[{"editor":[{"first_name":"T","last_name":"et al. Tran","full_name":"et al. Tran, T"}],"doi":"10.1007/978-3-031-21743-2_1","user_id":"15504","_id":"46301","language":[{"iso":"eng"}],"publisher":"Springer International Publishing","page":"3–16","date_updated":"2023-10-16T12:35:22Z","author":[{"full_name":"Assenmacher, D","first_name":"D","last_name":"Assenmacher"},{"id":"100740","full_name":"Trautmann, Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike"}],"title":"Textual One-Pass Stream Clustering with Automated Distance Threshold Adaption","year":"2022","status":"public","department":[{"_id":"819"},{"_id":"34"}],"type":"conference","place":"Cham","date_created":"2023-08-04T07:04:54Z","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>.","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>","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>.","short":"D. Assenmacher, H. Trautmann, in: T. et al. Tran (Ed.), Intelligent Information and Database Systems, Springer International Publishing, Cham, 2022, pp. 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>.","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} }","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>"},"publication":"Intelligent Information and Database Systems"},{"date_created":"2023-08-04T07:26:36Z","type":"journal_article","department":[{"_id":"34"},{"_id":"819"}],"publication":"Social Science Computer Review","issue":"6","citation":{"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>.","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>","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>.","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.","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>.","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} }","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>"},"abstract":[{"lang":"eng","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. "}],"page":"1496-1522","_id":"46316","language":[{"iso":"eng"}],"doi":"10.1177/08944393211012268","user_id":"15504","volume":40,"title":"Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem","year":"2022","status":"public","author":[{"last_name":"Assenmacher","first_name":"Dennis","full_name":"Assenmacher, Dennis"},{"first_name":"Derek","last_name":"Weber","full_name":"Weber, Derek"},{"first_name":"Mike","last_name":"Preuss","full_name":"Preuss, Mike"},{"full_name":"Valdez, André Calero","last_name":"Valdez","first_name":"André Calero"},{"first_name":"Alison","last_name":"Bradshaw","full_name":"Bradshaw, Alison"},{"first_name":"Björn","last_name":"Ross","full_name":"Ross, Björn"},{"last_name":"Cresci","first_name":"Stefano","full_name":"Cresci, Stefano"},{"id":"100740","first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike"},{"last_name":"Neumann","first_name":"Frank","full_name":"Neumann, Frank"},{"first_name":"Christian","last_name":"Grimme","full_name":"Grimme, Christian"}],"date_updated":"2023-10-16T12:57:24Z","intvolume":"        40"},{"language":[{"iso":"eng"}],"doi":"10.1007/978-3-031-14714-2_40","title":"HPO x ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis","year":"2022","publication_identifier":{"isbn":["978-3-031-14714-2"]},"author":[{"full_name":"Schneider, Lennart","first_name":"Lennart","last_name":"Schneider"},{"last_name":"Schäpermeier","first_name":"Lennart","full_name":"Schäpermeier, Lennart"},{"last_name":"Prager","first_name":"Raphael Patrick","full_name":"Prager, Raphael Patrick"},{"first_name":"Bernd","last_name":"Bischl","full_name":"Bischl, Bernd"},{"id":"100740","last_name":"Trautmann","first_name":"Heike","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike"},{"full_name":"Kerschke, Pascal","first_name":"Pascal","last_name":"Kerschke"}],"date_updated":"2023-10-16T12:51:27Z","date_created":"2023-08-04T07:15:16Z","type":"conference","department":[{"_id":"34"},{"_id":"819"}],"publication":"Parallel Problem Solving from Nature — PPSN XVII","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"}],"page":"575–589","_id":"46306","publisher":"Springer International Publishing","user_id":"15504","editor":[{"full_name":"Rudolph, Günter","first_name":"Günter","last_name":"Rudolph"},{"last_name":"Kononova","first_name":"Anna V.","full_name":"Kononova, Anna V."},{"full_name":"Aguirre, Hernán","first_name":"Hernán","last_name":"Aguirre"},{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"},{"last_name":"Ochoa","first_name":"Gabriela","full_name":"Ochoa, Gabriela"},{"last_name":"Tušar","first_name":"Tea","full_name":"Tušar, Tea"}],"status":"public","place":"Cham","citation":{"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>.","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>","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.","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>.","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} }","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>"}},{"author":[{"last_name":"Aspar","first_name":"Pelin","full_name":"Aspar, Pelin"},{"first_name":"Vera","last_name":"Steinhoff","full_name":"Steinhoff, Vera"},{"full_name":"Schäpermeier, Lennart","first_name":"Lennart","last_name":"Schäpermeier"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"id":"100740","last_name":"Trautmann","first_name":"Heike","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike"},{"last_name":"Grimme","first_name":"Christian","full_name":"Grimme, Christian"}],"year":"2022","title":"The objective that freed me: a multi-objective local search approach for continuous single-objective optimization","status":"public","intvolume":"         1","date_updated":"2023-10-16T12:52:33Z","_id":"46308","language":[{"iso":"eng"}],"page":"1–15","volume":1,"doi":"10.1007/s11047-022-09919-w","user_id":"15504","citation":{"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} }","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>","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>.","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.","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>."},"publication":"Natural Computing","abstract":[{"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.","lang":"eng"}],"date_created":"2023-08-04T07:16:40Z","department":[{"_id":"34"},{"_id":"819"}],"type":"journal_article"},{"intvolume":"        37","date_updated":"2024-03-13T10:28:01Z","author":[{"full_name":"Rodrigues, Agatha S.","last_name":"Rodrigues","first_name":"Agatha S."},{"last_name":"Kerschke","first_name":"Pascal","full_name":"Kerschke, Pascal"},{"first_name":"Carlos Alberto De Bragança","last_name":"Pereira","full_name":"Pereira, Carlos Alberto De Bragança"},{"id":"100740","full_name":"Trautmann, Heike","orcid":"0000-0002-9788-8282","first_name":"Heike","last_name":"Trautmann"},{"first_name":"Carolin","last_name":"Wagner","full_name":"Wagner, Carolin"},{"full_name":"Hellingrath, Bernd","first_name":"Bernd","last_name":"Hellingrath"},{"full_name":"Polpo, Adriano","last_name":"Polpo","first_name":"Adriano"}],"year":"2022","status":"public","title":"Estimation of component reliability from superposed renewal processes by means of latent variables","volume":37,"user_id":"15504","doi":"10.1007/S00180-021-01124-0","language":[{"iso":"eng"}],"_id":"52532","page":"355–379","citation":{"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>.","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.","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>.","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>","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} }","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>","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>."},"issue":"1","publication":"Comput. Stat.","department":[{"_id":"819"}],"type":"journal_article","date_created":"2024-03-13T09:59:21Z"},{"date_updated":"2024-06-07T07:13:23Z","status":"public","title":"A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes","year":"2022","publication_identifier":{"isbn":["9781450392372"]},"author":[{"full_name":"Seiler, Moritz","last_name":"Seiler","first_name":"Moritz","id":"105520"},{"full_name":"Prager, Raphael Patrick","first_name":"Raphael Patrick","last_name":"Prager"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"id":"100740","first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike"}],"user_id":"15504","doi":"10.1145/3512290.3528834","page":"657–665","_id":"46307","language":[{"iso":"eng"}],"publisher":"Association for Computing Machinery","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"}],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference","citation":{"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>.","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.","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>.","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>","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} }","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>","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>."},"type":"conference","department":[{"_id":"34"},{"_id":"819"}],"date_created":"2023-08-04T07:15:59Z","place":"New York, NY, USA"},{"type":"conference","department":[{"_id":"34"},{"_id":"819"}],"date_created":"2023-08-04T07:12:33Z","abstract":[{"text":"In recent years, feature-based automated algorithm selection using exploratory landscape analysis has demonstrated its great potential in single-objective continuous black-box optimization. However, feature computation is problem-specific and can be costly in terms of computational resources. This paper investigates feature-free approaches that rely on state-of-the-art deep learning techniques operating on either images or point clouds. We show that point-cloud-based strategies, in particular, are highly competitive and also substantially reduce the size of the required solver portfolio. Moreover, we highlight the effect and importance of cost-sensitive learning in automated algorithm selection models.","lang":"eng"}],"publication":"Parallel Problem Solving from Nature — PPSN XVII","doi":"10.1007/978-3-031-14714-2_1","language":[{"iso":"eng"}],"date_updated":"2024-06-07T07:13:47Z","year":"2022","title":"Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods","author":[{"last_name":"Prager","first_name":"Raphael Patrick","full_name":"Prager, Raphael Patrick"},{"id":"105520","last_name":"Seiler","first_name":"Moritz","full_name":"Seiler, Moritz"},{"last_name":"Trautmann","first_name":"Heike","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike","id":"100740"},{"full_name":"Kerschke, Pascal","first_name":"Pascal","last_name":"Kerschke"}],"publication_identifier":{"isbn":["978-3-031-14714-2"]},"place":"Cham","citation":{"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>.","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.","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>.","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>","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} }","ama":"Prager RP, Seiler M, Trautmann H, Kerschke P. Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods. In: Rudolph G, Kononova AV, Aguirre H, Kerschke P, Ochoa G, Tušar T, eds. <i>Parallel Problem Solving from Nature — PPSN XVII</i>. Springer International Publishing; 2022:3–17. doi:<a href=\"https://doi.org/10.1007/978-3-031-14714-2_1\">10.1007/978-3-031-14714-2_1</a>","mla":"Prager, Raphael Patrick, et al. “Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods.” <i>Parallel Problem Solving from Nature — PPSN XVII</i>, edited by Günter Rudolph et al., Springer International Publishing, 2022, pp. 3–17, doi:<a href=\"https://doi.org/10.1007/978-3-031-14714-2_1\">10.1007/978-3-031-14714-2_1</a>."},"user_id":"15504","editor":[{"full_name":"Rudolph, Günter","first_name":"Günter","last_name":"Rudolph"},{"full_name":"Kononova, Anna V.","first_name":"Anna V.","last_name":"Kononova"},{"first_name":"Hernán","last_name":"Aguirre","full_name":"Aguirre, Hernán"},{"full_name":"Kerschke, Pascal","first_name":"Pascal","last_name":"Kerschke"},{"full_name":"Ochoa, Gabriela","first_name":"Gabriela","last_name":"Ochoa"},{"full_name":"Tušar, Tea","first_name":"Tea","last_name":"Tušar"}],"page":"3–17","_id":"46304","publisher":"Springer International Publishing","status":"public"},{"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)","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.","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>.","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>.","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>","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>."},"type":"conference","department":[{"_id":"34"},{"_id":"819"}],"place":"Palo Alto, CA, USA","date_created":"2023-08-04T07:11:34Z","date_updated":"2024-06-07T07:13:35Z","year":"2022","status":"public","title":"Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches","author":[{"last_name":"Pohl","first_name":"Janina Susanne","full_name":"Pohl, Janina Susanne"},{"first_name":"Dennis","last_name":"Assenmacher","full_name":"Assenmacher, Dennis"},{"id":"105520","last_name":"Seiler","first_name":"Moritz","full_name":"Seiler, Moritz"},{"id":"100740","first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","full_name":"Trautmann, Heike"},{"full_name":"Grimme, Christian","last_name":"Grimme","first_name":"Christian"}],"doi":"10.36190/2022.91","user_id":"15504","editor":[{"last_name":"the Advancement of Artificial Intelligence (AAAI) Association","first_name":"for","full_name":"the Advancement of Artificial Intelligence (AAAI) Association, for"}],"page":"1–10","language":[{"iso":"eng"}],"_id":"46303","publisher":"AAAI Press"},{"date_created":"2023-08-04T07:17:23Z","type":"journal_article","department":[{"_id":"34"},{"_id":"819"}],"publication":"Applied Sciences","issue":"8","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>.","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>","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>.","short":"L. Clever, J.S. Pohl, J. Bossek, P. Kerschke, H. Trautmann, Applied Sciences 12 (2022) 1–44."},"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"}],"page":"1–44","language":[{"iso":"eng"}],"_id":"46309","doi":"10.3390/app12189094","user_id":"15504","volume":12,"status":"public","title":"Process-Oriented Stream Classification Pipeline: A Literature Review","year":"2022","author":[{"full_name":"Clever, Lena","first_name":"Lena","last_name":"Clever"},{"full_name":"Pohl, Janina Susanne","first_name":"Janina Susanne","last_name":"Pohl"},{"id":"102979","full_name":"Bossek, Jakob","first_name":"Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"id":"100740","full_name":"Trautmann, Heike","first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282"}],"date_updated":"2024-06-10T12:02:17Z","intvolume":"        12"},{"publication":"Parallel Problem Solving from Nature — PPSN XVII","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.","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.","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.","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} }","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.","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.","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."},"type":"conference","department":[{"_id":"34"},{"_id":"819"}],"place":"Cham","date_created":"2023-08-04T07:10:52Z","date_updated":"2024-06-10T12:02:35Z","year":"2022","title":"BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems","status":"public","publication_identifier":{"isbn":["978-3-031-14714-2"]},"author":[{"full_name":"Heins, J","last_name":"Heins","first_name":"J"},{"full_name":"Rook, J","last_name":"Rook","first_name":"J"},{"last_name":"Schäpermeier","first_name":"L","full_name":"Schäpermeier, L"},{"full_name":"Kerschke, P","last_name":"Kerschke","first_name":"P"},{"id":"102979","orcid":"0000-0002-4121-4668","first_name":"Jakob","last_name":"Bossek","full_name":"Bossek, Jakob"},{"id":"100740","full_name":"Trautmann, Heike","first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282"}],"user_id":"15504","editor":[{"last_name":"Rudolph","first_name":"G","full_name":"Rudolph, G"},{"last_name":"Kononova","first_name":"AV","full_name":"Kononova, AV"},{"last_name":"Aguirre","first_name":"H","full_name":"Aguirre, H"},{"full_name":"Kerschke, P","last_name":"Kerschke","first_name":"P"},{"first_name":"G","last_name":"Ochoa","full_name":"Ochoa, G"},{"full_name":"Tušar, T","first_name":"T","last_name":"Tušar"}],"page":"192–206","publisher":"Springer International Publishing","_id":"46302","language":[{"iso":"eng"}]},{"series_title":"GECCO ’22","language":[{"iso":"eng"}],"doi":"10.1145/3520304.3528998","title":"On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems","year":"2022","author":[{"full_name":"Rook, J","first_name":"J","last_name":"Rook"},{"id":"100740","full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike","orcid":"0000-0002-9788-8282"},{"first_name":"Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","full_name":"Bossek, Jakob","id":"102979"},{"first_name":"C","last_name":"Grimme","full_name":"Grimme, C"}],"publication_identifier":{"isbn":["9781450392686"]},"date_updated":"2026-02-19T15:12:35Z","date_created":"2023-08-04T07:14:24Z","type":"conference","department":[{"_id":"34"},{"_id":"819"}],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","abstract":[{"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.","lang":"eng"}],"page":"356–359","_id":"46305","publisher":"Association for Computing Machinery","user_id":"14972","editor":[{"last_name":"Fieldsend","first_name":"J","full_name":"Fieldsend, J"},{"last_name":"Wagner","first_name":"M.","full_name":"Wagner, M."}],"status":"public","place":"New York, NY, USA","citation":{"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} }","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>.","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>","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.","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>.","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>","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>."}},{"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"}],"citation":{"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.","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>.","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>","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>.","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>","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} }","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>."},"publication":"Computers & Operations Research","department":[{"_id":"34"},{"_id":"819"}],"type":"journal_article","keyword":["Multimodal optimization","Multi-objective continuous optimization","Landscape analysis","Visualization","Benchmarking","Theory","Algorithms"],"date_created":"2023-08-04T07:28:34Z","intvolume":"       136","date_updated":"2023-10-16T12:58:42Z","author":[{"full_name":"Grimme, Christian","last_name":"Grimme","first_name":"Christian"},{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"},{"full_name":"Aspar, Pelin","last_name":"Aspar","first_name":"Pelin"},{"orcid":"0000-0002-9788-8282","first_name":"Heike","last_name":"Trautmann","full_name":"Trautmann, Heike","id":"100740"},{"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"},{"last_name":"Emmerich","first_name":"Michael","full_name":"Emmerich, Michael"}],"publication_identifier":{"issn":["0305-0548"]},"year":"2021","status":"public","title":"Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization","volume":136,"user_id":"15504","doi":"https://doi.org/10.1016/j.cor.2021.105489","_id":"46318","language":[{"iso":"eng"}],"page":"105489"},{"date_updated":"2023-10-16T12:54:29Z","author":[{"last_name":"Aspar","first_name":"Pelin","full_name":"Aspar, Pelin"},{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"},{"last_name":"Steinhoff","first_name":"Vera","full_name":"Steinhoff, Vera"},{"id":"100740","orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike","full_name":"Trautmann, Heike"},{"first_name":"Christian","last_name":"Grimme","full_name":"Grimme, Christian"}],"title":"Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective Space by Means of Multiobjectivization","status":"public","year":"2021","editor":[{"full_name":"et al. Ishibuchi, H.","first_name":"H.","last_name":"et al. Ishibuchi"}],"doi":"10.1007/978-3-030-72062-9_25","user_id":"15504","language":[{"iso":"eng"}],"_id":"46311","publisher":"Springer","page":"311–322","abstract":[{"lang":"eng","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."}],"citation":{"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>.","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.","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>","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>.","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>","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} }","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>."},"publication":"Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","place":"Heidelberg, Berlin","date_created":"2023-08-04T07:21:17Z"},{"abstract":[{"text":"One of the most significant recent technological developments concerns the development and implementation of ‘intelligent machines’ that draw on recent advances in artificial intelligence (AI) and robotics. However, there are growing tensions between human freedoms and machine controls. This article reports the findings of a workshop that investigated the application of the principles of human freedom throughout intelligent machine development and use. Forty IS researchers from ten different countries discussed four contemporary AI and humanity issues and the most relevant IS domain challenges. This article summarizes their experiences and opinions regarding four AI and humanity themes: Crime & conflict, Jobs, Attention, and Wellbeing. The outcomes of the workshop discussions identify three attributes of humanity that need preservation: a critique of the design and application of AI, and the intelligent machines it can create; human involvement in the loop of intelligent machine decision-making processes; and the ability to interpret and explain intelligent machine decision-making processes. The article provides an agenda for future AI and humanity research.","lang":"eng"}],"citation":{"short":"C. Coombs, P. Stacey, P. Kawalek, B. Simeonova, J. Becker, K. Bergener, J.Á. Carvalho, M. Fantinato, N.F. Garmann-Johnsen, C. Grimme, A. Stein, H. Trautmann, International Journal of Information Management 58 (2021).","chicago":"Coombs, Crispin, Patrick Stacey, Peter Kawalek, Boyka Simeonova, Jörg Becker, Katrin Bergener, João Álvaro Carvalho, et al. “What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective.” <i>International Journal of Information Management</i> 58 (2021). <a href=\"https://doi.org/10.1016/j.ijinfomgt.2021.102311\">https://doi.org/10.1016/j.ijinfomgt.2021.102311</a>.","ieee":"C. Coombs <i>et al.</i>, “What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective,” <i>International Journal of Information Management</i>, vol. 58, 2021, doi: <a href=\"https://doi.org/10.1016/j.ijinfomgt.2021.102311\">10.1016/j.ijinfomgt.2021.102311</a>.","apa":"Coombs, C., Stacey, P., Kawalek, P., Simeonova, B., Becker, J., Bergener, K., Carvalho, J. Á., Fantinato, M., Garmann-Johnsen, N. F., Grimme, C., Stein, A., &#38; Trautmann, H. (2021). What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective. <i>International Journal of Information Management</i>, <i>58</i>. <a href=\"https://doi.org/10.1016/j.ijinfomgt.2021.102311\">https://doi.org/10.1016/j.ijinfomgt.2021.102311</a>","bibtex":"@article{Coombs_Stacey_Kawalek_Simeonova_Becker_Bergener_Carvalho_Fantinato_Garmann-Johnsen_Grimme_et al._2021, title={What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective}, volume={58}, DOI={<a href=\"https://doi.org/10.1016/j.ijinfomgt.2021.102311\">10.1016/j.ijinfomgt.2021.102311</a>}, journal={International Journal of Information Management}, author={Coombs, Crispin and Stacey, Patrick and Kawalek, Peter and Simeonova, Boyka and Becker, Jörg and Bergener, Katrin and Carvalho, João Álvaro and Fantinato, Marcelo and Garmann-Johnsen, Niels F. and Grimme, Christian and et al.}, year={2021} }","ama":"Coombs C, Stacey P, Kawalek P, et al. What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective. <i>International Journal of Information Management</i>. 2021;58. doi:<a href=\"https://doi.org/10.1016/j.ijinfomgt.2021.102311\">10.1016/j.ijinfomgt.2021.102311</a>","mla":"Coombs, Crispin, et al. “What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective.” <i>International Journal of Information Management</i>, vol. 58, 2021, doi:<a href=\"https://doi.org/10.1016/j.ijinfomgt.2021.102311\">10.1016/j.ijinfomgt.2021.102311</a>."},"publication":"International Journal of Information Management","department":[{"_id":"34"},{"_id":"819"}],"type":"journal_article","date_created":"2023-08-04T07:27:14Z","intvolume":"        58","date_updated":"2023-10-16T12:58:02Z","author":[{"first_name":"Crispin","last_name":"Coombs","full_name":"Coombs, Crispin"},{"full_name":"Stacey, Patrick","first_name":"Patrick","last_name":"Stacey"},{"full_name":"Kawalek, Peter","first_name":"Peter","last_name":"Kawalek"},{"full_name":"Simeonova, Boyka","last_name":"Simeonova","first_name":"Boyka"},{"full_name":"Becker, Jörg","last_name":"Becker","first_name":"Jörg"},{"first_name":"Katrin","last_name":"Bergener","full_name":"Bergener, Katrin"},{"full_name":"Carvalho, João Álvaro","first_name":"João Álvaro","last_name":"Carvalho"},{"full_name":"Fantinato, Marcelo","last_name":"Fantinato","first_name":"Marcelo"},{"first_name":"Niels F.","last_name":"Garmann-Johnsen","full_name":"Garmann-Johnsen, Niels F."},{"full_name":"Grimme, Christian","first_name":"Christian","last_name":"Grimme"},{"first_name":"Armin","last_name":"Stein","full_name":"Stein, Armin"},{"id":"100740","orcid":"0000-0002-9788-8282","first_name":"Heike","last_name":"Trautmann","full_name":"Trautmann, Heike"}],"title":"What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective","status":"public","year":"2021","volume":58,"doi":"10.1016/j.ijinfomgt.2021.102311","user_id":"15504","_id":"46317","language":[{"iso":"eng"}]},{"date_created":"2023-08-04T07:25:08Z","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","citation":{"ama":"Prager RP, Seiler M, Trautmann H, Kerschke P. Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization. In: <i>2021 IEEE Symposium Series on Computational Intelligence (SSCI)</i>. ; 2021:1-8. doi:<a href=\"https://doi.org/10.1109/SSCI50451.2021.9660174\">10.1109/SSCI50451.2021.9660174</a>","bibtex":"@inproceedings{Prager_Seiler_Trautmann_Kerschke_2021, title={Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization}, DOI={<a href=\"https://doi.org/10.1109/SSCI50451.2021.9660174\">10.1109/SSCI50451.2021.9660174</a>}, booktitle={2021 IEEE Symposium Series on Computational Intelligence (SSCI)}, author={Prager, Raphael Patrick and Seiler, Moritz and Trautmann, Heike and Kerschke, Pascal}, year={2021}, pages={1–8} }","mla":"Prager, Raphael Patrick, et al. “Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization.” <i>2021 IEEE Symposium Series on Computational Intelligence (SSCI)</i>, 2021, pp. 1–8, doi:<a href=\"https://doi.org/10.1109/SSCI50451.2021.9660174\">10.1109/SSCI50451.2021.9660174</a>.","chicago":"Prager, Raphael Patrick, Moritz Seiler, Heike Trautmann, and Pascal Kerschke. “Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization.” In <i>2021 IEEE Symposium Series on Computational Intelligence (SSCI)</i>, 1–8, 2021. <a href=\"https://doi.org/10.1109/SSCI50451.2021.9660174\">https://doi.org/10.1109/SSCI50451.2021.9660174</a>.","short":"R.P. Prager, M. Seiler, H. Trautmann, P. Kerschke, in: 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021, pp. 1–8.","apa":"Prager, R. P., Seiler, M., Trautmann, H., &#38; Kerschke, P. (2021). Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization. <i>2021 IEEE Symposium Series on Computational Intelligence (SSCI)</i>, 1–8. <a href=\"https://doi.org/10.1109/SSCI50451.2021.9660174\">https://doi.org/10.1109/SSCI50451.2021.9660174</a>","ieee":"R. P. Prager, M. Seiler, H. Trautmann, and P. Kerschke, “Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization,” in <i>2021 IEEE Symposium Series on Computational Intelligence (SSCI)</i>, 2021, pp. 1–8, doi: <a href=\"https://doi.org/10.1109/SSCI50451.2021.9660174\">10.1109/SSCI50451.2021.9660174</a>."},"publication":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","abstract":[{"text":"We propose a novel method for automated algorithm selection in the domain of single-objective continuous black-box optimization. In contrast to existing methods, we use convolutional neural networks as the selection apparatus which bases its decision on a so-called ‘fitness map’. This fitness map is a 2D representation of a two dimensional search space where different gray scales indicate the quality of found solutions in certain areas. Our devised approach uses a modular CMA-ES framework which offers the option to create the conventional CMA-ES, CMA-ES with the alternate step-size adaptation and many other variants proposed over the years. In total, 4 608 different configurations are possible where most configurations are of complementary nature. In this proof-of-concept work, we consider a subset of 32 possible configurations. The developed method is evaluated against an excerpt of BBOB functions and its performance is compared against baselines that are commonly used in automated algorithm selection - the best standalone algorithm (configuration) and the best obtainable sequence of configurations. While the results indicate that the use of the fitness map is not superior on every benchmark problem, it indubitably shows its merit on more hard-to-solve problems. This offers a promising perspective for generalizing to other types of optimization problems and problem domains.","lang":"eng"}],"_id":"46315","language":[{"iso":"eng"}],"page":"1-8","doi":"10.1109/SSCI50451.2021.9660174","user_id":"15504","author":[{"first_name":"Raphael Patrick","last_name":"Prager","full_name":"Prager, Raphael Patrick"},{"id":"105520","full_name":"Seiler, Moritz","last_name":"Seiler","first_name":"Moritz"},{"full_name":"Trautmann, Heike","first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","id":"100740"},{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"}],"status":"public","title":"Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization","year":"2021","date_updated":"2024-06-07T07:12:28Z"},{"author":[{"full_name":"Assenmacher, Dennis","last_name":"Assenmacher","first_name":"Dennis"},{"first_name":"Marco","last_name":"Niemann","full_name":"Niemann, Marco"},{"full_name":"Müller, Kilian","last_name":"Müller","first_name":"Kilian"},{"first_name":"Moritz","last_name":"Seiler","full_name":"Seiler, Moritz","id":"105520"},{"full_name":"Riehle, Dennis M.","first_name":"Dennis M.","last_name":"Riehle"},{"id":"100740","full_name":"Trautmann, Heike","orcid":"0000-0002-9788-8282","first_name":"Heike","last_name":"Trautmann"}],"status":"public","title":"RP-Mod & RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets","year":"2021","date_updated":"2024-06-07T07:13:04Z","language":[{"iso":"eng"}],"_id":"46312","page":"1–14","user_id":"15504","citation":{"bibtex":"@inproceedings{Assenmacher_Niemann_Müller_Seiler_Riehle_Trautmann_2021, place={Virtual Event}, title={RP-Mod &#38; RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets}, booktitle={Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021)}, author={Assenmacher, Dennis and Niemann, Marco and Müller, Kilian and Seiler, Moritz and Riehle, Dennis M. and Trautmann, Heike}, year={2021}, pages={1–14} }","ama":"Assenmacher D, Niemann M, Müller K, Seiler M, Riehle DM, Trautmann H. RP-Mod &#38; RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets. In: <i>Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021)</i>. ; 2021:1–14.","mla":"Assenmacher, Dennis, et al. “RP-Mod &#38; RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets.” <i>Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021)</i>, 2021, pp. 1–14.","short":"D. Assenmacher, M. Niemann, K. Müller, M. Seiler, D.M. Riehle, H. Trautmann, in: Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021), Virtual Event, 2021, pp. 1–14.","chicago":"Assenmacher, Dennis, Marco Niemann, Kilian Müller, Moritz Seiler, Dennis M. Riehle, and Heike Trautmann. “RP-Mod &#38; RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets.” In <i>Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021)</i>, 1–14. Virtual Event, 2021.","ieee":"D. Assenmacher, M. Niemann, K. Müller, M. Seiler, D. M. Riehle, and H. Trautmann, “RP-Mod &#38; RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets,” in <i>Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021)</i>, 2021, pp. 1–14.","apa":"Assenmacher, D., Niemann, M., Müller, K., Seiler, M., Riehle, D. M., &#38; Trautmann, H. (2021). RP-Mod &#38; RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets. <i>Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021)</i>, 1–14."},"publication":"Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021)","abstract":[{"lang":"eng","text":"Abuse and hate are penetrating social media and many comment sections of news media companies. These platform providers invest considerable efforts to mod- erate user-generated contributions to prevent losing readers who get appalled by inappropriate texts. This is further enforced by legislative actions, which make non-clearance of these comments a punishable action. While (semi-)automated solutions using Natural Language Processing and advanced Machine Learning techniques are getting increasingly sophisticated, the domain of abusive language detection still struggles as large non-English and well-curated datasets are scarce or not publicly available. With this work, we publish and analyse the largest annotated German abusive language comment datasets to date. In contrast to existing datasets, we achieve a high labelling standard by conducting a thorough crowd-based an- notation study that complements professional moderators’ decisions, which are also included in the dataset. We compare and cross-evaluate the performance of baseline algorithms and state-of-the-art transformer-based language models, which are fine-tuned on our datasets and an existing alternative, showing the usefulness for the community."}],"date_created":"2023-08-04T07:22:59Z","place":"Virtual Event","department":[{"_id":"34"},{"_id":"819"}],"type":"conference"},{"author":[{"full_name":"Heins, Jonathan","last_name":"Heins","first_name":"Jonathan"},{"id":"102979","full_name":"Bossek, Jakob","orcid":"0000-0002-4121-4668","first_name":"Jakob","last_name":"Bossek"},{"first_name":"Janina","last_name":"Pohl","full_name":"Pohl, Janina"},{"id":"105520","full_name":"Seiler, Moritz","first_name":"Moritz","last_name":"Seiler"},{"full_name":"Trautmann, Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike","id":"100740"},{"last_name":"Kerschke","first_name":"Pascal","full_name":"Kerschke, Pascal"}],"status":"public","title":"On the Potential of Normalized TSP Features for Automated Algorithm Selection","year":"2021","date_updated":"2024-06-10T11:57:04Z","publisher":"Association for Computing Machinery","_id":"46313","language":[{"iso":"eng"}],"page":"1–15","editor":[{"full_name":"Computing Machinery Association, for","last_name":"Computing Machinery Association","first_name":"for"}],"doi":"10.1145/3450218.3477308","user_id":"15504","citation":{"bibtex":"@inproceedings{Heins_Bossek_Pohl_Seiler_Trautmann_Kerschke_2021, place={Dornbirn, Austria}, title={On the Potential of Normalized TSP Features for Automated Algorithm Selection}, DOI={<a href=\"https://doi.org/10.1145/3450218.3477308\">10.1145/3450218.3477308</a>}, booktitle={Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of genetic Algorithms (FOGA XVI)}, publisher={Association for Computing Machinery}, author={Heins, Jonathan and Bossek, Jakob and Pohl, Janina and Seiler, Moritz and Trautmann, Heike and Kerschke, Pascal}, editor={Computing Machinery Association, for}, year={2021}, pages={1–15} }","ama":"Heins J, Bossek J, Pohl J, Seiler M, Trautmann H, Kerschke P. On the Potential of Normalized TSP Features for Automated Algorithm Selection. In: Computing Machinery Association  for, ed. <i>Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA XVI)</i>. Association for Computing Machinery; 2021:1–15. doi:<a href=\"https://doi.org/10.1145/3450218.3477308\">10.1145/3450218.3477308</a>","mla":"Heins, Jonathan, et al. “On the Potential of Normalized TSP Features for Automated Algorithm Selection.” <i>Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA XVI)</i>, edited by for Computing Machinery Association, Association for Computing Machinery, 2021, pp. 1–15, doi:<a href=\"https://doi.org/10.1145/3450218.3477308\">10.1145/3450218.3477308</a>.","short":"J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, P. Kerschke, in:  for Computing Machinery Association (Ed.), Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA XVI), Association for Computing Machinery, Dornbirn, Austria, 2021, pp. 1–15.","chicago":"Heins, Jonathan, Jakob Bossek, Janina Pohl, Moritz Seiler, Heike Trautmann, and Pascal Kerschke. “On the Potential of Normalized TSP Features for Automated Algorithm Selection.” In <i>Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA XVI)</i>, edited by for Computing Machinery Association, 1–15. Dornbirn, Austria: Association for Computing Machinery, 2021. <a href=\"https://doi.org/10.1145/3450218.3477308\">https://doi.org/10.1145/3450218.3477308</a>.","ieee":"J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, and P. Kerschke, “On the Potential of Normalized TSP Features for Automated Algorithm Selection,” in <i>Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of genetic Algorithms (FOGA XVI)</i>, 2021, pp. 1–15, doi: <a href=\"https://doi.org/10.1145/3450218.3477308\">10.1145/3450218.3477308</a>.","apa":"Heins, J., Bossek, J., Pohl, J., Seiler, M., Trautmann, H., &#38; Kerschke, P. (2021). On the Potential of Normalized TSP Features for Automated Algorithm Selection. In  for Computing Machinery Association (Ed.), <i>Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of genetic Algorithms (FOGA XVI)</i> (pp. 1–15). Association for Computing Machinery. <a href=\"https://doi.org/10.1145/3450218.3477308\">https://doi.org/10.1145/3450218.3477308</a>"},"publication":"Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of genetic Algorithms (FOGA XVI)","abstract":[{"lang":"eng","text":"Classic automated algorithm selection (AS) for (combinatorial) optimization problems heavily relies on so-called instance features, i.e., numerical characteristics of the problem at hand ideally extracted with computationally low-demanding routines. For the traveling salesperson problem (TSP) a plethora of features have been suggested. Most of these features are, if at all, only normalized imprecisely raising the issue of feature values being strongly affected by the instance size. Such artifacts may have detrimental effects on algorithm selection models. We propose a normalization for two feature groups which stood out in multiple AS studies on the TSP: (a) features based on a minimum spanning tree (MST) and (b) a k-nearest neighbor graph (NNG) transformation of the input instance. To this end we theoretically derive minimum and maximum values for properties of MSTs and k-NNGs of Euclidean graphs. We analyze the differences in feature space between normalized versions of these features and their unnormalized counterparts. Our empirical investigations on various TSP benchmark sets point out that the feature scaling succeeds in eliminating the effect of the instance size. Eventually, a proof-of-concept AS-study shows promising results: models trained with normalized features tend to outperform those trained with the respective vanilla features."}],"place":"Dornbirn, Austria","date_created":"2023-08-04T07:23:57Z","department":[{"_id":"34"},{"_id":"819"}],"type":"conference"},{"_id":"46319","language":[{"iso":"eng"}],"user_id":"15504","author":[{"full_name":"Assenmacher, D","last_name":"Assenmacher","first_name":"D"},{"full_name":"Adam, L","first_name":"L","last_name":"Adam"},{"id":"100740","full_name":"Trautmann, Heike","first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282"},{"full_name":"Grimme, C","last_name":"Grimme","first_name":"C"}],"status":"public","title":"Towards Real-Time and Unsupervised Campaign Detection in Social Media","year":"2020","date_updated":"2023-10-16T12:59:10Z","date_created":"2023-08-04T07:29:36Z","place":"Florida, USA","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","citation":{"mla":"Assenmacher, D., et al. “Towards Real-Time and Unsupervised Campaign Detection in Social Media.” <i>Proceedings of the Florida Artificial Intelligence Research Society Conference</i>, 2020.","ama":"Assenmacher D, Adam L, Trautmann H, Grimme C. Towards Real-Time and Unsupervised Campaign Detection in Social Media. In: <i>Proceedings of the Florida Artificial Intelligence Research Society Conference</i>. ; 2020.","bibtex":"@inproceedings{Assenmacher_Adam_Trautmann_Grimme_2020, place={Florida, USA}, title={Towards Real-Time and Unsupervised Campaign Detection in Social Media}, booktitle={Proceedings of the Florida Artificial Intelligence Research Society Conference}, author={Assenmacher, D and Adam, L and Trautmann, Heike and Grimme, C}, year={2020} }","apa":"Assenmacher, D., Adam, L., Trautmann, H., &#38; Grimme, C. (2020). Towards Real-Time and Unsupervised Campaign Detection in Social Media. <i>Proceedings of the Florida Artificial Intelligence Research Society Conference</i>.","ieee":"D. Assenmacher, L. Adam, H. Trautmann, and C. Grimme, “Towards Real-Time and Unsupervised Campaign Detection in Social Media,” 2020.","chicago":"Assenmacher, D, L Adam, Heike Trautmann, and C Grimme. “Towards Real-Time and Unsupervised Campaign Detection in Social Media.” In <i>Proceedings of the Florida Artificial Intelligence Research Society Conference</i>. Florida, USA, 2020.","short":"D. Assenmacher, L. Adam, H. Trautmann, C. Grimme, in: Proceedings of the Florida Artificial Intelligence Research Society Conference, Florida, USA, 2020."},"publication":"Proceedings of the Florida Artificial Intelligence Research Society Conference","abstract":[{"lang":"eng","text":"The detection of orchestrated and potentially manipulative campaigns in social media is far more meaningful than an- alyzing single account behaviour but also more challenging in terms of pattern recognition, data processing, and com- putational complexity. While supervised learning methods need an enormous amount of reliable ground truth data to find rather inflexible patterns, classical unsupervised learn- ing techniques need a lot of computational power to handle large amount of data. This makes them infeasible for real- time analysis. In this work, we demonstrate the applicability of text stream clustering for the real-time detection of coordi- nated campaigns."}]},{"language":[{"iso":"eng"}],"_id":"46328","page":"996–1003","user_id":"15504","doi":"10.1109/SSCI47803.2020.9308510","author":[{"full_name":"Prager, Raphael Patrick","last_name":"Prager","first_name":"Raphael Patrick"},{"full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike","orcid":"0000-0002-9788-8282","id":"100740"},{"first_name":"Hao","last_name":"Wang","full_name":"Wang, Hao"},{"full_name":"Bäck, Thomas H. W.","last_name":"Bäck","first_name":"Thomas H. W."},{"last_name":"Kerschke","first_name":"Pascal","full_name":"Kerschke, Pascal"}],"title":"Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis","year":"2020","status":"public","date_updated":"2023-10-16T13:04:15Z","date_created":"2023-08-04T07:37:30Z","place":"Canberra, Australia","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","citation":{"ama":"Prager RP, Trautmann H, Wang H, Bäck THW, Kerschke P. Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis. In: <i>Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)</i>. ; 2020:996–1003. doi:<a href=\"https://doi.org/10.1109/SSCI47803.2020.9308510\">10.1109/SSCI47803.2020.9308510</a>","bibtex":"@inproceedings{Prager_Trautmann_Wang_Bäck_Kerschke_2020, place={Canberra, Australia}, title={Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis}, DOI={<a href=\"https://doi.org/10.1109/SSCI47803.2020.9308510\">10.1109/SSCI47803.2020.9308510</a>}, booktitle={Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)}, author={Prager, Raphael Patrick and Trautmann, Heike and Wang, Hao and Bäck, Thomas H. W. and Kerschke, Pascal}, year={2020}, pages={996–1003} }","mla":"Prager, Raphael Patrick, et al. “Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis.” <i>Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)</i>, 2020, pp. 996–1003, doi:<a href=\"https://doi.org/10.1109/SSCI47803.2020.9308510\">10.1109/SSCI47803.2020.9308510</a>.","short":"R.P. Prager, H. Trautmann, H. Wang, T.H.W. Bäck, P. Kerschke, in: Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, Australia, 2020, pp. 996–1003.","chicago":"Prager, Raphael Patrick, Heike Trautmann, Hao Wang, Thomas H. W. Bäck, and Pascal Kerschke. “Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis.” In <i>Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)</i>, 996–1003. Canberra, Australia, 2020. <a href=\"https://doi.org/10.1109/SSCI47803.2020.9308510\">https://doi.org/10.1109/SSCI47803.2020.9308510</a>.","apa":"Prager, R. P., Trautmann, H., Wang, H., Bäck, T. H. W., &#38; Kerschke, P. (2020). Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis. <i>Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)</i>, 996–1003. <a href=\"https://doi.org/10.1109/SSCI47803.2020.9308510\">https://doi.org/10.1109/SSCI47803.2020.9308510</a>","ieee":"R. P. Prager, H. Trautmann, H. Wang, T. H. W. Bäck, and P. Kerschke, “Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis,” in <i>Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)</i>, 2020, pp. 996–1003, doi: <a href=\"https://doi.org/10.1109/SSCI47803.2020.9308510\">10.1109/SSCI47803.2020.9308510</a>."},"publication":"Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)","abstract":[{"lang":"eng","text":"In this paper, we rely on previous work proposing a modularized version of CMA-ES, which captures several alterations to the conventional CMA-ES developed in recent years. Each alteration provides significant advantages under certain problem properties, e.g., multi-modality, high conditioning. These distinct advancements are implemented as modules which result in 4608 unique versions of CMA-ES. Previous findings illustrate the competitive advantage of enabling and disabling the aforementioned modules for different optimization problems. Yet, this modular CMA-ES is lacking a method to automatically determine when the activation of specific modules is auspicious and when it is not. We propose a well-performing instance-specific algorithm configuration model which selects an (almost) optimal configuration of modules for a given problem instance. In addition, the structure of this configuration model is able to capture inter-dependencies between modules, e.g., two (or more) modules might only be advantageous in unison for some problem types, making the orchestration of modules a crucial task. This is accomplished by chaining multiple random forest classifiers together into a so-called Classifier Chain based on a set of numerical features extracted by means of Exploratory Landscape Analysis (ELA) to describe the given problem instances."}]},{"citation":{"chicago":"Assenmacher, D, L Clever, JS Pohl, Heike Trautmann, and C Grimme. “A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media.” In <i>Proceedings of the International Conference on Human-Computer Interaction (HCII 2020): Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis</i>, edited by G Meiselwitz, 201–214. Cham: Springer International Publishing, 2020. <a href=\"https://doi.org/10.1007/978-3-030-49570-1_14\">https://doi.org/10.1007/978-3-030-49570-1_14</a>.","short":"D. Assenmacher, L. Clever, J. Pohl, H. Trautmann, C. Grimme, in: G. Meiselwitz (Ed.), Proceedings of the International Conference on Human-Computer Interaction (HCII 2020): Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis, Springer International Publishing, Cham, 2020, pp. 201–214.","ieee":"D. Assenmacher, L. Clever, J. Pohl, H. Trautmann, and C. Grimme, “A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media,” in <i>Proceedings of the International Conference on Human-Computer Interaction (HCII 2020): Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis</i>, 2020, pp. 201–214, doi: <a href=\"https://doi.org/10.1007/978-3-030-49570-1_14\">10.1007/978-3-030-49570-1_14</a>.","apa":"Assenmacher, D., Clever, L., Pohl, J., Trautmann, H., &#38; Grimme, C. (2020). A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media. In G. Meiselwitz (Ed.), <i>Proceedings of the International Conference on Human-Computer Interaction (HCII 2020): Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis</i> (pp. 201–214). Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-030-49570-1_14\">https://doi.org/10.1007/978-3-030-49570-1_14</a>","bibtex":"@inproceedings{Assenmacher_Clever_Pohl_Trautmann_Grimme_2020, place={Cham}, title={A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-49570-1_14\">10.1007/978-3-030-49570-1_14</a>}, booktitle={Proceedings of the International Conference on Human-Computer Interaction (HCII 2020): Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis}, publisher={Springer International Publishing}, author={Assenmacher, D and Clever, L and Pohl, JS and Trautmann, Heike and Grimme, C}, editor={Meiselwitz, G}, year={2020}, pages={201–214} }","ama":"Assenmacher D, Clever L, Pohl J, Trautmann H, Grimme C. A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media. In: Meiselwitz G, ed. <i>Proceedings of the International Conference on Human-Computer Interaction (HCII 2020): Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis</i>. Springer International Publishing; 2020:201–214. doi:<a href=\"https://doi.org/10.1007/978-3-030-49570-1_14\">10.1007/978-3-030-49570-1_14</a>","mla":"Assenmacher, D., et al. “A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media.” <i>Proceedings of the International Conference on Human-Computer Interaction (HCII 2020): Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis</i>, edited by G Meiselwitz, Springer International Publishing, 2020, pp. 201–214, doi:<a href=\"https://doi.org/10.1007/978-3-030-49570-1_14\">10.1007/978-3-030-49570-1_14</a>."},"place":"Cham","status":"public","editor":[{"full_name":"Meiselwitz, G","last_name":"Meiselwitz","first_name":"G"}],"user_id":"15504","_id":"46320","publisher":"Springer International Publishing","page":"201–214","abstract":[{"lang":"eng","text":"The identification of coordinated campaigns within Social Media is a complex task that is often hindered by missing labels and large amounts of data that have to be processed. We propose a new two-phase framework that uses unsupervised stream clustering for detecting suspicious trends over time in a first step. Afterwards, traditional offline analyses are applied to distinguish between normal trend evolution and malicious manipulation attempts. We demonstrate the applicability of our framework in the context of the final days of the Brexit in 2019/2020."}],"publication":"Proceedings of the International Conference on Human-Computer Interaction (HCII 2020): Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","date_created":"2023-08-04T07:30:29Z","date_updated":"2023-10-16T12:59:28Z","publication_identifier":{"isbn":["978-3-030-49570-1"]},"author":[{"full_name":"Assenmacher, D","first_name":"D","last_name":"Assenmacher"},{"full_name":"Clever, L","first_name":"L","last_name":"Clever"},{"full_name":"Pohl, JS","last_name":"Pohl","first_name":"JS"},{"full_name":"Trautmann, Heike","first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","id":"100740"},{"first_name":"C","last_name":"Grimme","full_name":"Grimme, C"}],"year":"2020","title":"A Two-Phase Framework for Detecting Manipulation Campaigns in Social Media","doi":"10.1007/978-3-030-49570-1_14","language":[{"iso":"eng"}]}]