[{"language":[{"iso":"eng"}],"_id":"46307","department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","abstract":[{"lang":"eng","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."}],"status":"public","publication":"Proceedings of the Genetic and Evolutionary Computation Conference","type":"conference","title":"A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes","doi":"10.1145/3512290.3528834","publisher":"Association for Computing Machinery","date_updated":"2024-06-07T07:13:23Z","date_created":"2023-08-04T07:15:59Z","author":[{"last_name":"Seiler","id":"105520","full_name":"Seiler, Moritz","first_name":"Moritz"},{"first_name":"Raphael Patrick","last_name":"Prager","full_name":"Prager, Raphael Patrick"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","id":"100740","full_name":"Trautmann, Heike"}],"year":"2022","place":"New York, NY, USA","page":"657–665","citation":{"ama":"Seiler M, 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>","ieee":"M. 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>.","chicago":"Seiler, Moritz, 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 and Prager, Raphael Patrick and Kerschke, Pascal and Trautmann, Heike}, year={2022}, pages={657–665} }","short":"M. 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.","mla":"Seiler, Moritz, 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>.","apa":"Seiler, M., 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>"},"publication_identifier":{"isbn":["9781450392372"]}},{"department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","_id":"46304","language":[{"iso":"eng"}],"publication":"Parallel Problem Solving from Nature — PPSN XVII","type":"conference","status":"public","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"}],"editor":[{"first_name":"Günter","full_name":"Rudolph, Günter","last_name":"Rudolph"},{"first_name":"Anna V.","last_name":"Kononova","full_name":"Kononova, Anna V."},{"first_name":"Hernán","last_name":"Aguirre","full_name":"Aguirre, Hernán"},{"last_name":"Kerschke","full_name":"Kerschke, Pascal","first_name":"Pascal"},{"first_name":"Gabriela","last_name":"Ochoa","full_name":"Ochoa, Gabriela"},{"last_name":"Tušar","full_name":"Tušar, Tea","first_name":"Tea"}],"author":[{"full_name":"Prager, Raphael Patrick","last_name":"Prager","first_name":"Raphael Patrick"},{"first_name":"Moritz","last_name":"Seiler","id":"105520","full_name":"Seiler, Moritz"},{"orcid":"0000-0002-9788-8282","last_name":"Trautmann","full_name":"Trautmann, Heike","id":"100740","first_name":"Heike"},{"first_name":"Pascal","full_name":"Kerschke, Pascal","last_name":"Kerschke"}],"date_created":"2023-08-04T07:12:33Z","publisher":"Springer International Publishing","date_updated":"2024-06-07T07:13:47Z","doi":"10.1007/978-3-031-14714-2_1","title":"Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods","publication_identifier":{"isbn":["978-3-031-14714-2"]},"page":"3–17","citation":{"ieee":"R. P. Prager, M. 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>.","chicago":"Prager, Raphael Patrick, Moritz 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>.","ama":"Prager RP, Seiler M, 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>","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>.","short":"R.P. Prager, M. 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.","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 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} }","apa":"Prager, R. P., Seiler, M., 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>"},"year":"2022","place":"Cham"},{"editor":[{"full_name":"the Advancement of Artificial Intelligence (AAAI) Association, for","last_name":"the Advancement of Artificial Intelligence (AAAI) Association","first_name":"for"}],"abstract":[{"lang":"eng","text":"Social media platforms are essential for information sharing and, thus, prone to coordinated dis- and misinformation campaigns. Nevertheless, research in this area is hampered by strict data sharing regulations imposed by the platforms, resulting in a lack of benchmark data. Previous work focused on circumventing these rules by either pseudonymizing the data or sharing fragments. In this work, we will address the benchmarking crisis by presenting a methodology that can be used to create artificial campaigns out of original campaign building blocks. We conduct a proof-of-concept study using the freely available generative language model GPT-Neo in this context and demonstrate that the campaign patterns can flexibly be adapted to an underlying social media stream and evade state-of-the-art campaign detection approaches based on stream clustering. Thus, we not only provide a framework for artificial benchmark generation but also demonstrate the possible adversarial nature of such benchmarks for challenging and advancing current campaign detection methods."}],"status":"public","publication":"Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM)","type":"conference","language":[{"iso":"eng"}],"_id":"46303","department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","place":"Palo Alto, CA, USA","year":"2022","page":"1–10","citation":{"ama":"Pohl JS, Assenmacher D, Seiler M, Trautmann H, Grimme C. Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches. In: the Advancement of Artificial Intelligence (AAAI) Association  for, ed. <i>Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM)</i>. AAAI Press; 2022:1–10. doi:<a href=\"https://doi.org/10.36190/2022.91\">10.36190/2022.91</a>","chicago":"Pohl, Janina Susanne, Dennis Assenmacher, Moritz Seiler, Heike Trautmann, and Christian Grimme. “Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches.” In <i>Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM)</i>, edited by for the Advancement of Artificial Intelligence (AAAI) Association, 1–10. Palo Alto, CA, USA: AAAI Press, 2022. <a href=\"https://doi.org/10.36190/2022.91\">https://doi.org/10.36190/2022.91</a>.","ieee":"J. S. Pohl, D. Assenmacher, M. Seiler, H. Trautmann, and C. Grimme, “Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches,” in <i>Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM)</i>, 2022, pp. 1–10, doi: <a href=\"https://doi.org/10.36190/2022.91\">10.36190/2022.91</a>.","apa":"Pohl, J. S., Assenmacher, D., Seiler, M., Trautmann, H., &#38; Grimme, C. (2022). Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches. In  for the Advancement of Artificial Intelligence (AAAI) Association (Ed.), <i>Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM)</i> (pp. 1–10). AAAI Press. <a href=\"https://doi.org/10.36190/2022.91\">https://doi.org/10.36190/2022.91</a>","mla":"Pohl, Janina Susanne, et al. “Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches.” <i>Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM)</i>, edited by for the Advancement of Artificial Intelligence (AAAI) Association, AAAI Press, 2022, pp. 1–10, doi:<a href=\"https://doi.org/10.36190/2022.91\">10.36190/2022.91</a>.","short":"J.S. Pohl, D. Assenmacher, M. Seiler, H. Trautmann, C. Grimme, in:  for the Advancement of Artificial Intelligence (AAAI) Association (Ed.), Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM), AAAI Press, Palo Alto, CA, USA, 2022, pp. 1–10.","bibtex":"@inproceedings{Pohl_Assenmacher_Seiler_Trautmann_Grimme_2022, place={Palo Alto, CA, USA}, title={Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches}, DOI={<a href=\"https://doi.org/10.36190/2022.91\">10.36190/2022.91</a>}, booktitle={Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM)}, publisher={AAAI Press}, author={Pohl, Janina Susanne and Assenmacher, Dennis and Seiler, Moritz and Trautmann, Heike and Grimme, Christian}, editor={the Advancement of Artificial Intelligence (AAAI) Association, for}, year={2022}, pages={1–10} }"},"title":"Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches","doi":"10.36190/2022.91","date_updated":"2024-06-07T07:13:35Z","publisher":"AAAI Press","author":[{"first_name":"Janina Susanne","full_name":"Pohl, Janina Susanne","last_name":"Pohl"},{"last_name":"Assenmacher","full_name":"Assenmacher, Dennis","first_name":"Dennis"},{"first_name":"Moritz","last_name":"Seiler","id":"105520","full_name":"Seiler, Moritz"},{"first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","full_name":"Trautmann, Heike","id":"100740"},{"first_name":"Christian","full_name":"Grimme, Christian","last_name":"Grimme"}],"date_created":"2023-08-04T07:11:34Z"},{"citation":{"ieee":"L. Clever, J. S. Pohl, J. Bossek, P. Kerschke, and H. Trautmann, “Process-Oriented Stream Classification Pipeline: A Literature Review,” <i>Applied Sciences</i>, vol. 12, no. 8, pp. 1–44, 2022, doi: <a href=\"https://doi.org/10.3390/app12189094\">10.3390/app12189094</a>.","chicago":"Clever, Lena, Janina Susanne Pohl, Jakob Bossek, Pascal Kerschke, and Heike Trautmann. “Process-Oriented Stream Classification Pipeline: A Literature Review.” <i>Applied Sciences</i> 12, no. 8 (2022): 1–44. <a href=\"https://doi.org/10.3390/app12189094\">https://doi.org/10.3390/app12189094</a>.","ama":"Clever L, Pohl JS, Bossek J, Kerschke P, Trautmann H. Process-Oriented Stream Classification Pipeline: A Literature Review. <i>Applied Sciences</i>. 2022;12(8):1–44. doi:<a href=\"https://doi.org/10.3390/app12189094\">10.3390/app12189094</a>","apa":"Clever, L., Pohl, J. S., Bossek, J., Kerschke, P., &#38; Trautmann, H. (2022). Process-Oriented Stream Classification Pipeline: A Literature Review. <i>Applied Sciences</i>, <i>12</i>(8), 1–44. <a href=\"https://doi.org/10.3390/app12189094\">https://doi.org/10.3390/app12189094</a>","bibtex":"@article{Clever_Pohl_Bossek_Kerschke_Trautmann_2022, title={Process-Oriented Stream Classification Pipeline: A Literature Review}, volume={12}, DOI={<a href=\"https://doi.org/10.3390/app12189094\">10.3390/app12189094</a>}, number={8}, journal={Applied Sciences}, author={Clever, Lena and Pohl, Janina Susanne and Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}, year={2022}, pages={1–44} }","mla":"Clever, Lena, et al. “Process-Oriented Stream Classification Pipeline: A Literature Review.” <i>Applied Sciences</i>, vol. 12, no. 8, 2022, pp. 1–44, doi:<a href=\"https://doi.org/10.3390/app12189094\">10.3390/app12189094</a>.","short":"L. Clever, J.S. Pohl, J. Bossek, P. Kerschke, H. Trautmann, Applied Sciences 12 (2022) 1–44."},"page":"1–44","intvolume":"        12","year":"2022","issue":"8","doi":"10.3390/app12189094","title":"Process-Oriented Stream Classification Pipeline: A Literature Review","author":[{"full_name":"Clever, Lena","last_name":"Clever","first_name":"Lena"},{"first_name":"Janina Susanne","last_name":"Pohl","full_name":"Pohl, Janina Susanne"},{"first_name":"Jakob","full_name":"Bossek, Jakob","id":"102979","last_name":"Bossek","orcid":"0000-0002-4121-4668"},{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"},{"first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike","id":"100740"}],"date_created":"2023-08-04T07:17:23Z","volume":12,"date_updated":"2024-06-10T12:02:17Z","status":"public","abstract":[{"text":"Due to the rise of continuous data-generating applications, analyzing data streams has gained increasing attention over the past decades. A core research area in stream data is stream classification, which categorizes or detects data points within an evolving stream of observations. Areas of stream classification are diverse—ranging, e.g., from monitoring sensor data to analyzing a wide range of (social) media applications. Research in stream classification is related to developing methods that adapt to the changing and potentially volatile data stream. It focuses on individual aspects of the stream classification pipeline, e.g., designing suitable algorithm architectures, an efficient train and test procedure, or detecting so-called concept drifts. As a result of the many different research questions and strands, the field is challenging to grasp, especially for beginners. This survey explores, summarizes, and categorizes work within the domain of stream classification and identifies core research threads over the past few years. It is structured based on the stream classification process to facilitate coordination within this complex topic, including common application scenarios and benchmarking data sets. Thus, both newcomers to the field and experts who want to widen their scope can gain (additional) insight into this research area and find starting points and pointers to more in-depth literature on specific issues and research directions in the field.","lang":"eng"}],"type":"journal_article","publication":"Applied Sciences","language":[{"iso":"eng"}],"user_id":"15504","department":[{"_id":"34"},{"_id":"819"}],"_id":"46309"},{"publication":"Parallel Problem Solving from Nature — PPSN XVII","type":"conference","status":"public","editor":[{"first_name":"G","full_name":"Rudolph, G","last_name":"Rudolph"},{"full_name":"Kononova, AV","last_name":"Kononova","first_name":"AV"},{"first_name":"H","full_name":"Aguirre, H","last_name":"Aguirre"},{"first_name":"P","full_name":"Kerschke, P","last_name":"Kerschke"},{"last_name":"Ochoa","full_name":"Ochoa, G","first_name":"G"},{"last_name":"Tušar","full_name":"Tušar, T","first_name":"T"}],"department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","_id":"46302","language":[{"iso":"eng"}],"publication_identifier":{"isbn":["978-3-031-14714-2"]},"page":"192–206","citation":{"apa":"Heins, J., Rook, J., Schäpermeier, L., Kerschke, P., Bossek, J., &#38; Trautmann, H. (2022). BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems. In G. Rudolph, A. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, &#38; T. Tušar (Eds.), <i>Parallel Problem Solving from Nature — PPSN XVII</i> (pp. 192–206). Springer International Publishing.","ama":"Heins J, Rook J, Schäpermeier L, Kerschke P, Bossek J, Trautmann H. BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems. In: Rudolph G, Kononova A, Aguirre H, Kerschke P, Ochoa G, Tušar T, eds. <i>Parallel Problem Solving from Nature — PPSN XVII</i>. Springer International Publishing; 2022:192–206.","bibtex":"@inproceedings{Heins_Rook_Schäpermeier_Kerschke_Bossek_Trautmann_2022, place={Cham}, title={BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems}, booktitle={Parallel Problem Solving from Nature — PPSN XVII}, publisher={Springer International Publishing}, author={Heins, J and Rook, J and Schäpermeier, L and Kerschke, P and Bossek, Jakob and Trautmann, Heike}, editor={Rudolph, G and Kononova, AV and Aguirre, H and Kerschke, P and Ochoa, G and Tušar, T}, year={2022}, pages={192–206} }","short":"J. Heins, J. Rook, L. Schäpermeier, P. Kerschke, J. Bossek, H. Trautmann, in: G. Rudolph, A. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, T. Tušar (Eds.), Parallel Problem Solving from Nature — PPSN XVII, Springer International Publishing, Cham, 2022, pp. 192–206.","mla":"Heins, J., et al. “BBE: Basin-Based Evaluation of Multimodal Multi-Objective Optimization Problems.” <i>Parallel Problem Solving from Nature — PPSN XVII</i>, edited by G Rudolph et al., Springer International Publishing, 2022, pp. 192–206.","ieee":"J. Heins, J. Rook, L. Schäpermeier, P. Kerschke, J. Bossek, and H. Trautmann, “BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems,” in <i>Parallel Problem Solving from Nature — PPSN XVII</i>, 2022, pp. 192–206.","chicago":"Heins, J, J Rook, L Schäpermeier, P Kerschke, Jakob Bossek, and Heike Trautmann. “BBE: Basin-Based Evaluation of Multimodal Multi-Objective Optimization Problems.” In <i>Parallel Problem Solving from Nature — PPSN XVII</i>, edited by G Rudolph, AV Kononova, H Aguirre, P Kerschke, G Ochoa, and T Tušar, 192–206. Cham: Springer International Publishing, 2022."},"place":"Cham","year":"2022","author":[{"first_name":"J","last_name":"Heins","full_name":"Heins, J"},{"full_name":"Rook, J","last_name":"Rook","first_name":"J"},{"full_name":"Schäpermeier, L","last_name":"Schäpermeier","first_name":"L"},{"last_name":"Kerschke","full_name":"Kerschke, P","first_name":"P"},{"first_name":"Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","id":"102979","full_name":"Bossek, Jakob"},{"full_name":"Trautmann, Heike","id":"100740","orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike"}],"date_created":"2023-08-04T07:10:52Z","publisher":"Springer International Publishing","date_updated":"2024-06-10T12:02:35Z","title":"BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems"},{"date_created":"2023-02-05T22:04:28Z","author":[{"first_name":"Christian","last_name":"Fuchs","orcid":"0000-0003-0589-4579","full_name":"Fuchs, Christian","id":"21863"}],"date_updated":"2024-06-10T13:39:27Z","publisher":"transcript Verlag","title":"Der Tod und die Liebe: Die Metaphysik der Kommunikation","citation":{"apa":"Fuchs, C. (2022). Der Tod und die Liebe: Die Metaphysik der Kommunikation. In W. von Gahlen-Hoops (Ed.), <i>Soziale Ordnungen des Sterbens</i>. transcript Verlag.","bibtex":"@inbook{Fuchs_2022, place={Bielefeld}, title={Der Tod und die Liebe: Die Metaphysik der Kommunikation}, booktitle={Soziale Ordnungen des Sterbens}, publisher={transcript Verlag}, author={Fuchs, Christian}, editor={von Gahlen-Hoops, Wolfgang}, year={2022} }","short":"C. Fuchs, in: W. von Gahlen-Hoops (Ed.), Soziale Ordnungen des Sterbens, transcript Verlag, Bielefeld, 2022.","mla":"Fuchs, Christian. “Der Tod und die Liebe: Die Metaphysik der Kommunikation.” <i>Soziale Ordnungen des Sterbens</i>, edited by Wolfgang von Gahlen-Hoops, transcript Verlag, 2022.","ama":"Fuchs C. Der Tod und die Liebe: Die Metaphysik der Kommunikation. In: von Gahlen-Hoops W, ed. <i>Soziale Ordnungen des Sterbens</i>. transcript Verlag; 2022.","ieee":"C. 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