[{"publication_identifier":{"isbn":["978-3-030-43823-4"]},"author":[{"full_name":"Carnein, Matthias","first_name":"Matthias","last_name":"Carnein"},{"id":"100740","full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike","orcid":"0000-0002-9788-8282"},{"full_name":"Bifet, Albert","first_name":"Albert","last_name":"Bifet"},{"last_name":"Pfahringer","first_name":"Bernhard","full_name":"Pfahringer, Bernhard"}],"status":"public","year":"2020","title":"Towards Automated Configuration of Stream Clustering Algorithms","date_updated":"2023-10-16T13:03:15Z","_id":"46325","language":[{"iso":"eng"}],"page":"137–143","doi":"10.1007/978-3-030-43823-4_12","user_id":"15504","citation":{"apa":"Carnein, M., Trautmann, H., Bifet, A., &#38; Pfahringer, B. (2020). Towards Automated Configuration of Stream Clustering Algorithms. <i>Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)</i>, 137–143. <a href=\"https://doi.org/10.1007/978-3-030-43823-4_12\">https://doi.org/10.1007/978-3-030-43823-4_12</a>","ieee":"M. Carnein, H. Trautmann, A. Bifet, and B. Pfahringer, “Towards Automated Configuration of Stream Clustering Algorithms,” in <i>Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)</i>, 2020, pp. 137–143, doi: <a href=\"https://doi.org/10.1007/978-3-030-43823-4_12\">10.1007/978-3-030-43823-4_12</a>.","chicago":"Carnein, Matthias, Heike Trautmann, Albert Bifet, and Bernhard Pfahringer. “Towards Automated Configuration of Stream Clustering Algorithms.” In <i>Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)</i>, 137–143. Würzburg, Germany, 2020. <a href=\"https://doi.org/10.1007/978-3-030-43823-4_12\">https://doi.org/10.1007/978-3-030-43823-4_12</a>.","short":"M. Carnein, H. Trautmann, A. Bifet, B. Pfahringer, in: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19), Würzburg, Germany, 2020, pp. 137–143.","mla":"Carnein, Matthias, et al. “Towards Automated Configuration of Stream Clustering Algorithms.” <i>Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)</i>, 2020, pp. 137–143, doi:<a href=\"https://doi.org/10.1007/978-3-030-43823-4_12\">10.1007/978-3-030-43823-4_12</a>.","ama":"Carnein M, Trautmann H, Bifet A, Pfahringer B. Towards Automated Configuration of Stream Clustering Algorithms. In: <i>Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)</i>. ; 2020:137–143. doi:<a href=\"https://doi.org/10.1007/978-3-030-43823-4_12\">10.1007/978-3-030-43823-4_12</a>","bibtex":"@inproceedings{Carnein_Trautmann_Bifet_Pfahringer_2020, place={Würzburg, Germany}, title={Towards Automated Configuration of Stream Clustering Algorithms}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-43823-4_12\">10.1007/978-3-030-43823-4_12</a>}, booktitle={Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)}, author={Carnein, Matthias and Trautmann, Heike and Bifet, Albert and Pfahringer, Bernhard}, year={2020}, pages={137–143} }"},"publication":"Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD ’19)","abstract":[{"text":"Clustering is an important technique in data analysis which can reveal hidden patterns and unknown relationships in the data. A common problem in clustering is the proper choice of parameter settings. To tackle this, automated algorithm configuration is available which can automatically find the best parameter settings. In practice, however, many of our today’s data sources are data streams due to the widespread deployment of sensors, the internet-of-things or (social) media. Stream clustering aims to tackle this challenge by identifying, tracking and updating clusters over time. Unfortunately, none of the existing approaches for automated algorithm configuration are directly applicable to the streaming scenario. In this paper, we explore the possibility of automated algorithm configuration for stream clustering algorithms using an ensemble of different configurations. In first experiments, we demonstrate that our approach is able to automatically find superior configurations and refine them over time.","lang":"eng"}],"place":"Würzburg, Germany","date_created":"2023-08-04T07:35:24Z","department":[{"_id":"34"},{"_id":"819"}],"type":"conference"},{"citation":{"apa":"Assenmacher, D., Frischlich , L., Trautmann, H., Grimme, C., &#38; Adam, L. (2020). Inside the tool set of automation: Free social bot code revisited. In C. Grimme, M. Preuß, F. Takes, &#38; A. Waldherr (Eds.), <i>Disinformation in open online media</i> (pp. 101–114). Springer.","ieee":"D. Assenmacher, L. Frischlich , H. Trautmann, C. Grimme, and L. Adam, “Inside the tool set of automation: Free social bot code revisited,” in <i>Disinformation in open online media</i>, 2020, pp. 101–114.","chicago":"Assenmacher, Dennis, Lena Frischlich , Heike Trautmann, Christian Grimme, and Lena Adam. “Inside the Tool Set of Automation: Free Social Bot Code Revisited.” In <i>Disinformation in Open Online Media</i>, edited by Christian Grimme, Mike Preuß, Frank Takes, and Annie Waldherr, 101–114. Lecture Notes in Computer Science. Wiesbaden: Springer, 2020.","short":"D. Assenmacher, L. Frischlich , H. Trautmann, C. Grimme, L. Adam, in: C. Grimme, M. Preuß, F. Takes, A. Waldherr (Eds.), Disinformation in Open Online Media, Springer, Wiesbaden, 2020, pp. 101–114.","mla":"Assenmacher, Dennis, et al. “Inside the Tool Set of Automation: Free Social Bot Code Revisited.” <i>Disinformation in Open Online Media</i>, edited by Christian Grimme et al., Springer, 2020, pp. 101–114.","ama":"Assenmacher D, Frischlich  L, Trautmann H, Grimme C, Adam L. Inside the tool set of automation: Free social bot code revisited. In: Grimme C, Preuß M, Takes F, Waldherr A, eds. <i>Disinformation in Open Online Media</i>. Lecture Notes in Computer Science. Springer; 2020:101–114.","bibtex":"@inproceedings{Assenmacher_Frischlich _Trautmann_Grimme_Adam_2020, place={Wiesbaden}, series={Lecture Notes in Computer Science}, title={Inside the tool set of automation: Free social bot code revisited}, booktitle={Disinformation in open online media}, publisher={Springer}, author={Assenmacher, Dennis and Frischlich , Lena and Trautmann, Heike and Grimme, Christian and Adam, Lena}, editor={Grimme, Christian and Preuß, Mike and Takes, Frank and Waldherr, Annie}, year={2020}, pages={101–114}, collection={Lecture Notes in Computer Science} }"},"publication":"Disinformation in open online media","abstract":[{"text":"Social bots have recently gained attention in the context of public opinion manipulation on social media platforms. While a lot of research effort has been put into the classification and detection of such automated programs, it is still unclear how technically sophisticated those bots are, which platforms they target, and where they originate from. To answer these questions, we gathered repository data from open source collaboration platforms to identify the status-quo of social bot development as well as first insights into the overall skills of publicly available bot code.","lang":"eng"}],"date_created":"2023-08-04T07:31:13Z","place":"Wiesbaden","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","author":[{"full_name":"Assenmacher, Dennis","first_name":"Dennis","last_name":"Assenmacher"},{"full_name":"Frischlich , Lena","first_name":"Lena","last_name":"Frischlich "},{"full_name":"Trautmann, Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","first_name":"Heike","id":"100740"},{"first_name":"Christian","last_name":"Grimme","full_name":"Grimme, Christian"},{"first_name":"Lena","last_name":"Adam","full_name":"Adam, Lena"}],"title":"Inside the tool set of automation: Free social bot code revisited","status":"public","year":"2020","date_updated":"2023-10-16T13:00:15Z","series_title":"Lecture Notes in Computer Science","_id":"46321","language":[{"iso":"eng"}],"publisher":"Springer","page":"101–114","editor":[{"first_name":"Christian","last_name":"Grimme","full_name":"Grimme, Christian"},{"full_name":"Preuß, Mike","last_name":"Preuß","first_name":"Mike"},{"full_name":"Takes, Frank","last_name":"Takes","first_name":"Frank"},{"last_name":"Waldherr","first_name":"Annie","full_name":"Waldherr, Annie"}],"user_id":"15504"},{"abstract":[{"text":"Machine learning has become one of the most important tools in data analysis. However, selecting the most appropriate machine learning algorithm and tuning its hyperparameters to their optimal values remains a difficult task. This is even more difficult for streaming applications where automated approaches are often not available to help during algorithm selection and configuration. This paper proposes the first approach for automated algorithm selection and configuration of stream clustering algorithms. We train an ensemble of different stream clustering algorithms and configurations in parallel and use the best performing configuration to obtain a clustering solution. By drawing new configurations from better performing ones, we are able to improve the ensemble performance over time. In large experiments on real and artificial data we show how our ensemble approach can improve upon default configurations and can also compete with a-posteriori algorithm configuration. Our approach is considerably faster than a-posteriori approaches and applicable in real-time. In addition, it is not limited to stream clustering and can be generalised to all streaming applications, including stream classification and regression.","lang":"eng"}],"publication":"Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020)","citation":{"short":"M. Carnein, H. Trautmann, A. Bifet, B. Pfahringer, in: Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020), Athens, Greece, 2020, pp. 80–95.","chicago":"Carnein, Matthias, Heike Trautmann, Albert Bifet, and Bernhard Pfahringer. “ConfStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms.” In <i>Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020)</i>, 80–95. Athens, Greece, 2020. <a href=\"https://doi.org/10.1007/978-3-030-53552-0_10\">https://doi.org/10.1007/978-3-030-53552-0_10</a>.","apa":"Carnein, M., Trautmann, H., Bifet, A., &#38; Pfahringer, B. (2020). confStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms. <i>Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020)</i>, 80–95. <a href=\"https://doi.org/10.1007/978-3-030-53552-0_10\">https://doi.org/10.1007/978-3-030-53552-0_10</a>","ieee":"M. Carnein, H. Trautmann, A. Bifet, and B. Pfahringer, “confStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms,” in <i>Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020)</i>, 2020, pp. 80–95, doi: <a href=\"https://doi.org/10.1007/978-3-030-53552-0_10\">10.1007/978-3-030-53552-0_10</a>.","ama":"Carnein M, Trautmann H, Bifet A, Pfahringer B. confStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms. In: <i>Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020)</i>. ; 2020:80–95. doi:<a href=\"https://doi.org/10.1007/978-3-030-53552-0_10\">10.1007/978-3-030-53552-0_10</a>","bibtex":"@inproceedings{Carnein_Trautmann_Bifet_Pfahringer_2020, place={Athens, Greece}, title={confStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-53552-0_10\">10.1007/978-3-030-53552-0_10</a>}, booktitle={Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020)}, author={Carnein, Matthias and Trautmann, Heike and Bifet, Albert and Pfahringer, Bernhard}, year={2020}, pages={80–95} }","mla":"Carnein, Matthias, et al. “ConfStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms.” <i>Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020)</i>, 2020, pp. 80–95, doi:<a href=\"https://doi.org/10.1007/978-3-030-53552-0_10\">10.1007/978-3-030-53552-0_10</a>."},"type":"conference","department":[{"_id":"34"},{"_id":"819"}],"place":"Athens, Greece","date_created":"2023-08-04T07:36:03Z","date_updated":"2023-10-16T13:03:36Z","status":"public","year":"2020","title":"confStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms","author":[{"full_name":"Carnein, Matthias","first_name":"Matthias","last_name":"Carnein"},{"id":"100740","full_name":"Trautmann, Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","first_name":"Heike"},{"first_name":"Albert","last_name":"Bifet","full_name":"Bifet, Albert"},{"last_name":"Pfahringer","first_name":"Bernhard","full_name":"Pfahringer, Bernhard"}],"doi":"10.1007/978-3-030-53552-0_10","user_id":"15504","page":"80–95","_id":"46326","language":[{"iso":"eng"}]},{"date_updated":"2023-10-16T13:03:56Z","author":[{"last_name":"Lena","first_name":"Clever","full_name":"Lena, Clever"},{"last_name":"Frischlich","first_name":"Lena","full_name":"Frischlich, Lena"},{"last_name":"Trautmann","orcid":"0000-0002-9788-8282","first_name":"Heike","full_name":"Trautmann, Heike","id":"100740"},{"last_name":"Grimme","first_name":"Christian","full_name":"Grimme, Christian"}],"year":"2020","status":"public","title":"Automated detection of nostalgic text in the context of societal pessimism","editor":[{"first_name":"Christian","last_name":"Grimme","full_name":"Grimme, Christian"},{"full_name":"Preuß, Mike","last_name":"Preuß","first_name":"Mike"},{"first_name":"Frank","last_name":"Takes","full_name":"Takes, Frank"},{"full_name":"Waldherr, Annie","first_name":"Annie","last_name":"Waldherr"}],"user_id":"15504","language":[{"iso":"eng"}],"_id":"46327","page":"48–58","abstract":[{"text":"In online media environments, nostalgia can be used as important ingredient of propaganda strategies, specifically, by creating societal pessimism. This work addresses the automated detection of nostalgic text as a first step towards automatically identifying nostalgia-based manipulation strategies. We compare the performance of standard machine learning approaches on this challenge and demonstrate the successful transfer of the best performing approach to real-world nostalgia detection in a case study.","lang":"eng"}],"citation":{"mla":"Lena, Clever, et al. “Automated Detection of Nostalgic Text in the Context of Societal Pessimism.” <i>Disinformation in Open Online Media</i>, edited by Christian Grimme et al., 2020, pp. 48–58.","ama":"Lena C, Frischlich L, Trautmann H, Grimme C. Automated detection of nostalgic text in the context of societal pessimism. In: Grimme C, Preuß M, Takes F, Waldherr A, eds. <i>Disinformation in Open Online Media</i>. ; 2020:48–58.","bibtex":"@inproceedings{Lena_Frischlich_Trautmann_Grimme_2020, place={Hamburg, Deutschland}, title={Automated detection of nostalgic text in the context of societal pessimism}, booktitle={Disinformation in open online media}, author={Lena, Clever and Frischlich, Lena and Trautmann, Heike and Grimme, Christian}, editor={Grimme, Christian and Preuß, Mike and Takes, Frank and Waldherr, Annie}, year={2020}, pages={48–58} }","apa":"Lena, C., Frischlich, L., Trautmann, H., &#38; Grimme, C. (2020). Automated detection of nostalgic text in the context of societal pessimism. In C. Grimme, M. Preuß, F. Takes, &#38; A. Waldherr (Eds.), <i>Disinformation in open online media</i> (pp. 48–58).","ieee":"C. Lena, L. Frischlich, H. Trautmann, and C. Grimme, “Automated detection of nostalgic text in the context of societal pessimism,” in <i>Disinformation in open online media</i>, 2020, pp. 48–58.","short":"C. Lena, L. Frischlich, H. Trautmann, C. Grimme, in: C. Grimme, M. Preuß, F. Takes, A. Waldherr (Eds.), Disinformation in Open Online Media, Hamburg, Deutschland, 2020, pp. 48–58.","chicago":"Lena, Clever, Lena Frischlich, Heike Trautmann, and Christian Grimme. “Automated Detection of Nostalgic Text in the Context of Societal Pessimism.” In <i>Disinformation in Open Online Media</i>, edited by Christian Grimme, Mike Preuß, Frank Takes, and Annie Waldherr, 48–58. Hamburg, Deutschland, 2020."},"publication":"Disinformation in open online media","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","place":"Hamburg, Deutschland","date_created":"2023-08-04T07:36:43Z"},{"publication_identifier":{"isbn":["978-3-030-49576-3"]},"author":[{"full_name":"Riehle, Dennis M.","last_name":"Riehle","first_name":"Dennis M."},{"full_name":"Niemann, Marco","first_name":"Marco","last_name":"Niemann"},{"first_name":"Jens","last_name":"Brunk","full_name":"Brunk, Jens"},{"first_name":"Dennis","last_name":"Assenmacher","full_name":"Assenmacher, Dennis"},{"id":"100740","orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike","full_name":"Trautmann, Heike"},{"full_name":"Becker, Jörg","last_name":"Becker","first_name":"Jörg"}],"status":"public","title":"Building an Integrated Comment Moderation System – Towards a Semi-automatic Moderation Tool","year":"2020","date_updated":"2023-10-16T13:04:36Z","_id":"46329","language":[{"iso":"eng"}],"publisher":"Springer International Publishing","page":"71–86","editor":[{"last_name":"Meiselwitz","first_name":"Gabriele","full_name":"Meiselwitz, Gabriele"}],"user_id":"15504","citation":{"ieee":"D. M. Riehle, M. Niemann, J. Brunk, D. Assenmacher, H. Trautmann, and J. Becker, “Building an Integrated Comment Moderation System – Towards a Semi-automatic Moderation Tool,” in <i>Social Computing and Social Media. Participation, User Experience, Consumer Experience, and Applications of Social Computing</i>, 2020, pp. 71–86.","apa":"Riehle, D. M., Niemann, M., Brunk, J., Assenmacher, D., Trautmann, H., &#38; Becker, J. (2020). Building an Integrated Comment Moderation System – Towards a Semi-automatic Moderation Tool. In G. Meiselwitz (Ed.), <i>Social Computing and Social Media. Participation, User Experience, Consumer Experience, and Applications of Social Computing</i> (pp. 71–86). Springer International Publishing.","short":"D.M. Riehle, M. Niemann, J. Brunk, D. Assenmacher, H. Trautmann, J. Becker, in: G. Meiselwitz (Ed.), Social Computing and Social Media. Participation, User Experience, Consumer Experience, and Applications of Social Computing, Springer International Publishing, Cham, 2020, pp. 71–86.","chicago":"Riehle, Dennis M., Marco Niemann, Jens Brunk, Dennis Assenmacher, Heike Trautmann, and Jörg Becker. “Building an Integrated Comment Moderation System – Towards a Semi-Automatic Moderation Tool.” In <i>Social Computing and Social Media. Participation, User Experience, Consumer Experience, and Applications of Social Computing</i>, edited by Gabriele Meiselwitz, 71–86. Cham: Springer International Publishing, 2020.","mla":"Riehle, Dennis M., et al. “Building an Integrated Comment Moderation System – Towards a Semi-Automatic Moderation Tool.” <i>Social Computing and Social Media. Participation, User Experience, Consumer Experience, and Applications of Social Computing</i>, edited by Gabriele Meiselwitz, Springer International Publishing, 2020, pp. 71–86.","bibtex":"@inproceedings{Riehle_Niemann_Brunk_Assenmacher_Trautmann_Becker_2020, place={Cham}, title={Building an Integrated Comment Moderation System – Towards a Semi-automatic Moderation Tool}, booktitle={Social Computing and Social Media. Participation, User Experience, Consumer Experience, and Applications of Social Computing}, publisher={Springer International Publishing}, author={Riehle, Dennis M. and Niemann, Marco and Brunk, Jens and Assenmacher, Dennis and Trautmann, Heike and Becker, Jörg}, editor={Meiselwitz, Gabriele}, year={2020}, pages={71–86} }","ama":"Riehle DM, Niemann M, Brunk J, Assenmacher D, Trautmann H, Becker J. Building an Integrated Comment Moderation System – Towards a Semi-automatic Moderation Tool. In: Meiselwitz G, ed. <i>Social Computing and Social Media. Participation, User Experience, Consumer Experience, and Applications of Social Computing</i>. Springer International Publishing; 2020:71–86."},"publication":"Social Computing and Social Media. Participation, User Experience, Consumer Experience, and Applications of Social Computing","abstract":[{"lang":"eng","text":"The past decade has been characterized by a strong increase in the use of social media and a continuous growth of public online discussion. With the failure of purely manual moderation, platform operators started searching for semi-automated solutions, where the application of Natural Language Processing (NLP) and Machine Learning (ML) techniques is promising. However, this requires huge financial investments for algorithmic implementations, data collection, and model training, which only big players can afford. To support smaller or medium-sized media enterprises (SME), we developed an integrated comment moderation system as an IT platform. This platform acts as a service provider and offers Analytics as a Service (AaaS) to SMEs. Operating such a platform, however, requires a robust technology stack, integrated workflows and well-defined interfaces between all parties. In this paper, we develop and discuss a suitable IT architecture and present a prototypical implementation."}],"place":"Cham","date_created":"2023-08-04T07:38:42Z","department":[{"_id":"34"},{"_id":"819"}],"type":"conference"},{"volume":6,"user_id":"15504","doi":"10.1177/2056305120939264","language":[{"iso":"eng"}],"_id":"46333","page":"2056305120939264","intvolume":"         6","date_updated":"2023-10-16T13:06:34Z","author":[{"last_name":"Assenmacher","first_name":"Dennis","full_name":"Assenmacher, Dennis"},{"full_name":"Clever, Lena","last_name":"Clever","first_name":"Lena"},{"last_name":"Frischlich","first_name":"Lena","full_name":"Frischlich, Lena"},{"last_name":"Quandt","first_name":"Thorsten","full_name":"Quandt, Thorsten"},{"full_name":"Trautmann, Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike","id":"100740"},{"full_name":"Grimme, Christian","first_name":"Christian","last_name":"Grimme"}],"status":"public","title":"Demystifying Social Bots: On the Intelligence of Automated Social Media Actors","year":"2020","department":[{"_id":"34"},{"_id":"819"}],"type":"journal_article","date_created":"2023-08-04T07:41:37Z","abstract":[{"lang":"eng","text":" Recently, social bots, (semi-) automatized accounts in social media, gained global attention in the context of public opinion manipulation. Dystopian scenarios like the malicious amplification of topics, the spreading of disinformation, and the manipulation of elections through “opinion machines” created headlines around the globe. As a consequence, much research effort has been put into the classification and detection of social bots. Yet, it is still unclear how easy an average online media user can purchase social bots, which platforms they target, where they originate from, and how sophisticated these bots are. This work provides a much needed new perspective on these questions. By providing insights into the markets of social bots in the clearnet and darknet as well as an exhaustive analysis of freely available software tools for automation during the last decade, we shed light on the availability and capabilities of automated profiles in social media platforms. Our results confirm the increasing importance of social bot technology but also uncover an as yet unknown discrepancy of theoretical and practically achieved artificial intelligence in social bots: while literature reports on a high degree of intelligence for chat bots and assumes the same for social bots, the observed degree of intelligence in social bot implementations is limited. In fact, the overwhelming majority of available services and software are of supportive nature and merely provide modules of automation instead of fully fledged “intelligent” social bots. "}],"citation":{"mla":"Assenmacher, Dennis, et al. “Demystifying Social Bots: On the Intelligence of Automated Social Media Actors.” <i>Social Media + Society</i>, vol. 6, no. 3, 2020, p. 2056305120939264, doi:<a href=\"https://doi.org/10.1177/2056305120939264\">10.1177/2056305120939264</a>.","bibtex":"@article{Assenmacher_Clever_Frischlich_Quandt_Trautmann_Grimme_2020, title={Demystifying Social Bots: On the Intelligence of Automated Social Media Actors}, volume={6}, DOI={<a href=\"https://doi.org/10.1177/2056305120939264\">10.1177/2056305120939264</a>}, number={3}, journal={Social Media + Society}, author={Assenmacher, Dennis and Clever, Lena and Frischlich, Lena and Quandt, Thorsten and Trautmann, Heike and Grimme, Christian}, year={2020}, pages={2056305120939264} }","ama":"Assenmacher D, Clever L, Frischlich L, Quandt T, Trautmann H, Grimme C. Demystifying Social Bots: On the Intelligence of Automated Social Media Actors. <i>Social Media + Society</i>. 2020;6(3):2056305120939264. doi:<a href=\"https://doi.org/10.1177/2056305120939264\">10.1177/2056305120939264</a>","ieee":"D. Assenmacher, L. Clever, L. Frischlich, T. Quandt, H. Trautmann, and C. Grimme, “Demystifying Social Bots: On the Intelligence of Automated Social Media Actors,” <i>Social Media + Society</i>, vol. 6, no. 3, p. 2056305120939264, 2020, doi: <a href=\"https://doi.org/10.1177/2056305120939264\">10.1177/2056305120939264</a>.","apa":"Assenmacher, D., Clever, L., Frischlich, L., Quandt, T., Trautmann, H., &#38; Grimme, C. (2020). Demystifying Social Bots: On the Intelligence of Automated Social Media Actors. <i>Social Media + Society</i>, <i>6</i>(3), 2056305120939264. <a href=\"https://doi.org/10.1177/2056305120939264\">https://doi.org/10.1177/2056305120939264</a>","short":"D. Assenmacher, L. Clever, L. Frischlich, T. Quandt, H. Trautmann, C. Grimme, Social Media + Society 6 (2020) 2056305120939264.","chicago":"Assenmacher, Dennis, Lena Clever, Lena Frischlich, Thorsten Quandt, Heike Trautmann, and Christian Grimme. “Demystifying Social Bots: On the Intelligence of Automated Social Media Actors.” <i>Social Media + Society</i> 6, no. 3 (2020): 2056305120939264. <a href=\"https://doi.org/10.1177/2056305120939264\">https://doi.org/10.1177/2056305120939264</a>."},"issue":"3","publication":"Social Media + Society"},{"department":[{"_id":"34"},{"_id":"819"}],"type":"conference","place":"Canberra, Australia","date_created":"2023-08-04T07:40:33Z","abstract":[{"text":"Multimodality is one of the biggest difficulties for optimization as local optima are often preventing algorithms from making progress. This does not only challenge local strategies that can get stuck. It also hinders meta-heuristics like evolutionary algorithms in convergence to the global optimum. In this paper we present a new concept of gradient descent, which is able to escape local traps. It relies on multiobjectivization of the original problem and applies the recently proposed and here slightly modified multi-objective local search mechanism MOGSA. We use a sophisticated visualization technique for multi-objective problems to prove the working principle of our idea. As such, this work highlights the transfer of new insights from the multi-objective to the single-objective domain and provides first visual evidence that multiobjectivization can link single-objective local optima in multimodal landscapes.","lang":"eng"}],"citation":{"ama":"Steinhoff V, Kerschke P, Aspar P, Trautmann H, Grimme C. Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent. In: <i>Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)</i>. ; 2020:2445–2452. doi:<a href=\"https://doi.org/10.1109/SSCI47803.2020.9308259\">10.1109/SSCI47803.2020.9308259</a>","bibtex":"@inproceedings{Steinhoff_Kerschke_Aspar_Trautmann_Grimme_2020, place={Canberra, Australia}, title={Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent}, DOI={<a href=\"https://doi.org/10.1109/SSCI47803.2020.9308259\">10.1109/SSCI47803.2020.9308259</a>}, booktitle={Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)}, author={Steinhoff, Vera and Kerschke, Pascal and Aspar, Pelin and Trautmann, Heike and Grimme, Christian}, year={2020}, pages={2445–2452} }","mla":"Steinhoff, Vera, et al. “Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent.” <i>Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)</i>, 2020, pp. 2445–2452, doi:<a href=\"https://doi.org/10.1109/SSCI47803.2020.9308259\">10.1109/SSCI47803.2020.9308259</a>.","chicago":"Steinhoff, Vera, Pascal Kerschke, Pelin Aspar, Heike Trautmann, and Christian Grimme. “Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent.” In <i>Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)</i>, 2445–2452. Canberra, Australia, 2020. <a href=\"https://doi.org/10.1109/SSCI47803.2020.9308259\">https://doi.org/10.1109/SSCI47803.2020.9308259</a>.","short":"V. Steinhoff, P. Kerschke, P. Aspar, H. Trautmann, C. Grimme, in: Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, Australia, 2020, pp. 2445–2452.","apa":"Steinhoff, V., Kerschke, P., Aspar, P., Trautmann, H., &#38; Grimme, C. (2020). Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent. <i>Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)</i>, 2445–2452. <a href=\"https://doi.org/10.1109/SSCI47803.2020.9308259\">https://doi.org/10.1109/SSCI47803.2020.9308259</a>","ieee":"V. Steinhoff, P. Kerschke, P. Aspar, H. Trautmann, and C. Grimme, “Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent,” in <i>Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)</i>, 2020, pp. 2445–2452, doi: <a href=\"https://doi.org/10.1109/SSCI47803.2020.9308259\">10.1109/SSCI47803.2020.9308259</a>."},"publication":"Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)","doi":"10.1109/SSCI47803.2020.9308259","user_id":"15504","_id":"46332","language":[{"iso":"eng"}],"page":"2445–2452","date_updated":"2023-10-16T13:05:49Z","author":[{"first_name":"Vera","last_name":"Steinhoff","full_name":"Steinhoff, Vera"},{"full_name":"Kerschke, Pascal","first_name":"Pascal","last_name":"Kerschke"},{"full_name":"Aspar, Pelin","first_name":"Pelin","last_name":"Aspar"},{"id":"100740","full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike","orcid":"0000-0002-9788-8282"},{"full_name":"Grimme, Christian","first_name":"Christian","last_name":"Grimme"}],"title":"Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent","year":"2020","status":"public"},{"author":[{"id":"105520","full_name":"Seiler, Moritz","last_name":"Seiler","first_name":"Moritz"},{"id":"100740","full_name":"Trautmann, Heike","first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"}],"title":"Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries","status":"public","year":"2020","date_updated":"2024-06-07T07:11:53Z","_id":"46331","language":[{"iso":"eng"}],"page":"1–8","user_id":"15504","doi":"10.1109/IJCNN48605.2020.9207338","citation":{"short":"M. Seiler, H. Trautmann, P. Kerschke, in: Proceedings of the International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 2020, pp. 1–8.","chicago":"Seiler, Moritz, Heike Trautmann, and Pascal Kerschke. “Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries.” In <i>Proceedings of the International Joint Conference on Neural Networks (IJCNN)</i>, 1–8. Glasgow, UK, 2020. <a href=\"https://doi.org/10.1109/IJCNN48605.2020.9207338\">https://doi.org/10.1109/IJCNN48605.2020.9207338</a>.","ieee":"M. Seiler, H. Trautmann, and P. Kerschke, “Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries,” in <i>Proceedings of the International Joint Conference on Neural Networks (IJCNN)</i>, 2020, pp. 1–8, doi: <a href=\"https://doi.org/10.1109/IJCNN48605.2020.9207338\">10.1109/IJCNN48605.2020.9207338</a>.","apa":"Seiler, M., Trautmann, H., &#38; Kerschke, P. (2020). Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries. <i>Proceedings of the International Joint Conference on Neural Networks (IJCNN)</i>, 1–8. <a href=\"https://doi.org/10.1109/IJCNN48605.2020.9207338\">https://doi.org/10.1109/IJCNN48605.2020.9207338</a>","bibtex":"@inproceedings{Seiler_Trautmann_Kerschke_2020, place={Glasgow, UK}, title={Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries}, DOI={<a href=\"https://doi.org/10.1109/IJCNN48605.2020.9207338\">10.1109/IJCNN48605.2020.9207338</a>}, booktitle={Proceedings of the International Joint Conference on Neural Networks (IJCNN)}, author={Seiler, Moritz and Trautmann, Heike and Kerschke, Pascal}, year={2020}, pages={1–8} }","ama":"Seiler M, Trautmann H, Kerschke P. Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries. In: <i>Proceedings of the International Joint Conference on Neural Networks (IJCNN)</i>. ; 2020:1–8. doi:<a href=\"https://doi.org/10.1109/IJCNN48605.2020.9207338\">10.1109/IJCNN48605.2020.9207338</a>","mla":"Seiler, Moritz, et al. “Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries.” <i>Proceedings of the International Joint Conference on Neural Networks (IJCNN)</i>, 2020, pp. 1–8, doi:<a href=\"https://doi.org/10.1109/IJCNN48605.2020.9207338\">10.1109/IJCNN48605.2020.9207338</a>."},"publication":"Proceedings of the International Joint Conference on Neural Networks (IJCNN)","abstract":[{"text":"Artificial neural networks in general and deep learning networks in particular established themselves as popular and powerful machine learning algorithms. While the often tremendous sizes of these networks are beneficial when solving complex tasks, the tremendous number of parameters also causes such networks to be vulnerable to malicious behavior such as adversarial perturbations. These perturbations can change a model's classification decision. Moreover, while single-step adversaries can easily be transferred from network to network, the transfer of more powerful multi-step adversaries has - usually - been rather difficult.In this work, we introduce a method for generating strong adversaries that can easily (and frequently) be transferred between different models. This method is then used to generate a large set of adversaries, based on which the effects of selected defense methods are experimentally assessed. At last, we introduce a novel, simple, yet effective approach to enhance the resilience of neural networks against adversaries and benchmark it against established defense methods. In contrast to the already existing methods, our proposed defense approach is much more efficient as it only requires a single additional forward-pass to achieve comparable performance results.","lang":"eng"}],"date_created":"2023-08-04T07:39:48Z","place":"Glasgow, UK","department":[{"_id":"34"},{"_id":"819"}],"type":"conference"},{"type":"conference","department":[{"_id":"34"},{"_id":"819"}],"date_created":"2023-08-04T07:39:05Z","place":"Leiden, The Netherlands","abstract":[{"lang":"eng","text":"In this work we focus on the well-known Euclidean Traveling Salesperson Problem (TSP) and two highly competitive inexact heuristic TSP solvers, EAX and LKH, in the context of per-instance algorithm selection (AS). We evolve instances with 1000 nodes where the solvers show strongly different performance profiles. These instances serve as a basis for an exploratory study on the identification of well-discriminating problem characteristics (features). Our results in a nutshell: we show that even though (1) promising features exist, (2) these are in line with previous results from the literature, and (3) models trained with these features are more accurate than models adopting sophisticated feature selection methods, the advantage is not close to the virtual best solver in terms of penalized average runtime and so is the performance gain over the single best solver. However, we show that a feature-free deep neural network based approach solely based on visual representation of the instances already matches classical AS model results and thus shows huge potential for future studies."}],"publication":"Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI)","citation":{"bibtex":"@inproceedings{Seiler_Pohl_Bossek_Kerschke_Trautmann_2020, place={Leiden, The Netherlands}, title={Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">10.1007/978-3-030-58112-1_4</a>}, booktitle={Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI)}, author={Seiler, Moritz and Pohl, Janina and Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}, editor={Bäck, Thomas and Preuss, Mike and Deutz, André and Wang, Hao and Doerr, Carola and Emmerich, Michael and Trautmann, Heike}, year={2020}, pages={48–64} }","ama":"Seiler M, Pohl J, Bossek J, Kerschke P, Trautmann H. Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. In: Bäck T, Preuss M, Deutz A, et al., eds. <i>Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI)</i>. ; 2020:48–64. doi:<a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">10.1007/978-3-030-58112-1_4</a>","mla":"Seiler, Moritz, et al. “Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem.” <i>Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI)</i>, edited by Thomas Bäck et al., 2020, pp. 48–64, doi:<a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">10.1007/978-3-030-58112-1_4</a>.","chicago":"Seiler, Moritz, Janina Pohl, Jakob Bossek, Pascal Kerschke, and Heike Trautmann. “Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem.” In <i>Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI)</i>, edited by Thomas Bäck, Mike Preuss, André Deutz, Hao Wang, Carola Doerr, Michael Emmerich, and Heike Trautmann, 48–64. Leiden, The Netherlands, 2020. <a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">https://doi.org/10.1007/978-3-030-58112-1_4</a>.","short":"M. Seiler, J. Pohl, J. Bossek, P. Kerschke, H. Trautmann, in: T. Bäck, M. Preuss, A. Deutz, H. Wang, C. Doerr, M. Emmerich, H. Trautmann (Eds.), Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI), Leiden, The Netherlands, 2020, pp. 48–64.","ieee":"M. Seiler, J. Pohl, J. Bossek, P. Kerschke, and H. Trautmann, “Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem,” in <i>Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI)</i>, 2020, pp. 48–64, doi: <a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">10.1007/978-3-030-58112-1_4</a>.","apa":"Seiler, M., Pohl, J., Bossek, J., Kerschke, P., &#38; Trautmann, H. (2020). Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. In T. Bäck, M. Preuss, A. Deutz, H. Wang, C. Doerr, M. Emmerich, &#38; H. Trautmann (Eds.), <i>Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI)</i> (pp. 48–64). <a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">https://doi.org/10.1007/978-3-030-58112-1_4</a>"},"user_id":"15504","doi":"10.1007/978-3-030-58112-1_4","editor":[{"last_name":"Bäck","first_name":"Thomas","full_name":"Bäck, Thomas"},{"full_name":"Preuss, Mike","last_name":"Preuss","first_name":"Mike"},{"first_name":"André","last_name":"Deutz","full_name":"Deutz, André"},{"last_name":"Wang","first_name":"Hao","full_name":"Wang, Hao"},{"full_name":"Doerr, Carola","first_name":"Carola","last_name":"Doerr"},{"first_name":"Michael","last_name":"Emmerich","full_name":"Emmerich, Michael"},{"full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike"}],"page":"48–64","language":[{"iso":"eng"}],"_id":"46330","date_updated":"2024-06-10T11:57:13Z","title":"Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem","status":"public","year":"2020","author":[{"full_name":"Seiler, Moritz","first_name":"Moritz","last_name":"Seiler","id":"105520"},{"full_name":"Pohl, Janina","first_name":"Janina","last_name":"Pohl"},{"id":"102979","last_name":"Bossek","first_name":"Jakob","orcid":"0000-0002-4121-4668","full_name":"Bossek, Jakob"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"full_name":"Trautmann, Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike","id":"100740"}]},{"author":[{"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","orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike"}],"publication_identifier":{"issn":["1568-4946"]},"status":"public","title":"A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms","year":"2020","intvolume":"        88","date_updated":"2024-06-10T12:00:46Z","_id":"46334","language":[{"iso":"eng"}],"page":"105901","volume":88,"doi":"https://doi.org/10.1016/j.asoc.2019.105901","user_id":"15504","citation":{"mla":"Bossek, Jakob, et al. “A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms.” <i>Applied Soft Computing</i>, vol. 88, 2020, p. 105901, doi:<a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>.","ama":"Bossek J, Kerschke P, Trautmann H. A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms. <i>Applied Soft Computing</i>. 2020;88:105901. doi:<a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>","bibtex":"@article{Bossek_Kerschke_Trautmann_2020, title={A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms}, volume={88}, DOI={<a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>}, journal={Applied Soft Computing}, author={Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}, year={2020}, pages={105901} }","apa":"Bossek, J., Kerschke, P., &#38; Trautmann, H. (2020). A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms. <i>Applied Soft Computing</i>, <i>88</i>, 105901. <a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>","ieee":"J. Bossek, P. Kerschke, and H. Trautmann, “A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms,” <i>Applied Soft Computing</i>, vol. 88, p. 105901, 2020, doi: <a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>.","short":"J. Bossek, P. Kerschke, H. Trautmann, Applied Soft Computing 88 (2020) 105901.","chicago":"Bossek, Jakob, Pascal Kerschke, and Heike Trautmann. “A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms.” <i>Applied Soft Computing</i> 88 (2020): 105901. <a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>."},"publication":"Applied Soft Computing","abstract":[{"lang":"eng","text":"We build upon a recently proposed multi-objective view onto performance measurement of single-objective stochastic solvers. The trade-off between the fraction of failed runs and the mean runtime of successful runs – both to be minimized – is directly analyzed based on a study on algorithm selection of inexact state-of-the-art solvers for the famous Traveling Salesperson Problem (TSP). Moreover, we adopt the hypervolume indicator (HV) commonly used in multi-objective optimization for simultaneously assessing both conflicting objectives and investigate relations to commonly used performance indicators, both theoretically and empirically. Next to Penalized Average Runtime (PAR) and Penalized Quantile Runtime (PQR), the HV measure is used as a core concept within the construction of per-instance algorithm selection models offering interesting insights into complementary behavior of inexact TSP solvers."}],"date_created":"2023-08-04T07:42:26Z","department":[{"_id":"34"},{"_id":"819"}],"keyword":["Algorithm selection","Multi-objective optimization","Performance measurement","Combinatorial optimization","Traveling Salesperson Problem"],"type":"journal_article"},{"date_created":"2023-08-04T07:32:36Z","place":"Glasgow, UK","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","citation":{"chicago":"Bossek, Jakob, Christian Grimme, Günter Rudolph, and Heike Trautmann. “Towards Decision Support in Dynamic Bi-Objective Vehicle Routing.” In <i>Proceedings of the IEEE Congress on Evolutionary Computation (CEC)</i>, 1–8. Glasgow, UK, 2020. <a href=\"https://doi.org/10.1109/CEC48606.2020.9185778\">https://doi.org/10.1109/CEC48606.2020.9185778</a>.","short":"J. Bossek, C. Grimme, G. Rudolph, H. Trautmann, in: Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Glasgow, UK, 2020, pp. 1–8.","apa":"Bossek, J., Grimme, C., Rudolph, G., &#38; Trautmann, H. (2020). Towards Decision Support in Dynamic Bi-Objective Vehicle Routing. <i>Proceedings of the IEEE Congress on Evolutionary Computation (CEC)</i>, 1–8. <a href=\"https://doi.org/10.1109/CEC48606.2020.9185778\">https://doi.org/10.1109/CEC48606.2020.9185778</a>","ieee":"J. Bossek, C. Grimme, G. Rudolph, and H. Trautmann, “Towards Decision Support in Dynamic Bi-Objective Vehicle Routing,” in <i>Proceedings of the IEEE Congress on Evolutionary Computation (CEC)</i>, 2020, pp. 1–8, doi: <a href=\"https://doi.org/10.1109/CEC48606.2020.9185778\">10.1109/CEC48606.2020.9185778</a>.","ama":"Bossek J, Grimme C, Rudolph G, Trautmann H. Towards Decision Support in Dynamic Bi-Objective Vehicle Routing. In: <i>Proceedings of the IEEE Congress on Evolutionary Computation (CEC)</i>. ; 2020:1–8. doi:<a href=\"https://doi.org/10.1109/CEC48606.2020.9185778\">10.1109/CEC48606.2020.9185778</a>","bibtex":"@inproceedings{Bossek_Grimme_Rudolph_Trautmann_2020, place={Glasgow, UK}, title={Towards Decision Support in Dynamic Bi-Objective Vehicle Routing}, DOI={<a href=\"https://doi.org/10.1109/CEC48606.2020.9185778\">10.1109/CEC48606.2020.9185778</a>}, booktitle={Proceedings of the IEEE Congress on Evolutionary Computation (CEC)}, author={Bossek, Jakob and Grimme, Christian and Rudolph, Günter and Trautmann, Heike}, year={2020}, pages={1–8} }","mla":"Bossek, Jakob, et al. “Towards Decision Support in Dynamic Bi-Objective Vehicle Routing.” <i>Proceedings of the IEEE Congress on Evolutionary Computation (CEC)</i>, 2020, pp. 1–8, doi:<a href=\"https://doi.org/10.1109/CEC48606.2020.9185778\">10.1109/CEC48606.2020.9185778</a>."},"publication":"Proceedings of the IEEE Congress on Evolutionary Computation (CEC)","abstract":[{"lang":"eng","text":"We consider a dynamic bi-objective vehicle routing problem, where a subset of customers ask for service over time. Therein, the distance traveled by a single vehicle and the number of unserved dynamic requests is minimized by a dynamic evolutionary multi-objective algorithm (DEMOA), which operates on discrete time windows (eras). A decision is made at each era by a decision-maker, thus any decision depends on irreversible decisions made in foregoing eras. To understand effects of sequences of decision-making and interactions/dependencies between decisions made, we conduct a series of experiments. More precisely, we fix a set of decision-maker preferences D and the number of eras n t and analyze all |D| nt combinations of decision-maker options. We find that for random uniform instances (a) the final selected solutions mainly depend on the final decision and not on the decision history, (b) solutions are quite robust with respect to the number of unvisited dynamic customers, and (c) solutions of the dynamic approach can even dominate solutions obtained by a clairvoyant EMOA. In contrast, for instances with clustered customers, we observe a strong dependency on decision-making history as well as more variance in solution diversity."}],"_id":"46322","language":[{"iso":"eng"}],"page":"1–8","user_id":"15504","doi":"10.1109/CEC48606.2020.9185778","author":[{"id":"102979","orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob","full_name":"Bossek, Jakob"},{"first_name":"Christian","last_name":"Grimme","full_name":"Grimme, Christian"},{"last_name":"Rudolph","first_name":"Günter","full_name":"Rudolph, Günter"},{"id":"100740","full_name":"Trautmann, Heike","orcid":"0000-0002-9788-8282","first_name":"Heike","last_name":"Trautmann"}],"title":"Towards Decision Support in Dynamic Bi-Objective Vehicle Routing","year":"2020","status":"public","date_updated":"2024-06-10T12:02:05Z"},{"date_updated":"2024-06-10T12:01:46Z","title":"Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection","status":"public","year":"2020","author":[{"id":"102979","full_name":"Bossek, Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob"},{"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"}],"user_id":"15504","page":"1–8","_id":"46324","publisher":"IEEE","language":[{"iso":"eng"}],"abstract":[{"text":"The Traveling-Salesperson-Problem (TSP) is arguably one of the best-known NP-hard combinatorial optimization problems. The two sophisticated heuristic solvers LKH and EAX and respective (restart) variants manage to calculate close-to optimal or even optimal solutions, also for large instances with several thousand nodes in reasonable time. In this work we extend existing benchmarking studies by addressing anytime behaviour of inexact TSP solvers based on empirical runtime distributions leading to an increased understanding of solver behaviour and the respective relation to problem hardness. It turns out that performance ranking of solvers is highly dependent on the focused approximation quality. Insights on intersection points of performances offer huge potential for the construction of hybridized solvers depending on instance features. Moreover, instance features tailored to anytime performance and corresponding performance indicators will highly improve automated algorithm selection models by including comprehensive information on solver quality.","lang":"eng"}],"publication":"Proceedings of the IEEE Congress on Evolutionary Computation (CEC)","citation":{"bibtex":"@inproceedings{Bossek_Kerschke_Trautmann_2020, place={Glasgow, UK}, title={Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection}, booktitle={Proceedings of the IEEE Congress on Evolutionary Computation (CEC)}, publisher={IEEE}, author={Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}, year={2020}, pages={1–8} }","chicago":"Bossek, Jakob, Pascal Kerschke, and Heike Trautmann. “Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection.” In <i>Proceedings of the IEEE Congress on Evolutionary Computation (CEC)</i>, 1–8. Glasgow, UK: IEEE, 2020.","ama":"Bossek J, Kerschke P, Trautmann H. Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection. In: <i>Proceedings of the IEEE Congress on Evolutionary Computation (CEC)</i>. IEEE; 2020:1–8.","short":"J. Bossek, P. Kerschke, H. Trautmann, in: Proceedings of the IEEE Congress on Evolutionary Computation (CEC), IEEE, Glasgow, UK, 2020, pp. 1–8.","ieee":"J. Bossek, P. Kerschke, and H. Trautmann, “Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection,” in <i>Proceedings of the IEEE Congress on Evolutionary Computation (CEC)</i>, 2020, pp. 1–8.","apa":"Bossek, J., Kerschke, P., &#38; Trautmann, H. (2020). Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection. <i>Proceedings of the IEEE Congress on Evolutionary Computation (CEC)</i>, 1–8.","mla":"Bossek, Jakob, et al. “Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection.” <i>Proceedings of the IEEE Congress on Evolutionary Computation (CEC)</i>, IEEE, 2020, pp. 1–8."},"type":"conference","department":[{"_id":"34"},{"_id":"819"}],"place":"Glasgow, UK","date_created":"2023-08-04T07:34:40Z"},{"type":"conference","department":[{"_id":"34"},{"_id":"819"}],"place":"Cancun, Mexico","date_created":"2023-08-04T07:33:30Z","abstract":[{"lang":"eng","text":"In practice, e.g. in delivery and service scenarios, Vehicle-Routing-Problems (VRPs) often imply repeated decision making on dynamic customer requests. As in classical VRPs, tours have to be planned short while the number of serviced customers has to be maximized at the same time resulting in a multi-objective problem. Beyond that, however, dynamic requests lead to the need for re-planning of not yet realized tour parts, while already realized tour parts are irreversible. In this paper we study this type of bi-objective dynamic VRP including sequential decision making and concurrent realization of decisions. We adopt a recently proposed Dynamic Evolutionary Multi-Objective Algorithm (DEMOA) for a related VRP problem and extend it to the more realistic (here considered) scenario of multiple vehicles. We empirically show that our DEMOA is competitive with a multi-vehicle offline and clairvoyant variant of the proposed DEMOA as well as with the dynamic single-vehicle approach proposed earlier."}],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20)","citation":{"short":"J. Bossek, C. Grimme, H. Trautmann, in: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20), ACM, Cancun, Mexico, 2020, pp. 166–174.","chicago":"Bossek, Jakob, Christian Grimme, and Heike Trautmann. “Dynamic Bi-Objective Routing of Multiple Vehicles.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20)</i>, 166–174. Cancun, Mexico: ACM, 2020.","ieee":"J. Bossek, C. Grimme, and H. Trautmann, “Dynamic Bi-Objective Routing of Multiple Vehicles,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20)</i>, 2020, pp. 166–174.","apa":"Bossek, J., Grimme, C., &#38; Trautmann, H. (2020). Dynamic Bi-Objective Routing of Multiple Vehicles. <i>Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20)</i>, 166–174.","bibtex":"@inproceedings{Bossek_Grimme_Trautmann_2020, place={Cancun, Mexico}, title={Dynamic Bi-Objective Routing of Multiple Vehicles}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20)}, publisher={ACM}, author={Bossek, Jakob and Grimme, Christian and Trautmann, Heike}, year={2020}, pages={166–174} }","ama":"Bossek J, Grimme C, Trautmann H. Dynamic Bi-Objective Routing of Multiple Vehicles. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20)</i>. ACM; 2020:166–174.","mla":"Bossek, Jakob, et al. “Dynamic Bi-Objective Routing of Multiple Vehicles.” <i>Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20)</i>, ACM, 2020, pp. 166–174."},"user_id":"15504","page":"166–174","_id":"46323","publisher":"ACM","language":[{"iso":"eng"}],"date_updated":"2024-06-10T12:01:57Z","title":"Dynamic Bi-Objective Routing of Multiple Vehicles","year":"2020","status":"public","author":[{"full_name":"Bossek, Jakob","last_name":"Bossek","first_name":"Jakob","orcid":"0000-0002-4121-4668","id":"102979"},{"first_name":"Christian","last_name":"Grimme","full_name":"Grimme, Christian"},{"id":"100740","orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike","full_name":"Trautmann, Heike"}]},{"citation":{"mla":"Grimme, Christian, et al. “Multimodality in Multi-Objective Optimization — More Boon than Bane?” <i>Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO)</i>, edited by Kalyanmoy Deb et al., vol. 11411, Springer, 2019, pp. 126–138, doi:<a href=\"https://doi.org/10.1007/978-3-030-12598-1_11\">10.1007/978-3-030-12598-1_11</a>.","ama":"Grimme C, Kerschke P, Trautmann H. Multimodality in Multi-Objective Optimization — More Boon than Bane? In: Deb K, Goodman E, Coello CCA, et al., eds. <i>Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO)</i>. Vol 11411. Lecture Notes in Computer Science. Springer; 2019:126–138. doi:<a href=\"https://doi.org/10.1007/978-3-030-12598-1_11\">10.1007/978-3-030-12598-1_11</a>","bibtex":"@inproceedings{Grimme_Kerschke_Trautmann_2019, place={East Lansing, MI, USA}, series={Lecture Notes in Computer Science}, title={Multimodality in Multi-Objective Optimization — More Boon than Bane?}, volume={11411}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-12598-1_11\">10.1007/978-3-030-12598-1_11</a>}, booktitle={Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO)}, publisher={Springer}, author={Grimme, Christian and Kerschke, Pascal and Trautmann, Heike}, editor={Deb, Kalyanmoy and Goodman, Erik and Coello, Coello Carlos A. and Klamroth, Kathrin and Miettinen, Kaisa and Mostaghim, Sanaz and Reed, Patrick}, year={2019}, pages={126–138}, collection={Lecture Notes in Computer Science} }","apa":"Grimme, C., Kerschke, P., &#38; Trautmann, H. (2019). Multimodality in Multi-Objective Optimization — More Boon than Bane? In K. Deb, E. Goodman, C. C. A. Coello, K. Klamroth, K. Miettinen, S. Mostaghim, &#38; P. Reed (Eds.), <i>Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO)</i> (Vol. 11411, pp. 126–138). Springer. <a href=\"https://doi.org/10.1007/978-3-030-12598-1_11\">https://doi.org/10.1007/978-3-030-12598-1_11</a>","ieee":"C. Grimme, P. Kerschke, and H. Trautmann, “Multimodality in Multi-Objective Optimization — More Boon than Bane?,” in <i>Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO)</i>, 2019, vol. 11411, pp. 126–138, doi: <a href=\"https://doi.org/10.1007/978-3-030-12598-1_11\">10.1007/978-3-030-12598-1_11</a>.","short":"C. Grimme, P. Kerschke, H. Trautmann, in: K. Deb, E. Goodman, C.C.A. Coello, K. Klamroth, K. Miettinen, S. Mostaghim, P. Reed (Eds.), Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO), Springer, East Lansing, MI, USA, 2019, pp. 126–138.","chicago":"Grimme, Christian, Pascal Kerschke, and Heike Trautmann. “Multimodality in Multi-Objective Optimization — More Boon than Bane?” In <i>Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO)</i>, edited by Kalyanmoy Deb, Erik Goodman, Coello Carlos A. Coello, Kathrin Klamroth, Kaisa Miettinen, Sanaz Mostaghim, and Patrick Reed, 11411:126–138. Lecture Notes in Computer Science. East Lansing, MI, USA: Springer, 2019. <a href=\"https://doi.org/10.1007/978-3-030-12598-1_11\">https://doi.org/10.1007/978-3-030-12598-1_11</a>."},"place":"East Lansing, MI, USA","status":"public","page":"126–138","publisher":"Springer","_id":"46343","user_id":"15504","editor":[{"first_name":"Kalyanmoy","last_name":"Deb","full_name":"Deb, Kalyanmoy"},{"full_name":"Goodman, Erik","last_name":"Goodman","first_name":"Erik"},{"full_name":"Coello, Coello Carlos A.","first_name":"Coello Carlos A.","last_name":"Coello"},{"full_name":"Klamroth, Kathrin","last_name":"Klamroth","first_name":"Kathrin"},{"first_name":"Kaisa","last_name":"Miettinen","full_name":"Miettinen, Kaisa"},{"full_name":"Mostaghim, Sanaz","last_name":"Mostaghim","first_name":"Sanaz"},{"last_name":"Reed","first_name":"Patrick","full_name":"Reed, Patrick"}],"volume":11411,"publication":"Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO)","abstract":[{"lang":"eng","text":"This paper addresses multimodality of multi-objective (MO) optimization landscapes. Contrary to common perception of local optima, according to which they are hindering the progress of optimization algorithms, it will be shown that local efficient sets in a multi-objective setting can assist optimizers in finding global efficient sets. We use sophisticated visualization techniques, which rely on gradient field heatmaps, to highlight those insights into landscape characteristics. Finally, the MO local optimizer MOGSA is introduced, which exploits those observations by sliding down the multi-objective gradient hill and moving along the local efficient sets."}],"date_created":"2023-08-04T07:49:08Z","type":"conference","department":[{"_id":"34"},{"_id":"819"}],"year":"2019","title":"Multimodality in Multi-Objective Optimization — More Boon than Bane?","author":[{"full_name":"Grimme, Christian","first_name":"Christian","last_name":"Grimme"},{"last_name":"Kerschke","first_name":"Pascal","full_name":"Kerschke, Pascal"},{"id":"100740","first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike"}],"date_updated":"2023-10-16T13:31:03Z","intvolume":"     11411","language":[{"iso":"eng"}],"series_title":"Lecture Notes in Computer Science","doi":"10.1007/978-3-030-12598-1_11"},{"_id":"46345","language":[{"iso":"eng"}],"page":"3–45","volume":27,"doi":"10.1162/evco_a_00242","user_id":"15504","author":[{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"last_name":"Hoos","first_name":"Holger H","full_name":"Hoos, Holger H"},{"full_name":"Neumann, Frank","first_name":"Frank","last_name":"Neumann"},{"first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike","id":"100740"}],"year":"2019","status":"public","title":"Automated Algorithm Selection: Survey and Perspectives","intvolume":"        27","date_updated":"2023-10-16T13:31:40Z","date_created":"2023-08-04T07:50:33Z","department":[{"_id":"34"},{"_id":"819"}],"type":"journal_article","citation":{"chicago":"Kerschke, Pascal, Holger H Hoos, Frank Neumann, and Heike Trautmann. “Automated Algorithm Selection: Survey and Perspectives.” <i>Evolutionary Computation (ECJ)</i> 27, no. 1 (2019): 3–45. <a href=\"https://doi.org/10.1162/evco_a_00242\">https://doi.org/10.1162/evco_a_00242</a>.","short":"P. Kerschke, H.H. Hoos, F. Neumann, H. Trautmann, Evolutionary Computation (ECJ) 27 (2019) 3–45.","apa":"Kerschke, P., Hoos, H. H., Neumann, F., &#38; Trautmann, H. (2019). Automated Algorithm Selection: Survey and Perspectives. <i>Evolutionary Computation (ECJ)</i>, <i>27</i>(1), 3–45. <a href=\"https://doi.org/10.1162/evco_a_00242\">https://doi.org/10.1162/evco_a_00242</a>","ieee":"P. Kerschke, H. H. Hoos, F. Neumann, and H. Trautmann, “Automated Algorithm Selection: Survey and Perspectives,” <i>Evolutionary Computation (ECJ)</i>, vol. 27, no. 1, pp. 3–45, 2019, doi: <a href=\"https://doi.org/10.1162/evco_a_00242\">10.1162/evco_a_00242</a>.","ama":"Kerschke P, Hoos HH, Neumann F, Trautmann H. Automated Algorithm Selection: Survey and Perspectives. <i>Evolutionary Computation (ECJ)</i>. 2019;27(1):3–45. doi:<a href=\"https://doi.org/10.1162/evco_a_00242\">10.1162/evco_a_00242</a>","bibtex":"@article{Kerschke_Hoos_Neumann_Trautmann_2019, title={Automated Algorithm Selection: Survey and Perspectives}, volume={27}, DOI={<a href=\"https://doi.org/10.1162/evco_a_00242\">10.1162/evco_a_00242</a>}, number={1}, journal={Evolutionary Computation (ECJ)}, author={Kerschke, Pascal and Hoos, Holger H and Neumann, Frank and Trautmann, Heike}, year={2019}, pages={3–45} }","mla":"Kerschke, Pascal, et al. “Automated Algorithm Selection: Survey and Perspectives.” <i>Evolutionary Computation (ECJ)</i>, vol. 27, no. 1, 2019, pp. 3–45, doi:<a href=\"https://doi.org/10.1162/evco_a_00242\">10.1162/evco_a_00242</a>."},"issue":"1","publication":"Evolutionary Computation (ECJ)","abstract":[{"lang":"eng","text":"It has long been observed that for practically any computational problem that has been intensely studied, different instances are best solved using different algorithms. This is particularly pronounced for computationally hard problems, where in most cases, no single algorithm defines the state of the art; instead, there is a set of algorithms with complementary strengths. This performance complementarity can be exploited in various ways, one of which is based on the idea of selecting, from a set of given algorithms, for each problem instance to be solved the one expected to perform best. The task of automatically selecting an algorithm from a given set is known as the per-instance algorithm selection problem and has been intensely studied over the past 15 years, leading to major improvements in the state of the art in solving a growing number of discrete combinatorial problems, including propositional satisfiability and AI planning. Per-instance algorithm selection also shows much promise for boosting performance in solving continuous and mixed discrete/continuous optimisation problems. This survey provides an overview of research in automated algorithm selection, ranging from early and seminal works to recent and promising application areas. Different from earlier work, it covers applications to discrete and continuous problems, and discusses algorithm selection in context with conceptually related approaches, such as algorithm configuration, scheduling, or portfolio selection. Since informative and cheaply computable problem instance features provide the basis for effective per-instance algorithm selection systems, we also provide an overview of such features for discrete and continuous problems. Finally, we provide perspectives on future work in the area and discuss a number of open research challenges."}]},{"user_id":"15504","volume":61,"page":"277–297","_id":"46344","language":[{"iso":"eng"}],"date_updated":"2023-10-16T13:31:21Z","intvolume":"        61","status":"public","year":"2019","title":"Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms","author":[{"full_name":"Carnein, Matthias","last_name":"Carnein","first_name":"Matthias"},{"orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike","full_name":"Trautmann, Heike","id":"100740"}],"type":"journal_article","department":[{"_id":"34"},{"_id":"819"}],"date_created":"2023-08-04T07:49:47Z","abstract":[{"lang":"eng","text":"Analyzing data streams has received considerable attention over the past decades due to the widespread usage of sensors, social media and other streaming data sources. A core research area in this field is stream clustering which aims to recognize patterns in an unordered, infinite and evolving stream of observations. Clustering can be a crucial support in decision making, since it aims for an optimized aggregated representation of a continuous data stream over time and allows to identify patterns in large and high-dimensional data. A multitude of algorithms and approaches has been developed that are able to find and maintain clusters over time in the challenging streaming scenario. This survey explores, summarizes and categorizes a total of 51 stream clustering algorithms and identifies core research threads over the past decades. In particular, it identifies categories of algorithms based on distance thresholds, density grids and statistical models as well as algorithms for high dimensional data. Furthermore, it discusses applications scenarios, available software and how to configure stream clustering algorithms. This survey is considerably more extensive than comparable studies, more up-to-date and highlights how concepts are interrelated and have been developed over time."}],"publication":"Business and Information Systems Engineering (BISE)","issue":"3","citation":{"chicago":"Carnein, Matthias, and Heike Trautmann. “Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms.” <i>Business and Information Systems Engineering (BISE)</i> 61, no. 3 (2019): 277–297.","short":"M. Carnein, H. Trautmann, Business and Information Systems Engineering (BISE) 61 (2019) 277–297.","ama":"Carnein M, Trautmann H. Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms. <i>Business and Information Systems Engineering (BISE)</i>. 2019;61(3):277–297.","bibtex":"@article{Carnein_Trautmann_2019, title={Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms}, volume={61}, number={3}, journal={Business and Information Systems Engineering (BISE)}, author={Carnein, Matthias and Trautmann, Heike}, year={2019}, pages={277–297} }","apa":"Carnein, M., &#38; Trautmann, H. (2019). Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms. <i>Business and Information Systems Engineering (BISE)</i>, <i>61</i>(3), 277–297.","mla":"Carnein, Matthias, and Heike Trautmann. “Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms.” <i>Business and Information Systems Engineering (BISE)</i>, vol. 61, no. 3, 2019, pp. 277–297.","ieee":"M. Carnein and H. Trautmann, “Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms,” <i>Business and Information Systems Engineering (BISE)</i>, vol. 61, no. 3, pp. 277–297, 2019."}},{"date_updated":"2023-10-16T13:29:53Z","author":[{"first_name":"Matthias","last_name":"Carnein","full_name":"Carnein, Matthias"},{"full_name":"Homann, Leschek","last_name":"Homann","first_name":"Leschek"},{"full_name":"Trautmann, Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike","id":"100740"},{"full_name":"Vossen, Gottfried","last_name":"Vossen","first_name":"Gottfried"}],"title":"A Recommender System Based on Omni-Channel Customer Data","year":"2019","status":"public","user_id":"15504","_id":"46340","language":[{"iso":"eng"}],"page":"65–74","abstract":[{"text":"Recommender systems aim to provide personalized suggestions to customers which products to buy or services to consume. They can help to increase sales by helping customers discover new and relevant products. Traditionally, recommender systems use the purchase history of a customer, e.g., the purchased quantity or properties of the items. While this allows to build personalized recommendations, it is a very limited view of the problem. Nowadays, extensive information about customers and their personal preferences is available which goes far beyond their purchase behaviour. For example, customers reveal their preferences in social media, by their browsing habits and online search behaviour or their interest in specific newsletters. In this paper, we investigate how information from different sources and channels can be collected and incorporated into the recommendation process. We demonstrate this, based on a real-life case study of a retailer with several million transactions. We discuss how to employ a recommender system in this scenario, evaluate various recommendation strategies and describe how to incorporate information from different sources and channels, both internal and external. Our results show that the recommendations can be better tailored to the personal preferences of customers.","lang":"eng"}],"citation":{"bibtex":"@inproceedings{Carnein_Homann_Trautmann_Vossen_2019, place={Moscow, Russia}, title={A Recommender System Based on Omni-Channel Customer Data}, booktitle={Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19)}, author={Carnein, Matthias and Homann, Leschek and Trautmann, Heike and Vossen, Gottfried}, year={2019}, pages={65–74} }","ama":"Carnein M, Homann L, Trautmann H, Vossen G. A Recommender System Based on Omni-Channel Customer Data. In: <i>Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19)</i>. ; 2019:65–74.","mla":"Carnein, Matthias, et al. “A Recommender System Based on Omni-Channel Customer Data.” <i>Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19)</i>, 2019, pp. 65–74.","chicago":"Carnein, Matthias, Leschek Homann, Heike Trautmann, and Gottfried Vossen. “A Recommender System Based on Omni-Channel Customer Data.” In <i>Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19)</i>, 65–74. Moscow, Russia, 2019.","short":"M. Carnein, L. Homann, H. Trautmann, G. Vossen, in: Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19), Moscow, Russia, 2019, pp. 65–74.","ieee":"M. Carnein, L. Homann, H. Trautmann, and G. Vossen, “A Recommender System Based on Omni-Channel Customer Data,” in <i>Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19)</i>, 2019, pp. 65–74.","apa":"Carnein, M., Homann, L., Trautmann, H., &#38; Vossen, G. (2019). A Recommender System Based on Omni-Channel Customer Data. <i>Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19)</i>, 65–74."},"publication":"Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19)","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","place":"Moscow, Russia","date_created":"2023-08-04T07:46:20Z"},{"type":"conference","department":[{"_id":"34"},{"_id":"819"}],"date_created":"2023-08-04T07:47:20Z","place":"Macau, China","abstract":[{"lang":"eng","text":"Customer Segmentation aims to identify groups of customers that share similar interest or behaviour. It is an essential tool in marketing and can be used to target customer segments with tailored marketing strategies. Customer segmentation is often based on clustering techniques. This analysis is typically performed as a snapshot analysis where segments are identified at a specific point in time. However, this ignores the fact that customer segments are highly volatile and segments change over time. Once segments change, the entire analysis needs to be repeated and strategies adapted. In this paper we explore stream clustering as a tool to alleviate this problem. We propose a new stream clustering algorithm which allows to identify and track customer segments over time. The biggest challenge is that customer segmentation often relies on the transaction history of a customer. Since this data changes over time, it is necessary to update customers which have already been incorporated into the clustering. We show how to perform this step incrementally, without the need for periodic re-computations. As a result, customer segmentation can be performed continuously, faster and is more scalable. We demonstrate the performance of our algorithm using a large real-life case study."}],"publication":"Proceedings of the 23$^rd$ Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD ’19)","citation":{"mla":"Carnein, Matthias, and Heike Trautmann. “Customer Segmentation Based on Transactional Data Using Stream Clustering.” <i>Proceedings of the 23$^rd$ Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD ’19)</i>, 2019, pp. 280–292.","bibtex":"@inproceedings{Carnein_Trautmann_2019, place={Macau, China}, title={Customer Segmentation Based on Transactional Data Using Stream Clustering}, booktitle={Proceedings of the 23$^rd$ Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD ’19)}, author={Carnein, Matthias and Trautmann, Heike}, year={2019}, pages={280–292} }","ama":"Carnein M, Trautmann H. Customer Segmentation Based on Transactional Data Using Stream Clustering. In: <i>Proceedings of the 23$^rd$ Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD ’19)</i>. ; 2019:280–292.","ieee":"M. Carnein and H. Trautmann, “Customer Segmentation Based on Transactional Data Using Stream Clustering,” in <i>Proceedings of the 23$^rd$ Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD ’19)</i>, 2019, pp. 280–292.","apa":"Carnein, M., &#38; Trautmann, H. (2019). Customer Segmentation Based on Transactional Data Using Stream Clustering. <i>Proceedings of the 23$^rd$ Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD ’19)</i>, 280–292.","short":"M. Carnein, H. Trautmann, in: Proceedings of the 23$^rd$ Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD ’19), Macau, China, 2019, pp. 280–292.","chicago":"Carnein, Matthias, and Heike Trautmann. “Customer Segmentation Based on Transactional Data Using Stream Clustering.” In <i>Proceedings of the 23$^rd$ Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD ’19)</i>, 280–292. Macau, China, 2019."},"user_id":"15504","page":"280–292","language":[{"iso":"eng"}],"_id":"46341","date_updated":"2023-10-16T13:30:10Z","year":"2019","title":"Customer Segmentation Based on Transactional Data Using Stream Clustering","status":"public","author":[{"full_name":"Carnein, Matthias","first_name":"Matthias","last_name":"Carnein"},{"orcid":"0000-0002-9788-8282","first_name":"Heike","last_name":"Trautmann","full_name":"Trautmann, Heike","id":"100740"}]},{"date_updated":"2023-10-16T13:30:43Z","year":"2019","status":"public","title":"Sliding to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal Multi-Objective Optimization","author":[{"first_name":"Christian","last_name":"Grimme","full_name":"Grimme, Christian"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"full_name":"Emmerich, Michael T M","first_name":"Michael T M","last_name":"Emmerich"},{"last_name":"Preuss","first_name":"Mike","full_name":"Preuss, Mike"},{"full_name":"Deutz, André H","last_name":"Deutz","first_name":"André H"},{"id":"100740","full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike","orcid":"0000-0002-9788-8282"}],"user_id":"15504","doi":"10.1063/1.5090019","page":"020052-1-020052-4","language":[{"iso":"eng"}],"_id":"46342","publisher":"AIP Publishing","abstract":[{"text":"There is a range of phenomena in continuous, global multi-objective optimization, that cannot occur in single-objective optimization. For instance, in some multi-objective optimization problems it is possible to follow continuous paths of gradients of straightforward weighted scalarization functions, starting from locally efficient solutions, in order to reach globally Pareto optimal solutions. This paper seeks to better characterize multimodal multi-objective landscapes and to better understand the transitions from local optima to global optima in simple, path-oriented search procedures.","lang":"eng"}],"publication":"AIP Conference Proceedings","citation":{"ama":"Grimme C, Kerschke P, Emmerich MTM, Preuss M, Deutz AH, Trautmann H. Sliding to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal Multi-Objective Optimization. In: <i>AIP Conference Proceedings</i>. AIP Publishing; 2019:020052-1-020052-020054. doi:<a href=\"https://doi.org/10.1063/1.5090019\">10.1063/1.5090019</a>","bibtex":"@inproceedings{Grimme_Kerschke_Emmerich_Preuss_Deutz_Trautmann_2019, place={Leiden, The Netherlands}, title={Sliding to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal Multi-Objective Optimization}, DOI={<a href=\"https://doi.org/10.1063/1.5090019\">10.1063/1.5090019</a>}, booktitle={AIP Conference Proceedings}, publisher={AIP Publishing}, author={Grimme, Christian and Kerschke, Pascal and Emmerich, Michael T M and Preuss, Mike and Deutz, André H and Trautmann, Heike}, year={2019}, pages={020052-1-020052–4} }","mla":"Grimme, Christian, et al. “Sliding to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal Multi-Objective Optimization.” <i>AIP Conference Proceedings</i>, AIP Publishing, 2019, pp. 020052-1-020052–54, doi:<a href=\"https://doi.org/10.1063/1.5090019\">10.1063/1.5090019</a>.","short":"C. Grimme, P. Kerschke, M.T.M. Emmerich, M. Preuss, A.H. Deutz, H. Trautmann, in: AIP Conference Proceedings, AIP Publishing, Leiden, The Netherlands, 2019, pp. 020052-1-020052–4.","chicago":"Grimme, Christian, Pascal Kerschke, Michael T M Emmerich, Mike Preuss, André H Deutz, and Heike Trautmann. “Sliding to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal Multi-Objective Optimization.” In <i>AIP Conference Proceedings</i>, 020052-1-020052–54. Leiden, The Netherlands: AIP Publishing, 2019. <a href=\"https://doi.org/10.1063/1.5090019\">https://doi.org/10.1063/1.5090019</a>.","apa":"Grimme, C., Kerschke, P., Emmerich, M. T. M., Preuss, M., Deutz, A. H., &#38; Trautmann, H. (2019). Sliding to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal Multi-Objective Optimization. <i>AIP Conference Proceedings</i>, 020052-1-020052–020054. <a href=\"https://doi.org/10.1063/1.5090019\">https://doi.org/10.1063/1.5090019</a>","ieee":"C. Grimme, P. Kerschke, M. T. M. Emmerich, M. Preuss, A. H. Deutz, and H. Trautmann, “Sliding to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal Multi-Objective Optimization,” in <i>AIP Conference Proceedings</i>, 2019, pp. 020052-1-020052–4, doi: <a href=\"https://doi.org/10.1063/1.5090019\">10.1063/1.5090019</a>."},"type":"conference","department":[{"_id":"34"},{"_id":"819"}],"date_created":"2023-08-04T07:48:15Z","place":"Leiden, The Netherlands"},{"editor":[{"full_name":"Bauer, Nadja","first_name":"Nadja","last_name":"Bauer"},{"last_name":"Ickstadt","first_name":"Katja","full_name":"Ickstadt, Katja"},{"first_name":"Karsten","last_name":"Lübke","full_name":"Lübke, Karsten"},{"last_name":"Szepannek","first_name":"Gero","full_name":"Szepannek, Gero"},{"full_name":"Trautmann, Heike","first_name":"Heike","last_name":"Trautmann"},{"last_name":"Vichi","first_name":"Maurizio","full_name":"Vichi, Maurizio"}],"user_id":"15504","doi":"10.1007/978-3-030-25147-5_7","_id":"46336","language":[{"iso":"eng"}],"publisher":"Springer","page":"93–123","date_updated":"2023-10-16T13:08:22Z","author":[{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"},{"id":"100740","first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","full_name":"Trautmann, Heike"}],"status":"public","title":"Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-package flacco","year":"2019","department":[{"_id":"34"},{"_id":"819"}],"type":"book_chapter","date_created":"2023-08-04T07:43:30Z","abstract":[{"lang":"eng","text":"Choosing the best-performing optimizer(s) out of a portfolio of optimization algorithms is usually a difficult and complex task. It gets even worse, if the underlying functions are unknown, i.e., so-called black-box problems, and function evaluations are considered to be expensive. In case of continuous single-objective optimization problems, exploratory landscape analysis (ELA), a sophisticated and effective approach for characterizing the landscapes of such problems by means of numerical values before actually performing the optimization task itself, is advantageous. Unfortunately, until now it has been quite complicated to compute multiple ELA features simultaneously, as the corresponding code has been—if at all—spread across multiple platforms or at least across several packages within these platforms. This article presents a broad summary of existing ELA approaches and introduces flacco, an R-package for feature-based landscape analysis of continuous and constrained optimization problems. Although its functions neither solve the optimization problem itself nor the related algorithm selection problem (ASP), it offers easy access to an essential ingredient of the ASP by providing a wide collection of ELA features on a single platform—even within a single package. In addition, flacco provides multiple visualization techniques, which enhance the understanding of some of these numerical features, and thereby make certain landscape properties more comprehensible. On top of that, we will introduce the package’s built-in, as well as web-hosted and hence platform-independent, graphical user interface (GUI). It facilitates the usage of the package—especially for people who are not familiar with R—and thus makes flacco a very convenient toolbox when working towards algorithm selection of continuous single-objective optimization problems."}],"citation":{"chicago":"Kerschke, Pascal, and Heike Trautmann. “Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package Flacco.” In <i>Applications in Statistical Computing</i>, edited by Nadja Bauer, Katja Ickstadt, Karsten Lübke, Gero Szepannek, Heike Trautmann, and Maurizio Vichi, 93–123. Springer, 2019. <a href=\"https://doi.org/10.1007/978-3-030-25147-5_7\">https://doi.org/10.1007/978-3-030-25147-5_7</a>.","short":"P. Kerschke, H. Trautmann, in: N. Bauer, K. Ickstadt, K. Lübke, G. Szepannek, H. Trautmann, M. Vichi (Eds.), Applications in Statistical Computing, Springer, 2019, pp. 93–123.","ieee":"P. Kerschke and H. Trautmann, “Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-package flacco,” in <i>Applications in Statistical Computing</i>, N. Bauer, K. Ickstadt, K. Lübke, G. Szepannek, H. Trautmann, and M. Vichi, Eds. Springer, 2019, pp. 93–123.","apa":"Kerschke, P., &#38; Trautmann, H. (2019). Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-package flacco. In N. Bauer, K. Ickstadt, K. Lübke, G. Szepannek, H. Trautmann, &#38; M. Vichi (Eds.), <i>Applications in Statistical Computing</i> (pp. 93–123). Springer. <a href=\"https://doi.org/10.1007/978-3-030-25147-5_7\">https://doi.org/10.1007/978-3-030-25147-5_7</a>","bibtex":"@inbook{Kerschke_Trautmann_2019, title={Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-package flacco}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-25147-5_7\">10.1007/978-3-030-25147-5_7</a>}, booktitle={Applications in Statistical Computing}, publisher={Springer}, author={Kerschke, Pascal and Trautmann, Heike}, editor={Bauer, Nadja and Ickstadt, Katja and Lübke, Karsten and Szepannek, Gero and Trautmann, Heike and Vichi, Maurizio}, year={2019}, pages={93–123} }","ama":"Kerschke P, Trautmann H. Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-package flacco. In: Bauer N, Ickstadt K, Lübke K, Szepannek G, Trautmann H, Vichi M, eds. <i>Applications in Statistical Computing</i>. Springer; 2019:93–123. doi:<a href=\"https://doi.org/10.1007/978-3-030-25147-5_7\">10.1007/978-3-030-25147-5_7</a>","mla":"Kerschke, Pascal, and Heike Trautmann. “Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package Flacco.” <i>Applications in Statistical Computing</i>, edited by Nadja Bauer et al., Springer, 2019, pp. 93–123, doi:<a href=\"https://doi.org/10.1007/978-3-030-25147-5_7\">10.1007/978-3-030-25147-5_7</a>."},"publication":"Applications in Statistical Computing"}]