[{"user_id":"15504","department":[{"_id":"819"},{"_id":"34"}],"_id":"46300","language":[{"iso":"eng"}],"type":"book_chapter","publication":"Hate Speech — Definitionen, Ausprägungen, Lösungen","status":"public","editor":[{"full_name":"Weitzel, Gerrit","last_name":"Weitzel","first_name":"Gerrit"},{"first_name":"Stephan","last_name":"Mündges","full_name":"Mündges, Stephan"}],"author":[{"last_name":"Niemann","full_name":"Niemann, Marco","first_name":"Marco"},{"full_name":"Assenmacher, Dennis","last_name":"Assenmacher","first_name":"Dennis"},{"full_name":"Brunk, Jens","last_name":"Brunk","first_name":"Jens"},{"full_name":"Riehle, Dennis Maximilian","last_name":"Riehle","first_name":"Dennis Maximilian"},{"full_name":"Becker, Jörg","last_name":"Becker","first_name":"Jörg"},{"first_name":"Heike","id":"100740","full_name":"Trautmann, Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann"}],"date_created":"2023-08-04T07:03:47Z","date_updated":"2023-10-16T12:35:41Z","publisher":"VS Verlag für Sozialwissenschaften","doi":"10.1007/978-3-658-35658-3_13","title":"(Semi-)Automatische Kommentarmoderation zur Erhaltung Konstruktiver Diskurse","publication_identifier":{"isbn":["978-3-658-35658-3"]},"citation":{"short":"M. Niemann, D. Assenmacher, J. Brunk, D.M. Riehle, J. Becker, H. Trautmann, in: G. Weitzel, S. Mündges (Eds.), Hate Speech — Definitionen, Ausprägungen, Lösungen, VS Verlag für Sozialwissenschaften, Wiesbaden, 2022, pp. 249–274.","mla":"Niemann, Marco, et al. “(Semi-)Automatische Kommentarmoderation Zur Erhaltung Konstruktiver Diskurse.” <i>Hate Speech — Definitionen, Ausprägungen, Lösungen</i>, edited by Gerrit Weitzel and Stephan Mündges, VS Verlag für Sozialwissenschaften, 2022, pp. 249–274, doi:<a href=\"https://doi.org/10.1007/978-3-658-35658-3_13\">10.1007/978-3-658-35658-3_13</a>.","bibtex":"@inbook{Niemann_Assenmacher_Brunk_Riehle_Becker_Trautmann_2022, place={Wiesbaden}, title={(Semi-)Automatische Kommentarmoderation zur Erhaltung Konstruktiver Diskurse}, DOI={<a href=\"https://doi.org/10.1007/978-3-658-35658-3_13\">10.1007/978-3-658-35658-3_13</a>}, booktitle={Hate Speech — Definitionen, Ausprägungen, Lösungen}, publisher={VS Verlag für Sozialwissenschaften}, author={Niemann, Marco and Assenmacher, Dennis and Brunk, Jens and Riehle, Dennis Maximilian and Becker, Jörg and Trautmann, Heike}, editor={Weitzel, Gerrit and Mündges, Stephan}, year={2022}, pages={249–274} }","apa":"Niemann, M., Assenmacher, D., Brunk, J., Riehle, D. M., Becker, J., &#38; Trautmann, H. (2022). (Semi-)Automatische Kommentarmoderation zur Erhaltung Konstruktiver Diskurse. In G. Weitzel &#38; S. Mündges (Eds.), <i>Hate Speech — Definitionen, Ausprägungen, Lösungen</i> (pp. 249–274). VS Verlag für Sozialwissenschaften. <a href=\"https://doi.org/10.1007/978-3-658-35658-3_13\">https://doi.org/10.1007/978-3-658-35658-3_13</a>","ieee":"M. Niemann, D. Assenmacher, J. Brunk, D. M. Riehle, J. Becker, and H. Trautmann, “(Semi-)Automatische Kommentarmoderation zur Erhaltung Konstruktiver Diskurse,” in <i>Hate Speech — Definitionen, Ausprägungen, Lösungen</i>, G. Weitzel and S. Mündges, Eds. Wiesbaden: VS Verlag für Sozialwissenschaften, 2022, pp. 249–274.","chicago":"Niemann, Marco, Dennis Assenmacher, Jens Brunk, Dennis Maximilian Riehle, Jörg Becker, and Heike Trautmann. “(Semi-)Automatische Kommentarmoderation Zur Erhaltung Konstruktiver Diskurse.” In <i>Hate Speech — Definitionen, Ausprägungen, Lösungen</i>, edited by Gerrit Weitzel and Stephan Mündges, 249–274. Wiesbaden: VS Verlag für Sozialwissenschaften, 2022. <a href=\"https://doi.org/10.1007/978-3-658-35658-3_13\">https://doi.org/10.1007/978-3-658-35658-3_13</a>.","ama":"Niemann M, Assenmacher D, Brunk J, Riehle DM, Becker J, Trautmann H. (Semi-)Automatische Kommentarmoderation zur Erhaltung Konstruktiver Diskurse. In: Weitzel G, Mündges S, eds. <i>Hate Speech — Definitionen, Ausprägungen, Lösungen</i>. VS Verlag für Sozialwissenschaften; 2022:249–274. doi:<a href=\"https://doi.org/10.1007/978-3-658-35658-3_13\">10.1007/978-3-658-35658-3_13</a>"},"page":"249–274","place":"Wiesbaden","year":"2022"},{"_id":"46301","department":[{"_id":"819"},{"_id":"34"}],"user_id":"15504","language":[{"iso":"eng"}],"publication":"Intelligent Information and Database Systems","type":"conference","editor":[{"first_name":"T","last_name":"et al. Tran","full_name":"et al. Tran, T"}],"status":"public","publisher":"Springer International Publishing","date_updated":"2023-10-16T12:35:22Z","author":[{"first_name":"D","last_name":"Assenmacher","full_name":"Assenmacher, D"},{"first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","id":"100740","full_name":"Trautmann, Heike"}],"date_created":"2023-08-04T07:04:54Z","title":"Textual One-Pass Stream Clustering with Automated Distance Threshold Adaption","doi":"10.1007/978-3-031-21743-2_1","year":"2022","place":"Cham","page":"3–16","citation":{"ieee":"D. Assenmacher and H. Trautmann, “Textual One-Pass Stream Clustering with Automated Distance Threshold Adaption,” in <i>Intelligent Information and Database Systems</i>, 2022, pp. 3–16, doi: <a href=\"https://doi.org/10.1007/978-3-031-21743-2_1\">10.1007/978-3-031-21743-2_1</a>.","chicago":"Assenmacher, D, and Heike Trautmann. “Textual One-Pass Stream Clustering with Automated Distance Threshold Adaption.” In <i>Intelligent Information and Database Systems</i>, edited by T et al. Tran, 3–16. Cham: Springer International Publishing, 2022. <a href=\"https://doi.org/10.1007/978-3-031-21743-2_1\">https://doi.org/10.1007/978-3-031-21743-2_1</a>.","ama":"Assenmacher D, Trautmann H. Textual One-Pass Stream Clustering with Automated Distance Threshold Adaption. In: et al. Tran T, ed. <i>Intelligent Information and Database Systems</i>. Springer International Publishing; 2022:3–16. doi:<a href=\"https://doi.org/10.1007/978-3-031-21743-2_1\">10.1007/978-3-031-21743-2_1</a>","apa":"Assenmacher, D., &#38; Trautmann, H. (2022). Textual One-Pass Stream Clustering with Automated Distance Threshold Adaption. In T. et al. Tran (Ed.), <i>Intelligent Information and Database Systems</i> (pp. 3–16). Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-031-21743-2_1\">https://doi.org/10.1007/978-3-031-21743-2_1</a>","bibtex":"@inproceedings{Assenmacher_Trautmann_2022, place={Cham}, title={Textual One-Pass Stream Clustering with Automated Distance Threshold Adaption}, DOI={<a href=\"https://doi.org/10.1007/978-3-031-21743-2_1\">10.1007/978-3-031-21743-2_1</a>}, booktitle={Intelligent Information and Database Systems}, publisher={Springer International Publishing}, author={Assenmacher, D and Trautmann, Heike}, editor={et al. Tran, T}, year={2022}, pages={3–16} }","mla":"Assenmacher, D., and Heike Trautmann. “Textual One-Pass Stream Clustering with Automated Distance Threshold Adaption.” <i>Intelligent Information and Database Systems</i>, edited by T et al. Tran, Springer International Publishing, 2022, pp. 3–16, doi:<a href=\"https://doi.org/10.1007/978-3-031-21743-2_1\">10.1007/978-3-031-21743-2_1</a>.","short":"D. Assenmacher, H. Trautmann, in: T. et al. Tran (Ed.), Intelligent Information and Database Systems, Springer International Publishing, Cham, 2022, pp. 3–16."}},{"abstract":[{"text":" Computational social science uses computational and statistical methods in order to evaluate social interaction. The public availability of data sets is thus a necessary precondition for reliable and replicable research. These data allow researchers to benchmark the computational methods they develop, test the generalizability of their findings, and build confidence in their results. When social media data are concerned, data sharing is often restricted for legal or privacy reasons, which makes the comparison of methods and the replicability of research results infeasible. Social media analytics research, consequently, faces an integrity crisis. How is it possible to create trust in computational or statistical analyses, when they cannot be validated by third parties? In this work, we explore this well-known, yet little discussed, problem for social media analytics. We investigate how this problem can be solved by looking at related computational research areas. Moreover, we propose and implement a prototype to address the problem in the form of a new evaluation framework that enables the comparison of algorithms without the need to exchange data directly, while maintaining flexibility for the algorithm design. ","lang":"eng"}],"status":"public","publication":"Social Science Computer Review","type":"journal_article","language":[{"iso":"eng"}],"_id":"46316","department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","year":"2022","page":"1496-1522","intvolume":"        40","citation":{"bibtex":"@article{Assenmacher_Weber_Preuss_Valdez_Bradshaw_Ross_Cresci_Trautmann_Neumann_Grimme_2022, title={Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem}, volume={40}, DOI={<a href=\"https://doi.org/10.1177/08944393211012268\">10.1177/08944393211012268</a>}, number={6}, journal={Social Science Computer Review}, author={Assenmacher, Dennis and Weber, Derek and Preuss, Mike and Valdez, André Calero and Bradshaw, Alison and Ross, Björn and Cresci, Stefano and Trautmann, Heike and Neumann, Frank and Grimme, Christian}, year={2022}, pages={1496–1522} }","mla":"Assenmacher, Dennis, et al. “Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem.” <i>Social Science Computer Review</i>, vol. 40, no. 6, 2022, pp. 1496–522, doi:<a href=\"https://doi.org/10.1177/08944393211012268\">10.1177/08944393211012268</a>.","short":"D. Assenmacher, D. Weber, M. Preuss, A.C. Valdez, A. Bradshaw, B. Ross, S. Cresci, H. Trautmann, F. Neumann, C. Grimme, Social Science Computer Review 40 (2022) 1496–1522.","apa":"Assenmacher, D., Weber, D., Preuss, M., Valdez, A. C., Bradshaw, A., Ross, B., Cresci, S., Trautmann, H., Neumann, F., &#38; Grimme, C. (2022). Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem. <i>Social Science Computer Review</i>, <i>40</i>(6), 1496–1522. <a href=\"https://doi.org/10.1177/08944393211012268\">https://doi.org/10.1177/08944393211012268</a>","ama":"Assenmacher D, Weber D, Preuss M, et al. Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem. <i>Social Science Computer Review</i>. 2022;40(6):1496-1522. doi:<a href=\"https://doi.org/10.1177/08944393211012268\">10.1177/08944393211012268</a>","ieee":"D. Assenmacher <i>et al.</i>, “Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem,” <i>Social Science Computer Review</i>, vol. 40, no. 6, pp. 1496–1522, 2022, doi: <a href=\"https://doi.org/10.1177/08944393211012268\">10.1177/08944393211012268</a>.","chicago":"Assenmacher, Dennis, Derek Weber, Mike Preuss, André Calero Valdez, Alison Bradshaw, Björn Ross, Stefano Cresci, Heike Trautmann, Frank Neumann, and Christian Grimme. “Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem.” <i>Social Science Computer Review</i> 40, no. 6 (2022): 1496–1522. <a href=\"https://doi.org/10.1177/08944393211012268\">https://doi.org/10.1177/08944393211012268</a>."},"issue":"6","title":"Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem","doi":"10.1177/08944393211012268","date_updated":"2023-10-16T12:57:24Z","volume":40,"author":[{"last_name":"Assenmacher","full_name":"Assenmacher, Dennis","first_name":"Dennis"},{"full_name":"Weber, Derek","last_name":"Weber","first_name":"Derek"},{"last_name":"Preuss","full_name":"Preuss, Mike","first_name":"Mike"},{"first_name":"André Calero","last_name":"Valdez","full_name":"Valdez, André Calero"},{"first_name":"Alison","full_name":"Bradshaw, Alison","last_name":"Bradshaw"},{"first_name":"Björn","last_name":"Ross","full_name":"Ross, Björn"},{"full_name":"Cresci, Stefano","last_name":"Cresci","first_name":"Stefano"},{"id":"100740","full_name":"Trautmann, Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike"},{"full_name":"Neumann, Frank","last_name":"Neumann","first_name":"Frank"},{"last_name":"Grimme","full_name":"Grimme, Christian","first_name":"Christian"}],"date_created":"2023-08-04T07:26:36Z"},{"language":[{"iso":"eng"}],"_id":"46306","user_id":"15504","department":[{"_id":"34"},{"_id":"819"}],"abstract":[{"text":"Hyperparameter optimization (HPO) is a key component of machine learning models for achieving peak predictive performance. While numerous methods and algorithms for HPO have been proposed over the last years, little progress has been made in illuminating and examining the actual structure of these black-box optimization problems. Exploratory landscape analysis (ELA) subsumes a set of techniques that can be used to gain knowledge about properties of unknown optimization problems. In this paper, we evaluate the performance of five different black-box optimizers on 30 HPO problems, which consist of two-, three- and five-dimensional continuous search spaces of the XGBoost learner trained on 10 different data sets. This is contrasted with the performance of the same optimizers evaluated on 360 problem instances from the black-box optimization benchmark (BBOB). We then compute ELA features on the HPO and BBOB problems and examine similarities and differences. A cluster analysis of the HPO and BBOB problems in ELA feature space allows us to identify how the HPO problems compare to the BBOB problems on a structural meta-level. We identify a subset of BBOB problems that are close to the HPO problems in ELA feature space and show that optimizer performance is comparably similar on these two sets of benchmark problems. We highlight open challenges of ELA for HPO and discuss potential directions of future research and applications.","lang":"eng"}],"editor":[{"full_name":"Rudolph, Günter","last_name":"Rudolph","first_name":"Günter"},{"first_name":"Anna V.","last_name":"Kononova","full_name":"Kononova, Anna V."},{"last_name":"Aguirre","full_name":"Aguirre, Hernán","first_name":"Hernán"},{"first_name":"Pascal","full_name":"Kerschke, Pascal","last_name":"Kerschke"},{"full_name":"Ochoa, Gabriela","last_name":"Ochoa","first_name":"Gabriela"},{"first_name":"Tea","last_name":"Tušar","full_name":"Tušar, Tea"}],"status":"public","type":"conference","publication":"Parallel Problem Solving from Nature — PPSN XVII","title":"HPO x ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis","doi":"10.1007/978-3-031-14714-2_40","publisher":"Springer International Publishing","date_updated":"2023-10-16T12:51:27Z","date_created":"2023-08-04T07:15:16Z","author":[{"first_name":"Lennart","full_name":"Schneider, Lennart","last_name":"Schneider"},{"full_name":"Schäpermeier, Lennart","last_name":"Schäpermeier","first_name":"Lennart"},{"first_name":"Raphael Patrick","last_name":"Prager","full_name":"Prager, Raphael Patrick"},{"last_name":"Bischl","full_name":"Bischl, Bernd","first_name":"Bernd"},{"full_name":"Trautmann, Heike","id":"100740","orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike"},{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"}],"place":"Cham","year":"2022","citation":{"short":"L. Schneider, L. Schäpermeier, R.P. Prager, B. Bischl, 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. 575–589.","mla":"Schneider, Lennart, et al. “HPO x ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis.” <i>Parallel Problem Solving from Nature — PPSN XVII</i>, edited by Günter Rudolph et al., Springer International Publishing, 2022, pp. 575–589, doi:<a href=\"https://doi.org/10.1007/978-3-031-14714-2_40\">10.1007/978-3-031-14714-2_40</a>.","bibtex":"@inproceedings{Schneider_Schäpermeier_Prager_Bischl_Trautmann_Kerschke_2022, place={Cham}, title={HPO x ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis}, DOI={<a href=\"https://doi.org/10.1007/978-3-031-14714-2_40\">10.1007/978-3-031-14714-2_40</a>}, booktitle={Parallel Problem Solving from Nature — PPSN XVII}, publisher={Springer International Publishing}, author={Schneider, Lennart and Schäpermeier, Lennart and Prager, Raphael Patrick and Bischl, Bernd 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={575–589} }","apa":"Schneider, L., Schäpermeier, L., Prager, R. P., Bischl, B., Trautmann, H., &#38; Kerschke, P. (2022). HPO x ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis. 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. 575–589). Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-031-14714-2_40\">https://doi.org/10.1007/978-3-031-14714-2_40</a>","ieee":"L. Schneider, L. Schäpermeier, R. P. Prager, B. Bischl, H. Trautmann, and P. Kerschke, “HPO x ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis,” in <i>Parallel Problem Solving from Nature — PPSN XVII</i>, 2022, pp. 575–589, doi: <a href=\"https://doi.org/10.1007/978-3-031-14714-2_40\">10.1007/978-3-031-14714-2_40</a>.","chicago":"Schneider, Lennart, Lennart Schäpermeier, Raphael Patrick Prager, Bernd Bischl, Heike Trautmann, and Pascal Kerschke. “HPO x ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis.” 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, 575–589. Cham: Springer International Publishing, 2022. <a href=\"https://doi.org/10.1007/978-3-031-14714-2_40\">https://doi.org/10.1007/978-3-031-14714-2_40</a>.","ama":"Schneider L, Schäpermeier L, Prager RP, Bischl B, Trautmann H, Kerschke P. HPO x ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis. 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; 2022:575–589. doi:<a href=\"https://doi.org/10.1007/978-3-031-14714-2_40\">10.1007/978-3-031-14714-2_40</a>"},"page":"575–589","publication_identifier":{"isbn":["978-3-031-14714-2"]}},{"status":"public","abstract":[{"lang":"eng","text":"Single-objective continuous optimization can be challenging, especially when dealing with multimodal problems. This work sheds light on the effects that multi-objective optimization may have in the single-objective space. For this purpose, we examine the inner mechanisms of the recently developed sophisticated local search procedure SOMOGSA. This method solves multimodal single-objective continuous optimization problems based on first expanding the problem with an additional objective (e.g., a sphere function) to the bi-objective domain and subsequently exploiting local structures of the resulting landscapes. Our study particularly focuses on the sensitivity of this multiobjectivization approach w.r.t. (1) the parametrization of the artificial second objective, as well as (2) the position of the initial starting points in the search space. As SOMOGSA is a modular framework for encapsulating local search, we integrate Nelder–Mead local search as optimizer in the respective module and compare the performance of the resulting hybrid local search to its original single-objective counterpart. We show that the SOMOGSA framework can significantly boost local search by multiobjectivization. Hence, combined with more sophisticated local search and metaheuristics, this may help solve highly multimodal optimization problems in the future."}],"type":"journal_article","publication":"Natural Computing","language":[{"iso":"eng"}],"user_id":"15504","department":[{"_id":"34"},{"_id":"819"}],"_id":"46308","citation":{"apa":"Aspar, P., Steinhoff, V., Schäpermeier, L., Kerschke, P., Trautmann, H., &#38; Grimme, C. (2022). The objective that freed me: a multi-objective local search approach for continuous single-objective optimization. <i>Natural Computing</i>, <i>1</i>, 1–15. <a href=\"https://doi.org/10.1007/s11047-022-09919-w\">https://doi.org/10.1007/s11047-022-09919-w</a>","short":"P. Aspar, V. Steinhoff, L. Schäpermeier, P. Kerschke, H. Trautmann, C. Grimme, Natural Computing 1 (2022) 1–15.","mla":"Aspar, Pelin, et al. “The Objective That Freed Me: A Multi-Objective Local Search Approach for Continuous Single-Objective Optimization.” <i>Natural Computing</i>, vol. 1, 2022, pp. 1–15, doi:<a href=\"https://doi.org/10.1007/s11047-022-09919-w\">10.1007/s11047-022-09919-w</a>.","bibtex":"@article{Aspar_Steinhoff_Schäpermeier_Kerschke_Trautmann_Grimme_2022, title={The objective that freed me: a multi-objective local search approach for continuous single-objective optimization}, volume={1}, DOI={<a href=\"https://doi.org/10.1007/s11047-022-09919-w\">10.1007/s11047-022-09919-w</a>}, journal={Natural Computing}, author={Aspar, Pelin and Steinhoff, Vera and Schäpermeier, Lennart and Kerschke, Pascal and Trautmann, Heike and Grimme, Christian}, year={2022}, pages={1–15} }","ieee":"P. Aspar, V. Steinhoff, L. Schäpermeier, P. Kerschke, H. Trautmann, and C. Grimme, “The objective that freed me: a multi-objective local search approach for continuous single-objective optimization,” <i>Natural Computing</i>, vol. 1, pp. 1–15, 2022, doi: <a href=\"https://doi.org/10.1007/s11047-022-09919-w\">10.1007/s11047-022-09919-w</a>.","chicago":"Aspar, Pelin, Vera Steinhoff, Lennart Schäpermeier, Pascal Kerschke, Heike Trautmann, and Christian Grimme. “The Objective That Freed Me: A Multi-Objective Local Search Approach for Continuous Single-Objective Optimization.” <i>Natural Computing</i> 1 (2022): 1–15. <a href=\"https://doi.org/10.1007/s11047-022-09919-w\">https://doi.org/10.1007/s11047-022-09919-w</a>.","ama":"Aspar P, Steinhoff V, Schäpermeier L, Kerschke P, Trautmann H, Grimme C. The objective that freed me: a multi-objective local search approach for continuous single-objective optimization. <i>Natural Computing</i>. 2022;1:1–15. doi:<a href=\"https://doi.org/10.1007/s11047-022-09919-w\">10.1007/s11047-022-09919-w</a>"},"page":"1–15","intvolume":"         1","year":"2022","doi":"10.1007/s11047-022-09919-w","title":"The objective that freed me: a multi-objective local search approach for continuous single-objective optimization","date_created":"2023-08-04T07:16:40Z","author":[{"first_name":"Pelin","full_name":"Aspar, Pelin","last_name":"Aspar"},{"last_name":"Steinhoff","full_name":"Steinhoff, Vera","first_name":"Vera"},{"full_name":"Schäpermeier, Lennart","last_name":"Schäpermeier","first_name":"Lennart"},{"last_name":"Kerschke","full_name":"Kerschke, Pascal","first_name":"Pascal"},{"orcid":"0000-0002-9788-8282","last_name":"Trautmann","id":"100740","full_name":"Trautmann, Heike","first_name":"Heike"},{"full_name":"Grimme, Christian","last_name":"Grimme","first_name":"Christian"}],"volume":1,"date_updated":"2023-10-16T12:52:33Z"},{"language":[{"iso":"eng"}],"keyword":["instance features","instance generation","quality diversity","TSP"],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference","abstract":[{"text":"Generating instances of different properties is key to algorithm selection methods that differentiate between the performance of different solvers for a given combinatorial optimization problem. A wide range of methods using evolutionary computation techniques has been introduced in recent years. With this paper, we contribute to this area of research by providing a new approach based on quality diversity (QD) that is able to explore the whole feature space. QD algorithms allow to create solutions of high quality within a given feature space by splitting it up into boxes and improving solution quality within each box. We use our QD approach for the generation of TSP instances to visualize and analyze the variety of instances differentiating various TSP solvers and compare it to instances generated by established approaches from the literature.","lang":"eng"}],"date_created":"2023-11-14T15:58:55Z","publisher":"Association for Computing Machinery","title":"Exploring the Feature Space of TSP Instances Using Quality Diversity","year":"2022","department":[{"_id":"819"}],"user_id":"102979","series_title":"GECCO ’22","_id":"48861","extern":"1","type":"conference","status":"public","author":[{"full_name":"Bossek, Jakob","id":"102979","orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob"},{"last_name":"Neumann","full_name":"Neumann, Frank","first_name":"Frank"}],"date_updated":"2023-12-13T10:45:56Z","doi":"10.1145/3512290.3528851","publication_identifier":{"isbn":["978-1-4503-9237-2"]},"publication_status":"published","page":"186–194","citation":{"mla":"Bossek, Jakob, and Frank Neumann. “Exploring the Feature Space of TSP Instances Using Quality Diversity.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2022, pp. 186–194, doi:<a href=\"https://doi.org/10.1145/3512290.3528851\">10.1145/3512290.3528851</a>.","short":"J. Bossek, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2022, pp. 186–194.","bibtex":"@inproceedings{Bossek_Neumann_2022, place={New York, NY, USA}, series={GECCO ’22}, title={Exploring the Feature Space of TSP Instances Using Quality Diversity}, DOI={<a href=\"https://doi.org/10.1145/3512290.3528851\">10.1145/3512290.3528851</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Neumann, Frank}, year={2022}, pages={186–194}, collection={GECCO ’22} }","apa":"Bossek, J., &#38; Neumann, F. (2022). Exploring the Feature Space of TSP Instances Using Quality Diversity. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 186–194. <a href=\"https://doi.org/10.1145/3512290.3528851\">https://doi.org/10.1145/3512290.3528851</a>","chicago":"Bossek, Jakob, and Frank Neumann. “Exploring the Feature Space of TSP Instances Using Quality Diversity.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 186–194. GECCO ’22. New York, NY, USA: Association for Computing Machinery, 2022. <a href=\"https://doi.org/10.1145/3512290.3528851\">https://doi.org/10.1145/3512290.3528851</a>.","ieee":"J. Bossek and F. Neumann, “Exploring the Feature Space of TSP Instances Using Quality Diversity,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2022, pp. 186–194, doi: <a href=\"https://doi.org/10.1145/3512290.3528851\">10.1145/3512290.3528851</a>.","ama":"Bossek J, Neumann F. Exploring the Feature Space of TSP Instances Using Quality Diversity. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO ’22. Association for Computing Machinery; 2022:186–194. doi:<a href=\"https://doi.org/10.1145/3512290.3528851\">10.1145/3512290.3528851</a>"},"place":"New York, NY, USA"},{"_id":"48868","series_title":"GECCO’22","user_id":"102979","department":[{"_id":"819"}],"language":[{"iso":"eng"}],"extern":"1","type":"conference","publication":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","status":"public","date_updated":"2023-12-13T10:46:19Z","publisher":"Association for Computing Machinery","date_created":"2023-11-14T15:58:56Z","author":[{"full_name":"Bossek, Jakob","id":"102979","orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob"},{"full_name":"Neumann, Aneta","last_name":"Neumann","first_name":"Aneta"},{"first_name":"Frank","full_name":"Neumann, Frank","last_name":"Neumann"}],"title":"Evolutionary Diversity Optimization for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA","doi":"10.1145/3520304.3533626","publication_status":"published","publication_identifier":{"isbn":["978-1-4503-9268-6"]},"year":"2022","place":"New York, NY, USA","citation":{"short":"J. Bossek, A. Neumann, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, New York, NY, USA, 2022, pp. 824–842.","bibtex":"@inproceedings{Bossek_Neumann_Neumann_2022, place={New York, NY, USA}, series={GECCO’22}, title={Evolutionary Diversity Optimization for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA}, DOI={<a href=\"https://doi.org/10.1145/3520304.3533626\">10.1145/3520304.3533626</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference Companion}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Neumann, Aneta and Neumann, Frank}, year={2022}, pages={824–842}, collection={GECCO’22} }","mla":"Bossek, Jakob, et al. “Evolutionary Diversity Optimization for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA.” <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, Association for Computing Machinery, 2022, pp. 824–842, doi:<a href=\"https://doi.org/10.1145/3520304.3533626\">10.1145/3520304.3533626</a>.","apa":"Bossek, J., Neumann, A., &#38; Neumann, F. (2022). Evolutionary Diversity Optimization for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA. <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, 824–842. <a href=\"https://doi.org/10.1145/3520304.3533626\">https://doi.org/10.1145/3520304.3533626</a>","ama":"Bossek J, Neumann A, Neumann F. Evolutionary Diversity Optimization for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>. GECCO’22. Association for Computing Machinery; 2022:824–842. doi:<a href=\"https://doi.org/10.1145/3520304.3533626\">10.1145/3520304.3533626</a>","chicago":"Bossek, Jakob, Aneta Neumann, and Frank Neumann. “Evolutionary Diversity Optimization for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, 824–842. GECCO’22. New York, NY, USA: Association for Computing Machinery, 2022. <a href=\"https://doi.org/10.1145/3520304.3533626\">https://doi.org/10.1145/3520304.3533626</a>.","ieee":"J. Bossek, A. Neumann, and F. Neumann, “Evolutionary Diversity Optimization for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, 2022, pp. 824–842, doi: <a href=\"https://doi.org/10.1145/3520304.3533626\">10.1145/3520304.3533626</a>."},"page":"824–842"},{"page":"192–206","citation":{"chicago":"Heins, Jonathan, Jeroen Rook, Lennart Schäpermeier, Pascal 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 Günter Rudolph, Anna V. Kononova, Hernán Aguirre, Pascal Kerschke, Gabriela Ochoa, and Tea Tusar, 192–206. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2022. <a href=\"https://doi.org/10.1007/978-3-031-14714-2_14\">https://doi.org/10.1007/978-3-031-14714-2_14</a>.","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, doi: <a href=\"https://doi.org/10.1007/978-3-031-14714-2_14\">10.1007/978-3-031-14714-2_14</a>.","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 AV, Aguirre H, Kerschke P, Ochoa G, Tusar T, eds. <i>Parallel Problem Solving from Nature (PPSN XVII)</i>. Lecture Notes in Computer Science. Springer International Publishing; 2022:192–206. doi:<a href=\"https://doi.org/10.1007/978-3-031-14714-2_14\">10.1007/978-3-031-14714-2_14</a>","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. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, &#38; T. Tusar (Eds.), <i>Parallel Problem Solving from Nature (PPSN XVII)</i> (pp. 192–206). Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-031-14714-2_14\">https://doi.org/10.1007/978-3-031-14714-2_14</a>","bibtex":"@inproceedings{Heins_Rook_Schäpermeier_Kerschke_Bossek_Trautmann_2022, place={Cham}, series={Lecture Notes in Computer Science}, title={BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems}, DOI={<a href=\"https://doi.org/10.1007/978-3-031-14714-2_14\">10.1007/978-3-031-14714-2_14</a>}, booktitle={Parallel Problem Solving from Nature (PPSN XVII)}, publisher={Springer International Publishing}, author={Heins, Jonathan and Rook, Jeroen and Schäpermeier, Lennart and Kerschke, Pascal and Bossek, Jakob and Trautmann, Heike}, editor={Rudolph, Günter and Kononova, Anna V. and Aguirre, Hernán and Kerschke, Pascal and Ochoa, Gabriela and Tusar, Tea}, year={2022}, pages={192–206}, collection={Lecture Notes in Computer Science} }","short":"J. Heins, J. Rook, L. Schäpermeier, P. Kerschke, J. Bossek, H. Trautmann, in: G. Rudolph, A.V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, T. Tusar (Eds.), Parallel Problem Solving from Nature (PPSN XVII), Springer International Publishing, Cham, 2022, pp. 192–206.","mla":"Heins, Jonathan, et al. “BBE: Basin-Based Evaluation of Multimodal Multi-Objective Optimization Problems.” <i>Parallel Problem Solving from Nature (PPSN XVII)</i>, edited by Günter Rudolph et al., Springer International Publishing, 2022, pp. 192–206, doi:<a href=\"https://doi.org/10.1007/978-3-031-14714-2_14\">10.1007/978-3-031-14714-2_14</a>."},"place":"Cham","publication_identifier":{"isbn":["978-3-031-14714-2"]},"doi":"10.1007/978-3-031-14714-2_14","author":[{"full_name":"Heins, Jonathan","last_name":"Heins","first_name":"Jonathan"},{"first_name":"Jeroen","full_name":"Rook, Jeroen","last_name":"Rook"},{"last_name":"Schäpermeier","full_name":"Schäpermeier, Lennart","first_name":"Lennart"},{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"},{"last_name":"Bossek","orcid":"0000-0002-4121-4668","full_name":"Bossek, Jakob","id":"102979","first_name":"Jakob"},{"full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike"}],"date_updated":"2023-12-13T10:47:50Z","status":"public","editor":[{"full_name":"Rudolph, Günter","last_name":"Rudolph","first_name":"Günter"},{"last_name":"Kononova","full_name":"Kononova, Anna V.","first_name":"Anna V."},{"first_name":"Hernán","full_name":"Aguirre, Hernán","last_name":"Aguirre"},{"first_name":"Pascal","full_name":"Kerschke, Pascal","last_name":"Kerschke"},{"first_name":"Gabriela","last_name":"Ochoa","full_name":"Ochoa, Gabriela"},{"first_name":"Tea","last_name":"Tusar","full_name":"Tusar, Tea"}],"type":"conference","extern":"1","department":[{"_id":"819"}],"user_id":"102979","series_title":"Lecture Notes in Computer Science","_id":"48882","year":"2022","title":"BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems","date_created":"2023-11-14T15:58:58Z","publisher":"Springer International Publishing","abstract":[{"lang":"eng","text":"In multimodal multi-objective optimization (MMMOO), the focus is not solely on convergence in objective space, but rather also on explicitly ensuring diversity in decision space. We illustrate why commonly used diversity measures are not entirely appropriate for this task and propose a sophisticated basin-based evaluation (BBE) method. Also, BBE variants are developed, capturing the anytime behavior of algorithms. The set of BBE measures is tested by means of an algorithm configuration study. We show that these new measures also transfer properties of the well-established hypervolume (HV) indicator to the domain of MMMOO, thus also accounting for objective space convergence. Moreover, we advance MMMOO research by providing insights into the multimodal performance of the considered algorithms. Specifically, algorithms exploiting local structures are shown to outperform classical evolutionary multi-objective optimizers regarding the BBE variants and respective trade-off with HV."}],"publication":"Parallel Problem Solving from Nature (PPSN XVII)","language":[{"iso":"eng"}],"keyword":["Anytime behavior","Benchmarking","Continuous optimization","Multi-objective optimization","Multimodality","Performance metric"]},{"place":"Cham","citation":{"short":"A. Nikfarjam, A. Neumann, J. Bossek, F. Neumann, in: G. Rudolph, A.V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, T. Tu\\v sar (Eds.), Parallel Problem Solving from Nature (PPSN XVII), Springer International Publishing, Cham, 2022, pp. 237–249.","bibtex":"@inproceedings{Nikfarjam_Neumann_Bossek_Neumann_2022, place={Cham}, series={Lecture Notes in Computer Science}, title={Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem}, DOI={<a href=\"https://doi.org/10.1007/978-3-031-14714-2_17\">10.1007/978-3-031-14714-2_17</a>}, booktitle={Parallel Problem Solving from Nature (PPSN XVII)}, publisher={Springer International Publishing}, author={Nikfarjam, Adel and Neumann, Aneta and Bossek, Jakob and Neumann, Frank}, editor={Rudolph, Günter and Kononova, Anna V. and Aguirre, Hernán and Kerschke, Pascal and Ochoa, Gabriela and Tu\\v sar, Tea}, year={2022}, pages={237–249}, collection={Lecture Notes in Computer Science} }","mla":"Nikfarjam, Adel, et al. “Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem.” <i>Parallel Problem Solving from Nature (PPSN XVII)</i>, edited by Günter Rudolph et al., Springer International Publishing, 2022, pp. 237–249, doi:<a href=\"https://doi.org/10.1007/978-3-031-14714-2_17\">10.1007/978-3-031-14714-2_17</a>.","apa":"Nikfarjam, A., Neumann, A., Bossek, J., &#38; Neumann, F. (2022). Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem. In G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, &#38; T. Tu\\v sar (Eds.), <i>Parallel Problem Solving from Nature (PPSN XVII)</i> (pp. 237–249). Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-031-14714-2_17\">https://doi.org/10.1007/978-3-031-14714-2_17</a>","ieee":"A. Nikfarjam, A. Neumann, J. Bossek, and F. Neumann, “Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem,” in <i>Parallel Problem Solving from Nature (PPSN XVII)</i>, 2022, pp. 237–249, doi: <a href=\"https://doi.org/10.1007/978-3-031-14714-2_17\">10.1007/978-3-031-14714-2_17</a>.","chicago":"Nikfarjam, Adel, Aneta Neumann, Jakob Bossek, and Frank Neumann. “Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem.” 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 Tu\\v sar, 237–249. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2022. <a href=\"https://doi.org/10.1007/978-3-031-14714-2_17\">https://doi.org/10.1007/978-3-031-14714-2_17</a>.","ama":"Nikfarjam A, Neumann A, Bossek J, Neumann F. Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem. In: Rudolph G, Kononova AV, Aguirre H, Kerschke P, Ochoa G, Tu\\v sar T, eds. <i>Parallel Problem Solving from Nature (PPSN XVII)</i>. Lecture Notes in Computer Science. Springer International Publishing; 2022:237–249. doi:<a href=\"https://doi.org/10.1007/978-3-031-14714-2_17\">10.1007/978-3-031-14714-2_17</a>"},"page":"237–249","publication_status":"published","publication_identifier":{"isbn":["978-3-031-14714-2"]},"doi":"10.1007/978-3-031-14714-2_17","date_updated":"2023-12-13T10:49:51Z","author":[{"last_name":"Nikfarjam","full_name":"Nikfarjam, Adel","first_name":"Adel"},{"first_name":"Aneta","last_name":"Neumann","full_name":"Neumann, Aneta"},{"first_name":"Jakob","full_name":"Bossek, Jakob","id":"102979","last_name":"Bossek","orcid":"0000-0002-4121-4668"},{"full_name":"Neumann, Frank","last_name":"Neumann","first_name":"Frank"}],"editor":[{"last_name":"Rudolph","full_name":"Rudolph, Günter","first_name":"Günter"},{"full_name":"Kononova, Anna V.","last_name":"Kononova","first_name":"Anna V."},{"first_name":"Hernán","last_name":"Aguirre","full_name":"Aguirre, Hernán"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"last_name":"Ochoa","full_name":"Ochoa, Gabriela","first_name":"Gabriela"},{"full_name":"Tu\\v sar, Tea","last_name":"Tu\\v sar","first_name":"Tea"}],"status":"public","type":"conference","extern":"1","_id":"48894","series_title":"Lecture Notes in Computer Science","user_id":"102979","department":[{"_id":"819"}],"year":"2022","title":"Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem","publisher":"Springer International Publishing","date_created":"2023-11-14T15:59:00Z","abstract":[{"text":"Recently different evolutionary computation approaches have been developed that generate sets of high quality diverse solutions for a given optimisation problem. Many studies have considered diversity 1) as a mean to explore niches in behavioural space (quality diversity) or 2) to increase the structural differences of solutions (evolutionary diversity optimisation). In this study, we introduce a co-evolutionary algorithm to simultaneously explore the two spaces for the multi-component traveling thief problem. The results show the capability of the co-evolutionary algorithm to achieve significantly higher diversity compared to the baseline evolutionary diversity algorithms from the literature.","lang":"eng"}],"publication":"Parallel Problem Solving from Nature (PPSN XVII)","keyword":["Co-evolutionary algorithms","Evolutionary diversity optimisation","Quality diversity","Traveling thief problem"],"language":[{"iso":"eng"}]},{"publication":"Applied Sciences","type":"journal_article","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\\textemdash 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"}],"department":[{"_id":"819"}],"user_id":"102979","_id":"48878","language":[{"iso":"eng"}],"keyword":["big data","data mining","data stream analysis","machine learning","stream classification","supervised learning"],"issue":"18","publication_identifier":{"issn":["2076-3417"]},"page":"9094","intvolume":"        12","citation":{"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>(18), 9094. <a href=\"https://doi.org/10.3390/app12189094\">https://doi.org/10.3390/app12189094</a>","short":"L. Clever, J.S. Pohl, J. Bossek, P. Kerschke, H. Trautmann, Applied Sciences 12 (2022) 9094.","mla":"Clever, Lena, et al. “Process-Oriented Stream Classification Pipeline: A Literature Review.” <i>Applied Sciences</i>, vol. 12, no. 18, {Multidisciplinary Digital Publishing Institute}, 2022, p. 9094, doi:<a href=\"https://doi.org/10.3390/app12189094\">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={18}, journal={Applied Sciences}, publisher={{Multidisciplinary Digital Publishing Institute}}, author={Clever, Lena and Pohl, Janina Susanne and Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}, year={2022}, pages={9094} }","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. 18 (2022): 9094. <a href=\"https://doi.org/10.3390/app12189094\">https://doi.org/10.3390/app12189094</a>.","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. 18, p. 9094, 2022, doi: <a href=\"https://doi.org/10.3390/app12189094\">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(18):9094. doi:<a href=\"https://doi.org/10.3390/app12189094\">10.3390/app12189094</a>"},"year":"2022","volume":12,"author":[{"full_name":"Clever, Lena","last_name":"Clever","first_name":"Lena"},{"first_name":"Janina Susanne","last_name":"Pohl","full_name":"Pohl, Janina Susanne"},{"orcid":"0000-0002-4121-4668","last_name":"Bossek","id":"102979","full_name":"Bossek, Jakob","first_name":"Jakob"},{"first_name":"Pascal","full_name":"Kerschke, Pascal","last_name":"Kerschke"},{"first_name":"Heike","last_name":"Trautmann","full_name":"Trautmann, Heike"}],"date_created":"2023-11-14T15:58:57Z","publisher":"{Multidisciplinary Digital Publishing Institute}","date_updated":"2023-12-13T10:50:56Z","doi":"10.3390/app12189094","title":"Process-Oriented Stream Classification Pipeline: A Literature Review"},{"publisher":"Association for Computing Machinery","date_updated":"2023-12-13T10:50:24Z","author":[{"first_name":"Jeroen","last_name":"Rook","full_name":"Rook, Jeroen"},{"last_name":"Trautmann","full_name":"Trautmann, Heike","first_name":"Heike"},{"first_name":"Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","full_name":"Bossek, Jakob","id":"102979"},{"full_name":"Grimme, Christian","last_name":"Grimme","first_name":"Christian"}],"date_created":"2023-11-14T15:59:00Z","title":"On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems","doi":"10.1145/3520304.3528998","publication_identifier":{"isbn":["978-1-4503-9268-6"]},"place":"New York, NY, USA","year":"2022","page":"356–359","citation":{"ieee":"J. Rook, H. Trautmann, J. Bossek, and C. Grimme, “On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, 2022, pp. 356–359, doi: <a href=\"https://doi.org/10.1145/3520304.3528998\">10.1145/3520304.3528998</a>.","chicago":"Rook, Jeroen, Heike Trautmann, Jakob Bossek, and Christian Grimme. “On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, 356–359. GECCO’22. New York, NY, USA: Association for Computing Machinery, 2022. <a href=\"https://doi.org/10.1145/3520304.3528998\">https://doi.org/10.1145/3520304.3528998</a>.","ama":"Rook J, Trautmann H, Bossek J, Grimme C. On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>. GECCO’22. Association for Computing Machinery; 2022:356–359. doi:<a href=\"https://doi.org/10.1145/3520304.3528998\">10.1145/3520304.3528998</a>","mla":"Rook, Jeroen, et al. “On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems.” <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, Association for Computing Machinery, 2022, pp. 356–359, doi:<a href=\"https://doi.org/10.1145/3520304.3528998\">10.1145/3520304.3528998</a>.","short":"J. Rook, H. Trautmann, J. Bossek, C. Grimme, in: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, New York, NY, USA, 2022, pp. 356–359.","bibtex":"@inproceedings{Rook_Trautmann_Bossek_Grimme_2022, place={New York, NY, USA}, series={GECCO’22}, title={On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems}, DOI={<a href=\"https://doi.org/10.1145/3520304.3528998\">10.1145/3520304.3528998</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference Companion}, publisher={Association for Computing Machinery}, author={Rook, Jeroen and Trautmann, Heike and Bossek, Jakob and Grimme, Christian}, year={2022}, pages={356–359}, collection={GECCO’22} }","apa":"Rook, J., Trautmann, H., Bossek, J., &#38; Grimme, C. (2022). On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems. <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, 356–359. <a href=\"https://doi.org/10.1145/3520304.3528998\">https://doi.org/10.1145/3520304.3528998</a>"},"_id":"48896","department":[{"_id":"819"}],"series_title":"GECCO’22","user_id":"102979","keyword":["configuration","multi-modality","multi-objective optimization"],"extern":"1","language":[{"iso":"eng"}],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","type":"conference","abstract":[{"lang":"eng","text":"Hardness of Multi-Objective (MO) continuous optimization problems results from an interplay of various problem characteristics, e. g. the degree of multi-modality. We present a benchmark study of classical and diversity focused optimizers on multi-modal MO problems based on automated algorithm configuration. We show the large effect of the latter and investigate the trade-off between convergence in objective space and diversity in decision space."}],"status":"public"},{"issue":"1","year":"2022","citation":{"mla":"Rodrigues, Agatha S., et al. “Estimation of Component Reliability from Superposed Renewal Processes by Means of Latent Variables.” <i>Comput. Stat.</i>, vol. 37, no. 1, 2022, pp. 355–379, doi:<a href=\"https://doi.org/10.1007/S00180-021-01124-0\">10.1007/S00180-021-01124-0</a>.","short":"A.S. Rodrigues, P. Kerschke, C.A.D.B. Pereira, H. Trautmann, C. Wagner, B. Hellingrath, A. Polpo, Comput. Stat. 37 (2022) 355–379.","bibtex":"@article{Rodrigues_Kerschke_Pereira_Trautmann_Wagner_Hellingrath_Polpo_2022, title={Estimation of component reliability from superposed renewal processes by means of latent variables}, volume={37}, DOI={<a href=\"https://doi.org/10.1007/S00180-021-01124-0\">10.1007/S00180-021-01124-0</a>}, number={1}, journal={Comput. Stat.}, author={Rodrigues, Agatha S. and Kerschke, Pascal and Pereira, Carlos Alberto De Bragança and Trautmann, Heike and Wagner, Carolin and Hellingrath, Bernd and Polpo, Adriano}, year={2022}, pages={355–379} }","apa":"Rodrigues, A. S., Kerschke, P., Pereira, C. A. D. B., Trautmann, H., Wagner, C., Hellingrath, B., &#38; Polpo, A. (2022). Estimation of component reliability from superposed renewal processes by means of latent variables. <i>Comput. Stat.</i>, <i>37</i>(1), 355–379. <a href=\"https://doi.org/10.1007/S00180-021-01124-0\">https://doi.org/10.1007/S00180-021-01124-0</a>","ieee":"A. S. Rodrigues <i>et al.</i>, “Estimation of component reliability from superposed renewal processes by means of latent variables,” <i>Comput. Stat.</i>, vol. 37, no. 1, pp. 355–379, 2022, doi: <a href=\"https://doi.org/10.1007/S00180-021-01124-0\">10.1007/S00180-021-01124-0</a>.","chicago":"Rodrigues, Agatha S., Pascal Kerschke, Carlos Alberto De Bragança Pereira, Heike Trautmann, Carolin Wagner, Bernd Hellingrath, and Adriano Polpo. “Estimation of Component Reliability from Superposed Renewal Processes by Means of Latent Variables.” <i>Comput. Stat.</i> 37, no. 1 (2022): 355–379. <a href=\"https://doi.org/10.1007/S00180-021-01124-0\">https://doi.org/10.1007/S00180-021-01124-0</a>.","ama":"Rodrigues AS, Kerschke P, Pereira CADB, et al. Estimation of component reliability from superposed renewal processes by means of latent variables. <i>Comput Stat</i>. 2022;37(1):355–379. doi:<a href=\"https://doi.org/10.1007/S00180-021-01124-0\">10.1007/S00180-021-01124-0</a>"},"page":"355–379","intvolume":"        37","date_updated":"2024-03-13T10:28:01Z","date_created":"2024-03-13T09:59:21Z","author":[{"full_name":"Rodrigues, Agatha S.","last_name":"Rodrigues","first_name":"Agatha S."},{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"},{"first_name":"Carlos Alberto De Bragança","full_name":"Pereira, Carlos Alberto De Bragança","last_name":"Pereira"},{"first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","id":"100740","full_name":"Trautmann, Heike"},{"first_name":"Carolin","last_name":"Wagner","full_name":"Wagner, Carolin"},{"first_name":"Bernd","full_name":"Hellingrath, Bernd","last_name":"Hellingrath"},{"first_name":"Adriano","last_name":"Polpo","full_name":"Polpo, Adriano"}],"volume":37,"title":"Estimation of component reliability from superposed renewal processes by means of latent variables","doi":"10.1007/S00180-021-01124-0","type":"journal_article","publication":"Comput. Stat.","status":"public","_id":"52532","user_id":"15504","department":[{"_id":"819"}],"language":[{"iso":"eng"}]},{"publication_identifier":{"isbn":["9781450392372"]},"year":"2022","place":"New York, NY, USA","citation":{"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>","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>."},"page":"657–665","date_updated":"2024-06-07T07:13:23Z","publisher":"Association for Computing Machinery","author":[{"first_name":"Moritz","id":"105520","full_name":"Seiler, Moritz","last_name":"Seiler"},{"full_name":"Prager, Raphael Patrick","last_name":"Prager","first_name":"Raphael Patrick"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","full_name":"Trautmann, Heike","id":"100740"}],"date_created":"2023-08-04T07:15:59Z","title":"A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes","doi":"10.1145/3512290.3528834","type":"conference","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"}],"status":"public","_id":"46307","user_id":"15504","department":[{"_id":"34"},{"_id":"819"}],"language":[{"iso":"eng"}]},{"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.","full_name":"Kononova, Anna V.","last_name":"Kononova"},{"full_name":"Aguirre, Hernán","last_name":"Aguirre","first_name":"Hernán"},{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"},{"first_name":"Gabriela","full_name":"Ochoa, Gabriela","last_name":"Ochoa"},{"last_name":"Tušar","full_name":"Tušar, Tea","first_name":"Tea"}],"publication":"Parallel Problem Solving from Nature — PPSN XVII","type":"conference","language":[{"iso":"eng"}],"department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","_id":"46304","page":"3–17","citation":{"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.","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>.","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>","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>","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>."},"year":"2022","place":"Cham","publication_identifier":{"isbn":["978-3-031-14714-2"]},"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","date_created":"2023-08-04T07:12:33Z","author":[{"full_name":"Prager, Raphael Patrick","last_name":"Prager","first_name":"Raphael Patrick"},{"id":"105520","full_name":"Seiler, Moritz","last_name":"Seiler","first_name":"Moritz"},{"first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","id":"100740","full_name":"Trautmann, Heike"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"}],"publisher":"Springer International Publishing","date_updated":"2024-06-07T07:13:47Z"},{"status":"public","editor":[{"last_name":"the Advancement of Artificial Intelligence (AAAI) Association","full_name":"the Advancement of Artificial Intelligence (AAAI) Association, for","first_name":"for"}],"abstract":[{"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.","lang":"eng"}],"publication":"Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM)","type":"conference","language":[{"iso":"eng"}],"department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","_id":"46303","page":"1–10","citation":{"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} }","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>.","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>","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>.","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>.","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>"},"year":"2022","place":"Palo Alto, CA, USA","doi":"10.36190/2022.91","title":"Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches","date_created":"2023-08-04T07:11:34Z","author":[{"full_name":"Pohl, Janina Susanne","last_name":"Pohl","first_name":"Janina Susanne"},{"last_name":"Assenmacher","full_name":"Assenmacher, Dennis","first_name":"Dennis"},{"last_name":"Seiler","full_name":"Seiler, Moritz","id":"105520","first_name":"Moritz"},{"last_name":"Trautmann","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike","id":"100740","first_name":"Heike"},{"last_name":"Grimme","full_name":"Grimme, Christian","first_name":"Christian"}],"publisher":"AAAI Press","date_updated":"2024-06-07T07:13:35Z"},{"title":"Process-Oriented Stream Classification Pipeline: A Literature Review","doi":"10.3390/app12189094","date_updated":"2024-06-10T12:02:17Z","volume":12,"author":[{"last_name":"Clever","full_name":"Clever, Lena","first_name":"Lena"},{"last_name":"Pohl","full_name":"Pohl, Janina Susanne","first_name":"Janina Susanne"},{"orcid":"0000-0002-4121-4668","last_name":"Bossek","full_name":"Bossek, Jakob","id":"102979","first_name":"Jakob"},{"first_name":"Pascal","full_name":"Kerschke, Pascal","last_name":"Kerschke"},{"id":"100740","full_name":"Trautmann, Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike"}],"date_created":"2023-08-04T07:17:23Z","year":"2022","intvolume":"        12","page":"1–44","citation":{"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.","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} }","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>","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>"},"issue":"8","language":[{"iso":"eng"}],"_id":"46309","department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","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"}],"status":"public","publication":"Applied Sciences","type":"journal_article"},{"_id":"46302","user_id":"15504","department":[{"_id":"34"},{"_id":"819"}],"language":[{"iso":"eng"}],"type":"conference","publication":"Parallel Problem Solving from Nature — PPSN XVII","editor":[{"first_name":"G","full_name":"Rudolph, G","last_name":"Rudolph"},{"first_name":"AV","last_name":"Kononova","full_name":"Kononova, AV"},{"full_name":"Aguirre, H","last_name":"Aguirre","first_name":"H"},{"first_name":"P","full_name":"Kerschke, P","last_name":"Kerschke"},{"first_name":"G","full_name":"Ochoa, G","last_name":"Ochoa"},{"first_name":"T","full_name":"Tušar, T","last_name":"Tušar"}],"status":"public","publisher":"Springer International Publishing","date_updated":"2024-06-10T12:02:35Z","author":[{"first_name":"J","last_name":"Heins","full_name":"Heins, J"},{"full_name":"Rook, J","last_name":"Rook","first_name":"J"},{"first_name":"L","last_name":"Schäpermeier","full_name":"Schäpermeier, L"},{"first_name":"P","last_name":"Kerschke","full_name":"Kerschke, P"},{"last_name":"Bossek","orcid":"0000-0002-4121-4668","id":"102979","full_name":"Bossek, Jakob","first_name":"Jakob"},{"last_name":"Trautmann","orcid":"0000-0002-9788-8282","id":"100740","full_name":"Trautmann, Heike","first_name":"Heike"}],"date_created":"2023-08-04T07:10:52Z","title":"BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems","publication_identifier":{"isbn":["978-3-031-14714-2"]},"place":"Cham","year":"2022","citation":{"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.","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.","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.","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."},"page":"192–206"},{"publication_identifier":{"isbn":["9781450392686"]},"page":"356–359","citation":{"ieee":"J. Rook, H. Trautmann, J. Bossek, and C. Grimme, “On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, 2022, pp. 356–359, doi: <a href=\"https://doi.org/10.1145/3520304.3528998\">10.1145/3520304.3528998</a>.","chicago":"Rook, J, Heike Trautmann, Jakob Bossek, and C Grimme. “On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, edited by J Fieldsend and M. Wagner, 356–359. GECCO ’22. New York, NY, USA: Association for Computing Machinery, 2022. <a href=\"https://doi.org/10.1145/3520304.3528998\">https://doi.org/10.1145/3520304.3528998</a>.","bibtex":"@inproceedings{Rook_Trautmann_Bossek_Grimme_2022, place={New York, NY, USA}, series={GECCO ’22}, title={On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems}, DOI={<a href=\"https://doi.org/10.1145/3520304.3528998\">10.1145/3520304.3528998</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference Companion}, publisher={Association for Computing Machinery}, author={Rook, J and Trautmann, Heike and Bossek, Jakob and Grimme, C}, editor={Fieldsend, J and Wagner, M.}, year={2022}, pages={356–359}, collection={GECCO ’22} }","short":"J. Rook, H. Trautmann, J. Bossek, C. Grimme, in: J. Fieldsend, M. Wagner (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, New York, NY, USA, 2022, pp. 356–359.","mla":"Rook, J., et al. “On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems.” <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, edited by J Fieldsend and M. Wagner, Association for Computing Machinery, 2022, pp. 356–359, doi:<a href=\"https://doi.org/10.1145/3520304.3528998\">10.1145/3520304.3528998</a>.","apa":"Rook, J., Trautmann, H., Bossek, J., &#38; Grimme, C. (2022). On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems. In J. Fieldsend &#38; M. Wagner (Eds.), <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i> (pp. 356–359). Association for Computing Machinery. <a href=\"https://doi.org/10.1145/3520304.3528998\">https://doi.org/10.1145/3520304.3528998</a>","ama":"Rook J, Trautmann H, Bossek J, Grimme C. On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems. In: Fieldsend J, Wagner M, eds. <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>. GECCO ’22. Association for Computing Machinery; 2022:356–359. doi:<a href=\"https://doi.org/10.1145/3520304.3528998\">10.1145/3520304.3528998</a>"},"place":"New York, NY, USA","author":[{"first_name":"J","last_name":"Rook","full_name":"Rook, J"},{"first_name":"Heike","full_name":"Trautmann, Heike","id":"100740","orcid":"0000-0002-9788-8282","last_name":"Trautmann"},{"first_name":"Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","id":"102979","full_name":"Bossek, Jakob"},{"first_name":"C","full_name":"Grimme, C","last_name":"Grimme"}],"date_updated":"2026-02-19T15:12:35Z","doi":"10.1145/3520304.3528998","type":"conference","status":"public","editor":[{"first_name":"J","last_name":"Fieldsend","full_name":"Fieldsend, J"},{"last_name":"Wagner","full_name":"Wagner, M.","first_name":"M."}],"department":[{"_id":"34"},{"_id":"819"}],"user_id":"14972","series_title":"GECCO ’22","_id":"46305","year":"2022","date_created":"2023-08-04T07:14:24Z","publisher":"Association for Computing Machinery","title":"On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems","publication":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","abstract":[{"lang":"eng","text":"Hardness of Multi-Objective (MO) continuous optimization problems results from an interplay of various problem characteristics, e. g. the degree of multi-modality. We present a benchmark study of classical and diversity focused optimizers on multi-modal MO problems based on automated algorithm configuration. We show the large effect of the latter and investigate the trade-off between convergence in objective space and diversity in decision space."}],"language":[{"iso":"eng"}]},{"user_id":"15504","department":[{"_id":"34"},{"_id":"819"}],"_id":"46318","language":[{"iso":"eng"}],"keyword":["Multimodal optimization","Multi-objective continuous optimization","Landscape analysis","Visualization","Benchmarking","Theory","Algorithms"],"type":"journal_article","publication":"Computers & Operations Research","status":"public","abstract":[{"text":"Multi-objective (MO) optimization, i.e., the simultaneous optimization of multiple conflicting objectives, is gaining more and more attention in various research areas, such as evolutionary computation, machine learning (e.g., (hyper-)parameter optimization), or logistics (e.g., vehicle routing). Many works in this domain mention the structural problem property of multimodality as a challenge from two classical perspectives: (1) finding all globally optimal solution sets, and (2) avoiding to get trapped in local optima. Interestingly, these streams seem to transfer many traditional concepts of single-objective (SO) optimization into claims, assumptions, or even terminology regarding the MO domain, but mostly neglect the understanding of the structural properties as well as the algorithmic search behavior on a problem’s landscape. However, some recent works counteract this trend, by investigating the fundamentals and characteristics of MO problems using new visualization techniques and gaining surprising insights. Using these visual insights, this work proposes a step towards a unified terminology to capture multimodality and locality in a broader way than it is usually done. This enables us to investigate current research activities in multimodal continuous MO optimization and to highlight new implications and promising research directions for the design of benchmark suites, the discovery of MO landscape features, the development of new MO (or even SO) optimization algorithms, and performance indicators. For all these topics, we provide a review of ideas and methods but also an outlook on future challenges, research potential and perspectives that result from recent developments.","lang":"eng"}],"date_created":"2023-08-04T07:28:34Z","author":[{"first_name":"Christian","full_name":"Grimme, Christian","last_name":"Grimme"},{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"},{"first_name":"Pelin","last_name":"Aspar","full_name":"Aspar, Pelin"},{"id":"100740","full_name":"Trautmann, Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","first_name":"Heike"},{"full_name":"Preuss, Mike","last_name":"Preuss","first_name":"Mike"},{"full_name":"Deutz, André H.","last_name":"Deutz","first_name":"André H."},{"full_name":"Wang, Hao","last_name":"Wang","first_name":"Hao"},{"first_name":"Michael","full_name":"Emmerich, Michael","last_name":"Emmerich"}],"volume":136,"date_updated":"2023-10-16T12:58:42Z","doi":"https://doi.org/10.1016/j.cor.2021.105489","title":"Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization","publication_identifier":{"issn":["0305-0548"]},"citation":{"ieee":"C. Grimme <i>et al.</i>, “Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization,” <i>Computers &#38; Operations Research</i>, vol. 136, p. 105489, 2021, doi: <a href=\"https://doi.org/10.1016/j.cor.2021.105489\">https://doi.org/10.1016/j.cor.2021.105489</a>.","chicago":"Grimme, Christian, Pascal Kerschke, Pelin Aspar, Heike Trautmann, Mike Preuss, André H. Deutz, Hao Wang, and Michael Emmerich. “Peeking beyond Peaks: Challenges and Research Potentials of Continuous Multimodal Multi-Objective Optimization.” <i>Computers &#38; Operations Research</i> 136 (2021): 105489. <a href=\"https://doi.org/10.1016/j.cor.2021.105489\">https://doi.org/10.1016/j.cor.2021.105489</a>.","ama":"Grimme C, Kerschke P, Aspar P, et al. Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization. <i>Computers &#38; Operations Research</i>. 2021;136:105489. doi:<a href=\"https://doi.org/10.1016/j.cor.2021.105489\">https://doi.org/10.1016/j.cor.2021.105489</a>","mla":"Grimme, Christian, et al. “Peeking beyond Peaks: Challenges and Research Potentials of Continuous Multimodal Multi-Objective Optimization.” <i>Computers &#38; Operations Research</i>, vol. 136, 2021, p. 105489, doi:<a href=\"https://doi.org/10.1016/j.cor.2021.105489\">https://doi.org/10.1016/j.cor.2021.105489</a>.","short":"C. Grimme, P. Kerschke, P. Aspar, H. Trautmann, M. Preuss, A.H. Deutz, H. Wang, M. Emmerich, Computers &#38; Operations Research 136 (2021) 105489.","bibtex":"@article{Grimme_Kerschke_Aspar_Trautmann_Preuss_Deutz_Wang_Emmerich_2021, title={Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization}, volume={136}, DOI={<a href=\"https://doi.org/10.1016/j.cor.2021.105489\">https://doi.org/10.1016/j.cor.2021.105489</a>}, journal={Computers &#38; Operations Research}, author={Grimme, Christian and Kerschke, Pascal and Aspar, Pelin and Trautmann, Heike and Preuss, Mike and Deutz, André H. and Wang, Hao and Emmerich, Michael}, year={2021}, pages={105489} }","apa":"Grimme, C., Kerschke, P., Aspar, P., Trautmann, H., Preuss, M., Deutz, A. H., Wang, H., &#38; Emmerich, M. (2021). Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization. <i>Computers &#38; Operations Research</i>, <i>136</i>, 105489. <a href=\"https://doi.org/10.1016/j.cor.2021.105489\">https://doi.org/10.1016/j.cor.2021.105489</a>"},"intvolume":"       136","page":"105489","year":"2021"},{"publication":"Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings","type":"conference","abstract":[{"text":"In this work we examine the inner mechanisms of the recently developed sophisticated local search procedure SOMOGSA. This method solves multimodal single-objective continuous optimization problems by first expanding the problem with an additional objective (e.g., a sphere function) to the bi-objective space, and subsequently exploiting local structures and ridges of the resulting landscapes. Our study particularly focusses on the sensitivity of this multiobjectivization approach w.r.t. (i) the parametrization of the artificial second objective, as well as (ii) the position of the initial starting points in the search space.\r\n\r\nAs SOMOGSA is a modular framework for encapsulating local search, we integrate Gradient and Nelder-Mead local search (as optimizers in the respective module) and compare the performance of the resulting hybrid local search to their original single-objective counterparts. We show that the SOMOGSA framework can significantly boost local search by multiobjectivization. Combined with more sophisticated local search and metaheuristics this may help in solving highly multimodal optimization problems in future.","lang":"eng"}],"editor":[{"last_name":"et al. Ishibuchi","full_name":"et al. Ishibuchi, H.","first_name":"H."}],"status":"public","_id":"46311","department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","language":[{"iso":"eng"}],"place":"Heidelberg, Berlin","year":"2021","page":"311–322","citation":{"ieee":"P. Aspar, P. Kerschke, V. Steinhoff, H. Trautmann, and C. Grimme, “Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective Space by Means of Multiobjectivization,” in <i>Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings</i>, 2021, pp. 311–322, doi: <a href=\"https://doi.org/10.1007/978-3-030-72062-9_25\">10.1007/978-3-030-72062-9_25</a>.","chicago":"Aspar, Pelin, Pascal Kerschke, Vera Steinhoff, Heike Trautmann, and Christian Grimme. “Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective Space by Means of Multiobjectivization.” In <i>Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings</i>, edited by H. et al. Ishibuchi, 311–322. Heidelberg, Berlin: Springer, 2021. <a href=\"https://doi.org/10.1007/978-3-030-72062-9_25\">https://doi.org/10.1007/978-3-030-72062-9_25</a>.","ama":"Aspar P, Kerschke P, Steinhoff V, Trautmann H, Grimme C. Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective Space by Means of Multiobjectivization. In: et al. Ishibuchi H, ed. <i>Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings</i>. Springer; 2021:311–322. doi:<a href=\"https://doi.org/10.1007/978-3-030-72062-9_25\">10.1007/978-3-030-72062-9_25</a>","mla":"Aspar, Pelin, et al. “Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective Space by Means of Multiobjectivization.” <i>Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings</i>, edited by H. et al. Ishibuchi, Springer, 2021, pp. 311–322, doi:<a href=\"https://doi.org/10.1007/978-3-030-72062-9_25\">10.1007/978-3-030-72062-9_25</a>.","short":"P. Aspar, P. Kerschke, V. Steinhoff, H. Trautmann, C. Grimme, in: H. et al. Ishibuchi (Ed.), Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings, Springer, Heidelberg, Berlin, 2021, pp. 311–322.","bibtex":"@inproceedings{Aspar_Kerschke_Steinhoff_Trautmann_Grimme_2021, place={Heidelberg, Berlin}, title={Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective Space by Means of Multiobjectivization}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-72062-9_25\">10.1007/978-3-030-72062-9_25</a>}, booktitle={Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings}, publisher={Springer}, author={Aspar, Pelin and Kerschke, Pascal and Steinhoff, Vera and Trautmann, Heike and Grimme, Christian}, editor={et al. Ishibuchi, H.}, year={2021}, pages={311–322} }","apa":"Aspar, P., Kerschke, P., Steinhoff, V., Trautmann, H., &#38; Grimme, C. (2021). Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective Space by Means of Multiobjectivization. In H. et al. Ishibuchi (Ed.), <i>Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings</i> (pp. 311–322). Springer. <a href=\"https://doi.org/10.1007/978-3-030-72062-9_25\">https://doi.org/10.1007/978-3-030-72062-9_25</a>"},"date_updated":"2023-10-16T12:54:29Z","publisher":"Springer","author":[{"full_name":"Aspar, Pelin","last_name":"Aspar","first_name":"Pelin"},{"first_name":"Pascal","full_name":"Kerschke, Pascal","last_name":"Kerschke"},{"first_name":"Vera","last_name":"Steinhoff","full_name":"Steinhoff, Vera"},{"first_name":"Heike","id":"100740","full_name":"Trautmann, Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann"},{"first_name":"Christian","full_name":"Grimme, Christian","last_name":"Grimme"}],"date_created":"2023-08-04T07:21:17Z","title":"Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective Space by Means of Multiobjectivization","doi":"10.1007/978-3-030-72062-9_25"}]