"full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike","id":"100740","orcid":"0000-0002-9788-8282"},{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"}],"year":"2022","doi":"10.1007/978-3-031-14714-2_1","date_created":"2023-08-04T07:12:33Z","status":"public","_id":"46304","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","publisher":"Springer International Publishing","citation":{"ama":"Prager RP, Seiler MV, Trautmann H, Kerschke P. Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods. In: Rudolph G, Kononova AV, Aguirre H, Kerschke P, Ochoa G, Tušar T, eds. <i>Parallel Problem Solving from Nature — PPSN XVII</i>. Springer International Publishing; 2022:3–17. doi:<a href=\"https://doi.org/10.1007/978-3-031-14714-2_1\">10.1007/978-3-031-14714-2_1</a>","ieee":"R. P. Prager, M. V. Seiler, H. Trautmann, and P. Kerschke, “Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods,” in <i>Parallel Problem Solving from Nature — PPSN XVII</i>, 2022, pp. 3–17, doi: <a href=\"https://doi.org/10.1007/978-3-031-14714-2_1\">10.1007/978-3-031-14714-2_1</a>.","bibtex":"@inproceedings{Prager_Seiler_Trautmann_Kerschke_2022, place={Cham}, title={Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods}, DOI={<a href=\"https://doi.org/10.1007/978-3-031-14714-2_1\">10.1007/978-3-031-14714-2_1</a>}, booktitle={Parallel Problem Solving from Nature — PPSN XVII}, publisher={Springer International Publishing}, author={Prager, Raphael Patrick and Seiler, Moritz Vinzent and Trautmann, Heike and Kerschke, Pascal}, editor={Rudolph, Günter and Kononova, Anna V. and Aguirre, Hernán and Kerschke, Pascal and Ochoa, Gabriela and Tušar, Tea}, year={2022}, pages={3–17} }","short":"R.P. Prager, M.V. Seiler, H. Trautmann, P. Kerschke, in: G. Rudolph, A.V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, T. Tušar (Eds.), Parallel Problem Solving from Nature — PPSN XVII, Springer International Publishing, Cham, 2022, pp. 3–17.","apa":"Prager, R. P., Seiler, M. V., Trautmann, H., &#38; Kerschke, P. (2022). Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods. In G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, &#38; T. Tušar (Eds.), <i>Parallel Problem Solving from Nature — PPSN XVII</i> (pp. 3–17). Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-031-14714-2_1\">https://doi.org/10.1007/978-3-031-14714-2_1</a>","chicago":"Prager, Raphael Patrick, Moritz Vinzent Seiler, Heike Trautmann, and Pascal Kerschke. “Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods.” In <i>Parallel Problem Solving from Nature — PPSN XVII</i>, edited by Günter Rudolph, Anna V. Kononova, Hernán Aguirre, Pascal Kerschke, Gabriela Ochoa, and Tea Tušar, 3–17. Cham: Springer International Publishing, 2022. <a href=\"https://doi.org/10.1007/978-3-031-14714-2_1\">https://doi.org/10.1007/978-3-031-14714-2_1</a>.","mla":"Prager, Raphael Patrick, et al. “Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods.” <i>Parallel Problem Solving from Nature — PPSN XVII</i>, edited by Günter Rudolph et al., Springer International Publishing, 2022, pp. 3–17, doi:<a href=\"https://doi.org/10.1007/978-3-031-14714-2_1\">10.1007/978-3-031-14714-2_1</a>."},"date_updated":"2023-10-16T12:49:33Z","place":"Cham","language":[{"iso":"eng"}],"page":"3–17","abstract":[{"text":"In recent years, feature-based automated algorithm selection using exploratory landscape analysis has demonstrated its great potential in single-objective continuous black-box optimization. However, feature computation is problem-specific and can be costly in terms of computational resources. This paper investigates feature-free approaches that rely on state-of-the-art deep learning techniques operating on either images or point clouds. We show that point-cloud-based strategies, in particular, are highly competitive and also substantially reduce the size of the required solver portfolio. Moreover, we highlight the effect and importance of cost-sensitive learning in automated algorithm selection models.","lang":"eng"}],"publication":"Parallel Problem Solving from Nature — PPSN XVII","title":"Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods","editor":[{"full_name":"Rudolph, Günter","last_name":"Rudolph","first_name":"Günter"},{"last_name":"Kononova","full_name":"Kononova, Anna V.","first_name":"Anna V."},{"full_name":"Aguirre, Hernán","last_name":"Aguirre","first_name":"Hernán"},{"last_name":"Kerschke","full_name":"Kerschke, Pascal","first_name":"Pascal"},{"first_name":"Gabriela","last_name":"Ochoa","full_name":"Ochoa, Gabriela"},{"first_name":"Tea","last_name":"Tušar","full_name":"Tušar, Tea"}]}