[{"department":[{"_id":"34"},{"_id":"819"}],"type":"conference","date_created":"2023-08-04T07:31:13Z","place":"Wiesbaden","abstract":[{"lang":"eng","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."}],"citation":{"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.","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.","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} }","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."},"publication":"Disinformation in open online media","editor":[{"full_name":"Grimme, Christian","last_name":"Grimme","first_name":"Christian"},{"last_name":"Preuß","first_name":"Mike","full_name":"Preuß, Mike"},{"last_name":"Takes","first_name":"Frank","full_name":"Takes, Frank"},{"first_name":"Annie","last_name":"Waldherr","full_name":"Waldherr, Annie"}],"user_id":"15504","_id":"46321","language":[{"iso":"eng"}],"series_title":"Lecture Notes in Computer Science","publisher":"Springer","page":"101–114","date_updated":"2023-10-16T13:00:15Z","author":[{"first_name":"Dennis","last_name":"Assenmacher","full_name":"Assenmacher, Dennis"},{"first_name":"Lena","last_name":"Frischlich ","full_name":"Frischlich , Lena"},{"id":"100740","orcid":"0000-0002-9788-8282","first_name":"Heike","last_name":"Trautmann","full_name":"Trautmann, Heike"},{"last_name":"Grimme","first_name":"Christian","full_name":"Grimme, Christian"},{"full_name":"Adam, Lena","last_name":"Adam","first_name":"Lena"}],"status":"public","title":"Inside the tool set of automation: Free social bot code revisited","year":"2020"},{"page":"80–95","language":[{"iso":"eng"}],"_id":"46326","user_id":"15504","doi":"10.1007/978-3-030-53552-0_10","title":"confStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms","year":"2020","status":"public","author":[{"full_name":"Carnein, Matthias","last_name":"Carnein","first_name":"Matthias"},{"id":"100740","full_name":"Trautmann, Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike"},{"full_name":"Bifet, Albert","first_name":"Albert","last_name":"Bifet"},{"full_name":"Pfahringer, Bernhard","first_name":"Bernhard","last_name":"Pfahringer"}],"date_updated":"2023-10-16T13:03:36Z","date_created":"2023-08-04T07:36:03Z","place":"Athens, Greece","type":"conference","department":[{"_id":"34"},{"_id":"819"}],"publication":"Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020)","citation":{"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>.","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>","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>.","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.","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>.","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} }","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>"},"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"}]},{"author":[{"full_name":"Lena, Clever","first_name":"Clever","last_name":"Lena"},{"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"},{"full_name":"Grimme, Christian","first_name":"Christian","last_name":"Grimme"}],"year":"2020","status":"public","title":"Automated detection of nostalgic text in the context of societal pessimism","date_updated":"2023-10-16T13:03:56Z","_id":"46327","language":[{"iso":"eng"}],"page":"48–58","editor":[{"full_name":"Grimme, Christian","first_name":"Christian","last_name":"Grimme"},{"full_name":"Preuß, Mike","first_name":"Mike","last_name":"Preuß"},{"full_name":"Takes, Frank","first_name":"Frank","last_name":"Takes"},{"first_name":"Annie","last_name":"Waldherr","full_name":"Waldherr, Annie"}],"user_id":"15504","citation":{"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} }","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.","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.","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."},"publication":"Disinformation in open online media","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"}],"place":"Hamburg, Deutschland","date_created":"2023-08-04T07:36:43Z","department":[{"_id":"34"},{"_id":"819"}],"type":"conference"},{"date_created":"2023-08-04T07:38:42Z","place":"Cham","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","citation":{"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.","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} }","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.","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.","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.","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."},"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."}],"_id":"46329","publisher":"Springer International Publishing","language":[{"iso":"eng"}],"page":"71–86","editor":[{"full_name":"Meiselwitz, Gabriele","last_name":"Meiselwitz","first_name":"Gabriele"}],"user_id":"15504","publication_identifier":{"isbn":["978-3-030-49576-3"]},"author":[{"full_name":"Riehle, Dennis M.","first_name":"Dennis M.","last_name":"Riehle"},{"first_name":"Marco","last_name":"Niemann","full_name":"Niemann, Marco"},{"full_name":"Brunk, Jens","first_name":"Jens","last_name":"Brunk"},{"full_name":"Assenmacher, Dennis","first_name":"Dennis","last_name":"Assenmacher"},{"orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike","full_name":"Trautmann, Heike","id":"100740"},{"first_name":"Jörg","last_name":"Becker","full_name":"Becker, 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"},{"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":[{"full_name":"Assenmacher, Dennis","last_name":"Assenmacher","first_name":"Dennis"},{"first_name":"Lena","last_name":"Clever","full_name":"Clever, Lena"},{"full_name":"Frischlich, Lena","first_name":"Lena","last_name":"Frischlich"},{"last_name":"Quandt","first_name":"Thorsten","full_name":"Quandt, Thorsten"},{"id":"100740","full_name":"Trautmann, Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","first_name":"Heike"},{"full_name":"Grimme, Christian","last_name":"Grimme","first_name":"Christian"}],"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":{"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>.","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>"},"publication":"Social Media + Society","issue":"3"},{"date_created":"2023-08-04T07:40:33Z","place":"Canberra, Australia","type":"conference","department":[{"_id":"34"},{"_id":"819"}],"publication":"Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)","citation":{"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>.","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} }","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>","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>.","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>","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."},"abstract":[{"lang":"eng","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."}],"page":"2445–2452","language":[{"iso":"eng"}],"_id":"46332","user_id":"15504","doi":"10.1109/SSCI47803.2020.9308259","title":"Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent","status":"public","year":"2020","author":[{"first_name":"Vera","last_name":"Steinhoff","full_name":"Steinhoff, Vera"},{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"},{"first_name":"Pelin","last_name":"Aspar","full_name":"Aspar, Pelin"},{"first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike","id":"100740"},{"last_name":"Grimme","first_name":"Christian","full_name":"Grimme, Christian"}],"date_updated":"2023-10-16T13:05:49Z"},{"status":"public","user_id":"102979","page":"1277–1285","_id":"48847","publisher":"Association for Computing Machinery","citation":{"ama":"Bossek J, Neumann F, Peng P, Sudholt D. More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO ’20. Association for Computing Machinery; 2020:1277–1285. doi:<a href=\"https://doi.org/10.1145/3377930.3390174\">10.1145/3377930.3390174</a>","bibtex":"@inproceedings{Bossek_Neumann_Peng_Sudholt_2020, place={New York, NY, USA}, series={GECCO ’20}, title={More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization}, DOI={<a href=\"https://doi.org/10.1145/3377930.3390174\">10.1145/3377930.3390174</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Neumann, Frank and Peng, Pan and Sudholt, Dirk}, year={2020}, pages={1277–1285}, collection={GECCO ’20} }","mla":"Bossek, Jakob, et al. “More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2020, pp. 1277–1285, doi:<a href=\"https://doi.org/10.1145/3377930.3390174\">10.1145/3377930.3390174</a>.","short":"J. Bossek, F. Neumann, P. Peng, D. Sudholt, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2020, pp. 1277–1285.","chicago":"Bossek, Jakob, Frank Neumann, Pan Peng, and Dirk Sudholt. “More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 1277–1285. GECCO ’20. New York, NY, USA: Association for Computing Machinery, 2020. <a href=\"https://doi.org/10.1145/3377930.3390174\">https://doi.org/10.1145/3377930.3390174</a>.","apa":"Bossek, J., Neumann, F., Peng, P., &#38; Sudholt, D. (2020). More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 1277–1285. <a href=\"https://doi.org/10.1145/3377930.3390174\">https://doi.org/10.1145/3377930.3390174</a>","ieee":"J. Bossek, F. Neumann, P. Peng, and D. Sudholt, “More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2020, pp. 1277–1285, doi: <a href=\"https://doi.org/10.1145/3377930.3390174\">10.1145/3377930.3390174</a>."},"place":"New York, NY, USA","date_updated":"2023-12-13T10:43:41Z","publication_status":"published","title":"More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization","year":"2020","author":[{"id":"102979","orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob","full_name":"Bossek, Jakob"},{"last_name":"Neumann","first_name":"Frank","full_name":"Neumann, Frank"},{"first_name":"Pan","last_name":"Peng","full_name":"Peng, Pan"},{"first_name":"Dirk","last_name":"Sudholt","full_name":"Sudholt, Dirk"}],"publication_identifier":{"isbn":["978-1-4503-7128-5"]},"doi":"10.1145/3377930.3390174","language":[{"iso":"eng"}],"series_title":"GECCO ’20","abstract":[{"text":"Dynamic optimization problems have gained significant attention in evolutionary computation as evolutionary algorithms (EAs) can easily adapt to changing environments. We show that EAs can solve the graph coloring problem for bipartite graphs more efficiently by using dynamic optimization. In our approach the graph instance is given incrementally such that the EA can reoptimize its coloring when a new edge introduces a conflict. We show that, when edges are inserted in a way that preserves graph connectivity, Randomized Local Search (RLS) efficiently finds a proper 2-coloring for all bipartite graphs. This includes graphs for which RLS and other EAs need exponential expected time in a static optimization scenario. We investigate different ways of building up the graph by popular graph traversals such as breadth-first-search and depth-first-search and analyse the resulting runtime behavior. We further show that offspring populations (e. g. a (1 + {$\\lambda$}) RLS) lead to an exponential speedup in {$\\lambda$}. Finally, an island model using 3 islands succeeds in an optimal time of {$\\Theta$}(m) on every m-edge bipartite graph, outperforming offspring populations. This is the first example where an island model guarantees a speedup that is not bounded in the number of islands.","lang":"eng"}],"extern":"1","publication":"Proceedings of the Genetic and Evolutionary Computation Conference","type":"conference","keyword":["dynamic optimization","evolutionary algorithms","running time analysis","theory"],"department":[{"_id":"819"}],"date_created":"2023-11-14T15:58:53Z"},{"publication_status":"published","date_updated":"2023-12-13T10:43:53Z","author":[{"last_name":"Bossek","first_name":"Jakob","orcid":"0000-0002-4121-4668","full_name":"Bossek, Jakob","id":"102979"},{"last_name":"Doerr","first_name":"Carola","full_name":"Doerr, Carola"},{"full_name":"Kerschke, Pascal","first_name":"Pascal","last_name":"Kerschke"},{"full_name":"Neumann, Aneta","last_name":"Neumann","first_name":"Aneta"},{"full_name":"Neumann, Frank","first_name":"Frank","last_name":"Neumann"}],"publication_identifier":{"isbn":["978-3-030-58111-4"]},"year":"2020","title":"Evolving Sampling Strategies for One-Shot Optimization Tasks","doi":"10.1007/978-3-030-58112-1_8","language":[{"iso":"eng"}],"extern":"1","abstract":[{"text":"One-shot optimization tasks require to determine the set of solution candidates prior to their evaluation, i.e., without possibility for adaptive sampling. We consider two variants, classic one-shot optimization (where our aim is to find at least one solution of high quality) and one-shot regression (where the goal is to fit a model that resembles the true problem as well as possible). For both tasks it seems intuitive that well-distributed samples should perform better than uniform or grid-based samples, since they show a better coverage of the decision space. In practice, quasi-random designs such as Latin Hypercube Samples and low-discrepancy point sets are indeed very commonly used designs for one-shot optimization tasks. We study in this work how well low star discrepancy correlates with performance in one-shot optimization. Our results confirm an advantage of low-discrepancy designs, but also indicate the correlation between discrepancy values and overall performance is rather weak. We then demonstrate that commonly used designs may be far from optimal. More precisely, we evolve 24 very specific designs that each achieve good performance on one of our benchmark problems. Interestingly, we find that these specifically designed samples yield surprisingly good performance across the whole benchmark set. Our results therefore give strong indication that significant performance gains over state-of-the-art one-shot sampling techniques are possible, and that evolutionary algorithms can be an efficient means to evolve these.","lang":"eng"}],"publication":"Parallel Problem Solving from Nature (PPSN XVI)","department":[{"_id":"819"}],"keyword":["Continuous optimization","Fully parallel search","One-shot optimization","Regression","Surrogate-assisted optimization"],"type":"conference","date_created":"2023-11-14T15:58:53Z","status":"public","user_id":"102979","publisher":"Springer-Verlag","_id":"48849","page":"111–124","citation":{"ama":"Bossek J, Doerr C, Kerschke P, Neumann A, Neumann F. Evolving Sampling Strategies for One-Shot Optimization Tasks. In: <i>Parallel Problem Solving from Nature (PPSN XVI)</i>. Springer-Verlag; 2020:111–124. doi:<a href=\"https://doi.org/10.1007/978-3-030-58112-1_8\">10.1007/978-3-030-58112-1_8</a>","bibtex":"@inproceedings{Bossek_Doerr_Kerschke_Neumann_Neumann_2020, place={Berlin, Heidelberg}, title={Evolving Sampling Strategies for One-Shot Optimization Tasks}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-58112-1_8\">10.1007/978-3-030-58112-1_8</a>}, booktitle={Parallel Problem Solving from Nature (PPSN XVI)}, publisher={Springer-Verlag}, author={Bossek, Jakob and Doerr, Carola and Kerschke, Pascal and Neumann, Aneta and Neumann, Frank}, year={2020}, pages={111–124} }","mla":"Bossek, Jakob, et al. “Evolving Sampling Strategies for One-Shot Optimization Tasks.” <i>Parallel Problem Solving from Nature (PPSN XVI)</i>, Springer-Verlag, 2020, pp. 111–124, doi:<a href=\"https://doi.org/10.1007/978-3-030-58112-1_8\">10.1007/978-3-030-58112-1_8</a>.","short":"J. Bossek, C. Doerr, P. Kerschke, A. Neumann, F. Neumann, in: Parallel Problem Solving from Nature (PPSN XVI), Springer-Verlag, Berlin, Heidelberg, 2020, pp. 111–124.","chicago":"Bossek, Jakob, Carola Doerr, Pascal Kerschke, Aneta Neumann, and Frank Neumann. “Evolving Sampling Strategies for One-Shot Optimization Tasks.” In <i>Parallel Problem Solving from Nature (PPSN XVI)</i>, 111–124. Berlin, Heidelberg: Springer-Verlag, 2020. <a href=\"https://doi.org/10.1007/978-3-030-58112-1_8\">https://doi.org/10.1007/978-3-030-58112-1_8</a>.","apa":"Bossek, J., Doerr, C., Kerschke, P., Neumann, A., &#38; Neumann, F. (2020). Evolving Sampling Strategies for One-Shot Optimization Tasks. <i>Parallel Problem Solving from Nature (PPSN XVI)</i>, 111–124. <a href=\"https://doi.org/10.1007/978-3-030-58112-1_8\">https://doi.org/10.1007/978-3-030-58112-1_8</a>","ieee":"J. Bossek, C. Doerr, P. Kerschke, A. Neumann, and F. Neumann, “Evolving Sampling Strategies for One-Shot Optimization Tasks,” in <i>Parallel Problem Solving from Nature (PPSN XVI)</i>, 2020, pp. 111–124, doi: <a href=\"https://doi.org/10.1007/978-3-030-58112-1_8\">10.1007/978-3-030-58112-1_8</a>."},"place":"Berlin, Heidelberg"},{"status":"public","publisher":"Association for Computing Machinery","_id":"48851","page":"1286–1294","user_id":"102979","citation":{"mla":"Bossek, Jakob, et al. “The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2020, pp. 1286–1294, doi:<a href=\"https://doi.org/10.1145/3377930.3390243\">10.1145/3377930.3390243</a>.","bibtex":"@inproceedings{Bossek_Casel_Kerschke_Neumann_2020, place={New York, NY, USA}, series={GECCO ’20}, title={The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics}, DOI={<a href=\"https://doi.org/10.1145/3377930.3390243\">10.1145/3377930.3390243</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Casel, Katrin and Kerschke, Pascal and Neumann, Frank}, year={2020}, pages={1286–1294}, collection={GECCO ’20} }","ama":"Bossek J, Casel K, Kerschke P, Neumann F. The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO ’20. Association for Computing Machinery; 2020:1286–1294. doi:<a href=\"https://doi.org/10.1145/3377930.3390243\">10.1145/3377930.3390243</a>","ieee":"J. Bossek, K. Casel, P. Kerschke, and F. Neumann, “The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2020, pp. 1286–1294, doi: <a href=\"https://doi.org/10.1145/3377930.3390243\">10.1145/3377930.3390243</a>.","apa":"Bossek, J., Casel, K., Kerschke, P., &#38; Neumann, F. (2020). The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 1286–1294. <a href=\"https://doi.org/10.1145/3377930.3390243\">https://doi.org/10.1145/3377930.3390243</a>","short":"J. Bossek, K. Casel, P. Kerschke, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2020, pp. 1286–1294.","chicago":"Bossek, Jakob, Katrin Casel, Pascal Kerschke, and Frank Neumann. “The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 1286–1294. GECCO ’20. New York, NY, USA: Association for Computing Machinery, 2020. <a href=\"https://doi.org/10.1145/3377930.3390243\">https://doi.org/10.1145/3377930.3390243</a>."},"place":"New York, NY, USA","publication_identifier":{"isbn":["978-1-4503-7128-5"]},"author":[{"id":"102979","full_name":"Bossek, Jakob","first_name":"Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668"},{"last_name":"Casel","first_name":"Katrin","full_name":"Casel, Katrin"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"full_name":"Neumann, Frank","first_name":"Frank","last_name":"Neumann"}],"title":"The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics","year":"2020","date_updated":"2023-12-13T10:43:33Z","publication_status":"published","language":[{"iso":"eng"}],"series_title":"GECCO ’20","doi":"10.1145/3377930.3390243","publication":"Proceedings of the Genetic and Evolutionary Computation Conference","abstract":[{"lang":"eng","text":"Several important optimization problems in the area of vehicle routing can be seen as variants of the classical Traveling Salesperson Problem (TSP). In the area of evolutionary computation, the Traveling Thief Problem (TTP) has gained increasing interest over the last 5 years. In this paper, we investigate the effect of weights on such problems, in the sense that the cost of traveling increases with respect to the weights of nodes already visited during a tour. This provides abstractions of important TSP variants such as the Traveling Thief Problem and time dependent TSP variants, and allows to study precisely the increase in difficulty caused by weight dependence. We provide a 3.59-approximation for this weight dependent version of TSP with metric distances and bounded positive weights. Furthermore, we conduct experimental investigations for simple randomized local search with classical mutation operators and two variants of the state-of-the-art evolutionary algorithm EAX adapted to the weighted TSP. Our results show the impact of the node weights on the position of the nodes in the resulting tour."}],"extern":"1","date_created":"2023-11-14T15:58:53Z","department":[{"_id":"819"}],"keyword":["dynamic optimization","evolutionary algorithms","running time analysis","theory"],"type":"conference"},{"publication":"Proceedings of the Genetic and Evolutionary Computation Conference","extern":"1","abstract":[{"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.","lang":"eng"}],"date_created":"2023-11-14T15:58:52Z","department":[{"_id":"819"}],"type":"conference","keyword":["decision making","dynamic optimization","evolutionary algorithms","multi-objective optimization","vehicle routing"],"publication_identifier":{"isbn":["978-1-4503-7128-5"]},"author":[{"id":"102979","last_name":"Bossek","first_name":"Jakob","orcid":"0000-0002-4121-4668","full_name":"Bossek, Jakob"},{"full_name":"Grimme, Christian","last_name":"Grimme","first_name":"Christian"},{"last_name":"Trautmann","first_name":"Heike","full_name":"Trautmann, Heike"}],"title":"Dynamic Bi-Objective Routing of Multiple Vehicles","year":"2020","publication_status":"published","date_updated":"2023-12-13T10:43:24Z","language":[{"iso":"eng"}],"series_title":"GECCO ’20","doi":"10.1145/3377930.3390146","citation":{"mla":"Bossek, Jakob, et al. “Dynamic Bi-Objective Routing of Multiple Vehicles.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2020, pp. 166–174, doi:<a href=\"https://doi.org/10.1145/3377930.3390146\">10.1145/3377930.3390146</a>.","ama":"Bossek J, Grimme C, Trautmann H. Dynamic Bi-Objective Routing of Multiple Vehicles. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO ’20. Association for Computing Machinery; 2020:166–174. doi:<a href=\"https://doi.org/10.1145/3377930.3390146\">10.1145/3377930.3390146</a>","bibtex":"@inproceedings{Bossek_Grimme_Trautmann_2020, place={New York, NY, USA}, series={GECCO ’20}, title={Dynamic Bi-Objective Routing of Multiple Vehicles}, DOI={<a href=\"https://doi.org/10.1145/3377930.3390146\">10.1145/3377930.3390146</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Grimme, Christian and Trautmann, Heike}, year={2020}, pages={166–174}, collection={GECCO ’20} }","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</i>, 166–174. <a href=\"https://doi.org/10.1145/3377930.3390146\">https://doi.org/10.1145/3377930.3390146</a>","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</i>, 2020, pp. 166–174, doi: <a href=\"https://doi.org/10.1145/3377930.3390146\">10.1145/3377930.3390146</a>.","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</i>, 166–174. GECCO ’20. New York, NY, USA: Association for Computing Machinery, 2020. <a href=\"https://doi.org/10.1145/3377930.3390146\">https://doi.org/10.1145/3377930.3390146</a>.","short":"J. Bossek, C. Grimme, H. Trautmann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2020, pp. 166–174."},"place":"New York, NY, USA","status":"public","publisher":"Association for Computing Machinery","_id":"48845","page":"166–174","user_id":"102979"},{"department":[{"_id":"819"}],"type":"conference","date_created":"2023-11-14T15:58:52Z","place":"Glasgow, United Kingdom","extern":"1","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"}],"citation":{"chicago":"Bossek, Jakob, Pascal Kerschke, and Heike Trautmann. “Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection.” In <i>2020 IEEE Congress on Evolutionary Computation (CEC)</i>, 1–8. Glasgow, United Kingdom: IEEE Press, 2020. <a href=\"https://doi.org/10.1109/CEC48606.2020.9185613\">https://doi.org/10.1109/CEC48606.2020.9185613</a>.","short":"J. Bossek, P. Kerschke, H. Trautmann, in: 2020 IEEE Congress on Evolutionary Computation (CEC), IEEE Press, Glasgow, United Kingdom, 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>2020 IEEE Congress on Evolutionary Computation (CEC)</i>, 1–8. <a href=\"https://doi.org/10.1109/CEC48606.2020.9185613\">https://doi.org/10.1109/CEC48606.2020.9185613</a>","ieee":"J. Bossek, P. Kerschke, and H. Trautmann, “Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection,” in <i>2020 IEEE Congress on Evolutionary Computation (CEC)</i>, 2020, pp. 1–8, doi: <a href=\"https://doi.org/10.1109/CEC48606.2020.9185613\">10.1109/CEC48606.2020.9185613</a>.","ama":"Bossek J, Kerschke P, Trautmann H. Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection. In: <i>2020 IEEE Congress on Evolutionary Computation (CEC)</i>. IEEE Press; 2020:1–8. doi:<a href=\"https://doi.org/10.1109/CEC48606.2020.9185613\">10.1109/CEC48606.2020.9185613</a>","bibtex":"@inproceedings{Bossek_Kerschke_Trautmann_2020, place={Glasgow, United Kingdom}, title={Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection}, DOI={<a href=\"https://doi.org/10.1109/CEC48606.2020.9185613\">10.1109/CEC48606.2020.9185613</a>}, booktitle={2020 IEEE Congress on Evolutionary Computation (CEC)}, publisher={IEEE Press}, author={Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}, year={2020}, pages={1–8} }","mla":"Bossek, Jakob, et al. “Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection.” <i>2020 IEEE Congress on Evolutionary Computation (CEC)</i>, IEEE Press, 2020, pp. 1–8, doi:<a href=\"https://doi.org/10.1109/CEC48606.2020.9185613\">10.1109/CEC48606.2020.9185613</a>."},"publication":"2020 IEEE Congress on Evolutionary Computation (CEC)","user_id":"102979","doi":"10.1109/CEC48606.2020.9185613","_id":"48844","language":[{"iso":"eng"}],"publisher":"IEEE Press","page":"1–8","publication_status":"published","date_updated":"2023-12-13T10:43:16Z","author":[{"id":"102979","last_name":"Bossek","first_name":"Jakob","orcid":"0000-0002-4121-4668","full_name":"Bossek, Jakob"},{"last_name":"Kerschke","first_name":"Pascal","full_name":"Kerschke, Pascal"},{"last_name":"Trautmann","first_name":"Heike","full_name":"Trautmann, Heike"}],"year":"2020","status":"public","title":"Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection"},{"citation":{"ieee":"J. Bossek, C. Doerr, and P. Kerschke, “Initial Design Strategies and Their Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2020, pp. 778–786, doi: <a href=\"https://doi.org/10.1145/3377930.3390155\">10.1145/3377930.3390155</a>.","apa":"Bossek, J., Doerr, C., &#38; Kerschke, P. (2020). Initial Design Strategies and Their Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 778–786. <a href=\"https://doi.org/10.1145/3377930.3390155\">https://doi.org/10.1145/3377930.3390155</a>","short":"J. Bossek, C. Doerr, P. Kerschke, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2020, pp. 778–786.","chicago":"Bossek, Jakob, Carola Doerr, and Pascal Kerschke. “Initial Design Strategies and Their Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 778–786. GECCO ’20. New York, NY, USA: Association for Computing Machinery, 2020. <a href=\"https://doi.org/10.1145/3377930.3390155\">https://doi.org/10.1145/3377930.3390155</a>.","mla":"Bossek, Jakob, et al. “Initial Design Strategies and Their Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2020, pp. 778–786, doi:<a href=\"https://doi.org/10.1145/3377930.3390155\">10.1145/3377930.3390155</a>.","bibtex":"@inproceedings{Bossek_Doerr_Kerschke_2020, place={New York, NY, USA}, series={GECCO ’20}, title={Initial Design Strategies and Their Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB}, DOI={<a href=\"https://doi.org/10.1145/3377930.3390155\">10.1145/3377930.3390155</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Doerr, Carola and Kerschke, Pascal}, year={2020}, pages={778–786}, collection={GECCO ’20} }","ama":"Bossek J, Doerr C, Kerschke P. Initial Design Strategies and Their Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO ’20. Association for Computing Machinery; 2020:778–786. doi:<a href=\"https://doi.org/10.1145/3377930.3390155\">10.1145/3377930.3390155</a>"},"place":"New York, NY, USA","status":"public","user_id":"102979","page":"778–786","_id":"48850","publisher":"Association for Computing Machinery","extern":"1","abstract":[{"lang":"eng","text":"Sequential model-based optimization (SMBO) approaches are algorithms for solving problems that require computationally or otherwise expensive function evaluations. The key design principle of SMBO is a substitution of the true objective function by a surrogate, which is used to propose the point(s) to be evaluated next. SMBO algorithms are intrinsically modular, leaving the user with many important design choices. Significant research efforts go into understanding which settings perform best for which type of problems. Most works, however, focus on the choice of the model, the acquisition function, and the strategy used to optimize the latter. The choice of the initial sampling strategy, however, receives much less attention. Not surprisingly, quite diverging recommendations can be found in the literature. We analyze in this work how the size and the distribution of the initial sample influences the overall quality of the efficient global optimization (EGO) algorithm, a well-known SMBO approach. While, overall, small initial budgets using Halton sampling seem preferable, we also observe that the performance landscape is rather unstructured. We furthermore identify several situations in which EGO performs unfavorably against random sampling. Both observations indicate that an adaptive SMBO design could be beneficial, making SMBO an interesting test-bed for automated algorithm design."}],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference","type":"conference","keyword":["continuous black-box optimization","design of experiments","initial design","sequential model-based optimization"],"department":[{"_id":"819"}],"date_created":"2023-11-14T15:58:53Z","publication_status":"published","date_updated":"2023-12-13T10:44:01Z","title":"Initial Design Strategies and Their Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB","year":"2020","author":[{"full_name":"Bossek, Jakob","first_name":"Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","id":"102979"},{"first_name":"Carola","last_name":"Doerr","full_name":"Doerr, Carola"},{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"}],"publication_identifier":{"isbn":["978-1-4503-7128-5"]},"doi":"10.1145/3377930.3390155","series_title":"GECCO ’20","language":[{"iso":"eng"}]},{"citation":{"bibtex":"@inproceedings{Bossek_Neumann_Neumann_2020, place={Berlin, Heidelberg}, title={Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-58112-1_24\">10.1007/978-3-030-58112-1_24</a>}, booktitle={Parallel Problem Solving from Nature (PPSN XVI)}, publisher={Springer-Verlag}, author={Bossek, Jakob and Neumann, Aneta and Neumann, Frank}, year={2020}, pages={346–359} }","ama":"Bossek J, Neumann A, Neumann F. Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions. In: <i>Parallel Problem Solving from Nature (PPSN XVI)</i>. Springer-Verlag; 2020:346–359. doi:<a href=\"https://doi.org/10.1007/978-3-030-58112-1_24\">10.1007/978-3-030-58112-1_24</a>","mla":"Bossek, Jakob, et al. “Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions.” <i>Parallel Problem Solving from Nature (PPSN XVI)</i>, Springer-Verlag, 2020, pp. 346–359, doi:<a href=\"https://doi.org/10.1007/978-3-030-58112-1_24\">10.1007/978-3-030-58112-1_24</a>.","short":"J. Bossek, A. Neumann, F. Neumann, in: Parallel Problem Solving from Nature (PPSN XVI), Springer-Verlag, Berlin, Heidelberg, 2020, pp. 346–359.","chicago":"Bossek, Jakob, Aneta Neumann, and Frank Neumann. “Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions.” In <i>Parallel Problem Solving from Nature (PPSN XVI)</i>, 346–359. Berlin, Heidelberg: Springer-Verlag, 2020. <a href=\"https://doi.org/10.1007/978-3-030-58112-1_24\">https://doi.org/10.1007/978-3-030-58112-1_24</a>.","ieee":"J. Bossek, A. Neumann, and F. Neumann, “Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions,” in <i>Parallel Problem Solving from Nature (PPSN XVI)</i>, 2020, pp. 346–359, doi: <a href=\"https://doi.org/10.1007/978-3-030-58112-1_24\">10.1007/978-3-030-58112-1_24</a>.","apa":"Bossek, J., Neumann, A., &#38; Neumann, F. (2020). Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions. <i>Parallel Problem Solving from Nature (PPSN XVI)</i>, 346–359. <a href=\"https://doi.org/10.1007/978-3-030-58112-1_24\">https://doi.org/10.1007/978-3-030-58112-1_24</a>"},"place":"Berlin, Heidelberg","status":"public","page":"346–359","_id":"48852","publisher":"Springer-Verlag","user_id":"102979","publication":"Parallel Problem Solving from Nature (PPSN XVI)","extern":"1","abstract":[{"text":"The Traveling Salesperson Problem (TSP) is one of the best-known combinatorial optimisation problems. However, many real-world problems are composed of several interacting components. The Traveling Thief Problem (TTP) addresses such interactions by combining two combinatorial optimisation problems, namely the TSP and the Knapsack Problem (KP). Recently, a new problem called the node weight dependent Traveling Salesperson Problem (W-TSP) has been introduced where nodes have weights that influence the cost of the tour. In this paper, we compare W-TSP and TTP. We investigate the structure of the optimised tours for W-TSP and TTP and the impact of using each others fitness function. Our experimental results suggest (1) that the W-TSP often can be solved better using the TTP fitness function and (2) final W-TSP and TTP solutions show different distributions when compared with optimal TSP or weighted greedy solutions.","lang":"eng"}],"date_created":"2023-11-14T15:58:54Z","keyword":["Evolutionary algorithms","Node weight dependent TSP","Traveling Thief Problem"],"type":"conference","department":[{"_id":"819"}],"title":"Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions","year":"2020","publication_identifier":{"isbn":["978-3-030-58111-4"]},"author":[{"id":"102979","orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob","full_name":"Bossek, Jakob"},{"first_name":"Aneta","last_name":"Neumann","full_name":"Neumann, Aneta"},{"full_name":"Neumann, Frank","last_name":"Neumann","first_name":"Frank"}],"publication_status":"published","date_updated":"2023-12-13T10:44:54Z","language":[{"iso":"eng"}],"doi":"10.1007/978-3-030-58112-1_24"},{"doi":"10.1109/CEC48606.2020.9185778","user_id":"102979","page":"1–8","language":[{"iso":"eng"}],"_id":"48846","publisher":"IEEE Press","date_updated":"2023-12-13T10:44:17Z","publication_status":"published","year":"2020","status":"public","title":"Towards Decision Support in Dynamic Bi-Objective Vehicle Routing","author":[{"full_name":"Bossek, Jakob","orcid":"0000-0002-4121-4668","first_name":"Jakob","last_name":"Bossek","id":"102979"},{"full_name":"Grimme, Christian","first_name":"Christian","last_name":"Grimme"},{"last_name":"Rudolph","first_name":"Günter","full_name":"Rudolph, Günter"},{"last_name":"Trautmann","first_name":"Heike","full_name":"Trautmann, Heike"}],"type":"conference","department":[{"_id":"819"}],"place":"Glasgow, United Kingdom","date_created":"2023-11-14T15:58:53Z","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{$<$}inf{$>$}t{$<$}/inf{$>$} and analyze all $|D|\\^{n_t}$ 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."}],"extern":"1","publication":"2020 IEEE Congress on Evolutionary Computation (CEC)","citation":{"short":"J. Bossek, C. Grimme, G. Rudolph, H. Trautmann, in: 2020 IEEE Congress on Evolutionary Computation (CEC), IEEE Press, Glasgow, United Kingdom, 2020, pp. 1–8.","chicago":"Bossek, Jakob, Christian Grimme, Günter Rudolph, and Heike Trautmann. “Towards Decision Support in Dynamic Bi-Objective Vehicle Routing.” In <i>2020 IEEE Congress on Evolutionary Computation (CEC)</i>, 1–8. Glasgow, United Kingdom: IEEE Press, 2020. <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>2020 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>.","apa":"Bossek, J., Grimme, C., Rudolph, G., &#38; Trautmann, H. (2020). Towards Decision Support in Dynamic Bi-Objective Vehicle Routing. <i>2020 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>","bibtex":"@inproceedings{Bossek_Grimme_Rudolph_Trautmann_2020, place={Glasgow, United Kingdom}, 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={2020 IEEE Congress on Evolutionary Computation (CEC)}, publisher={IEEE Press}, author={Bossek, Jakob and Grimme, Christian and Rudolph, Günter and Trautmann, Heike}, year={2020}, pages={1–8} }","ama":"Bossek J, Grimme C, Rudolph G, Trautmann H. Towards Decision Support in Dynamic Bi-Objective Vehicle Routing. In: <i>2020 IEEE Congress on Evolutionary Computation (CEC)</i>. IEEE Press; 2020:1–8. doi:<a href=\"https://doi.org/10.1109/CEC48606.2020.9185778\">10.1109/CEC48606.2020.9185778</a>","mla":"Bossek, Jakob, et al. “Towards Decision Support in Dynamic Bi-Objective Vehicle Routing.” <i>2020 IEEE Congress on Evolutionary Computation (CEC)</i>, IEEE Press, 2020, pp. 1–8, doi:<a href=\"https://doi.org/10.1109/CEC48606.2020.9185778\">10.1109/CEC48606.2020.9185778</a>."}},{"citation":{"chicago":"Do, Anh Viet, Jakob Bossek, Aneta Neumann, and Frank Neumann. “Evolving Diverse Sets of Tours for the Travelling Salesperson Problem.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 681–689. GECCO’20. New York, NY, USA: Association for Computing Machinery, 2020. <a href=\"https://doi.org/10.1145/3377930.3389844\">https://doi.org/10.1145/3377930.3389844</a>.","short":"A.V. Do, J. Bossek, A. Neumann, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2020, pp. 681–689.","apa":"Do, A. V., Bossek, J., Neumann, A., &#38; Neumann, F. (2020). Evolving Diverse Sets of Tours for the Travelling Salesperson Problem. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 681–689. <a href=\"https://doi.org/10.1145/3377930.3389844\">https://doi.org/10.1145/3377930.3389844</a>","ieee":"A. V. Do, J. Bossek, A. Neumann, and F. Neumann, “Evolving Diverse Sets of Tours for the Travelling Salesperson Problem,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2020, pp. 681–689, doi: <a href=\"https://doi.org/10.1145/3377930.3389844\">10.1145/3377930.3389844</a>.","ama":"Do AV, Bossek J, Neumann A, Neumann F. Evolving Diverse Sets of Tours for the Travelling Salesperson Problem. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO’20. Association for Computing Machinery; 2020:681–689. doi:<a href=\"https://doi.org/10.1145/3377930.3389844\">10.1145/3377930.3389844</a>","bibtex":"@inproceedings{Do_Bossek_Neumann_Neumann_2020, place={New York, NY, USA}, series={GECCO’20}, title={Evolving Diverse Sets of Tours for the Travelling Salesperson Problem}, DOI={<a href=\"https://doi.org/10.1145/3377930.3389844\">10.1145/3377930.3389844</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Do, Anh Viet and Bossek, Jakob and Neumann, Aneta and Neumann, Frank}, year={2020}, pages={681–689}, collection={GECCO’20} }","mla":"Do, Anh Viet, et al. “Evolving Diverse Sets of Tours for the Travelling Salesperson Problem.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2020, pp. 681–689, doi:<a href=\"https://doi.org/10.1145/3377930.3389844\">10.1145/3377930.3389844</a>."},"place":"New York, NY, USA","status":"public","user_id":"102979","_id":"48879","publisher":"Association for Computing Machinery","page":"681–689","extern":"1","abstract":[{"lang":"eng","text":"Evolving diverse sets of high quality solutions has gained increasing interest in the evolutionary computation literature in recent years. With this paper, we contribute to this area of research by examining evolutionary diversity optimisation approaches for the classical Traveling Salesperson Problem (TSP). We study the impact of using different diversity measures for a given set of tours and the ability of evolutionary algorithms to obtain a diverse set of high quality solutions when adopting these measures. Our studies show that a large variety of diverse high quality tours can be achieved by using our approaches. Furthermore, we compare our approaches in terms of theoretical properties and the final set of tours obtained by the evolutionary diversity optimisation algorithm."}],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference","department":[{"_id":"819"}],"type":"conference","keyword":["diversity maximisation","evolutionary algorithms","travelling salesperson problem"],"date_created":"2023-11-14T15:58:58Z","date_updated":"2023-12-13T10:48:50Z","publication_identifier":{"isbn":["978-1-4503-7128-5"]},"author":[{"full_name":"Do, Anh Viet","first_name":"Anh Viet","last_name":"Do"},{"orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob","full_name":"Bossek, Jakob","id":"102979"},{"last_name":"Neumann","first_name":"Aneta","full_name":"Neumann, Aneta"},{"first_name":"Frank","last_name":"Neumann","full_name":"Neumann, Frank"}],"title":"Evolving Diverse Sets of Tours for the Travelling Salesperson Problem","year":"2020","doi":"10.1145/3377930.3389844","series_title":"GECCO’20","language":[{"iso":"eng"}]},{"_id":"48895","publisher":"Association for Computing Machinery","page":"551–559","user_id":"102979","status":"public","place":"New York, NY, USA","citation":{"short":"V. Roostapour, J. Bossek, F. Neumann, in: Proceedings of the 2020 Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2020, pp. 551–559.","chicago":"Roostapour, Vahid, Jakob Bossek, and Frank Neumann. “Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem.” In <i>Proceedings of the 2020 Genetic and Evolutionary Computation Conference</i>, 551–559. {GECCO} ’20. New York, NY, USA: Association for Computing Machinery, 2020. <a href=\"https://doi.org/10.1145/3377930.3390168\">https://doi.org/10.1145/3377930.3390168</a>.","ieee":"V. Roostapour, J. Bossek, and F. Neumann, “Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem,” in <i>Proceedings of the 2020 Genetic and Evolutionary Computation Conference</i>, 2020, pp. 551–559, doi: <a href=\"https://doi.org/10.1145/3377930.3390168\">10.1145/3377930.3390168</a>.","apa":"Roostapour, V., Bossek, J., &#38; Neumann, F. (2020). Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem. <i>Proceedings of the 2020 Genetic and Evolutionary Computation Conference</i>, 551–559. <a href=\"https://doi.org/10.1145/3377930.3390168\">https://doi.org/10.1145/3377930.3390168</a>","bibtex":"@inproceedings{Roostapour_Bossek_Neumann_2020, place={New York, NY, USA}, series={{GECCO} ’20}, title={Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem}, DOI={<a href=\"https://doi.org/10.1145/3377930.3390168\">10.1145/3377930.3390168</a>}, booktitle={Proceedings of the 2020 Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Roostapour, Vahid and Bossek, Jakob and Neumann, Frank}, year={2020}, pages={551–559}, collection={{GECCO} ’20} }","ama":"Roostapour V, Bossek J, Neumann F. Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem. In: <i>Proceedings of the 2020 Genetic and Evolutionary Computation Conference</i>. {GECCO} ’20. Association for Computing Machinery; 2020:551–559. doi:<a href=\"https://doi.org/10.1145/3377930.3390168\">10.1145/3377930.3390168</a>","mla":"Roostapour, Vahid, et al. “Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem.” <i>Proceedings of the 2020 Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2020, pp. 551–559, doi:<a href=\"https://doi.org/10.1145/3377930.3390168\">10.1145/3377930.3390168</a>."},"language":[{"iso":"eng"}],"series_title":"{GECCO} ’20","doi":"10.1145/3377930.3390168","publication_identifier":{"isbn":["978-1-4503-7128-5"]},"author":[{"full_name":"Roostapour, Vahid","last_name":"Roostapour","first_name":"Vahid"},{"id":"102979","first_name":"Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","full_name":"Bossek, Jakob"},{"last_name":"Neumann","first_name":"Frank","full_name":"Neumann, Frank"}],"year":"2020","title":"Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem","date_updated":"2023-12-13T10:49:38Z","date_created":"2023-11-14T15:59:00Z","department":[{"_id":"819"}],"type":"conference","keyword":["biased mutation","evolutionary algorithms","minimum spanning tree problem","runtime analysis"],"publication":"Proceedings of the 2020 Genetic and Evolutionary Computation Conference","extern":"1","abstract":[{"lang":"eng","text":"Evolutionary algorithms (EAs) are general-purpose problem solvers that usually perform an unbiased search. This is reasonable and desirable in a black-box scenario. For combinatorial optimization problems, often more knowledge about the structure of optimal solutions is given, which can be leveraged by means of biased search operators. We consider the Minimum Spanning Tree (MST) problem in a single- and multi-objective version, and introduce a biased mutation, which puts more emphasis on the selection of edges of low rank in terms of low domination number. We present example graphs where the biased mutation can significantly speed up the expected runtime until (Pareto-)optimal solutions are found. On the other hand, we demonstrate that bias can lead to exponential runtime if \"heavy\" edges are necessarily part of an optimal solution. However, on general graphs in the single-objective setting, we show that a combined mutation operator which decides for unbiased or biased edge selection in each step with equal probability exhibits a polynomial upper bound - as unbiased mutation - in the worst case and benefits from bias if the circumstances are favorable."}]},{"page":"48–64","_id":"48897","publisher":"Springer-Verlag","user_id":"102979","status":"public","place":"Berlin, Heidelberg","citation":{"bibtex":"@inproceedings{Seiler_Pohl_Bossek_Kerschke_Trautmann_2020, place={Berlin, Heidelberg}, 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={Parallel Problem Solving from {Nature} (PPSN XVI)}, publisher={Springer-Verlag}, author={Seiler, Moritz and Pohl, Janina and Bossek, Jakob and Kerschke, Pascal 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: <i>Parallel Problem Solving from {Nature} (PPSN XVI)</i>. Springer-Verlag; 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>","short":"M. Seiler, J. Pohl, J. Bossek, P. Kerschke, H. Trautmann, in: Parallel Problem Solving from {Nature} (PPSN XVI), Springer-Verlag, Berlin, Heidelberg, 2020, pp. 48–64.","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>Parallel Problem Solving from {Nature} (PPSN XVI)</i>, 48–64. Berlin, Heidelberg: Springer-Verlag, 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>.","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>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>.","mla":"Seiler, Moritz, et al. “Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem.” <i>Parallel Problem Solving from {Nature} (PPSN XVI)</i>, Springer-Verlag, 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. <i>Parallel Problem Solving from {Nature} (PPSN XVI)</i>, 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>"},"language":[{"iso":"eng"}],"doi":"10.1007/978-3-030-58112-1_4","title":"Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem","year":"2020","publication_identifier":{"isbn":["978-3-030-58111-4"]},"author":[{"last_name":"Seiler","first_name":"Moritz","full_name":"Seiler, Moritz"},{"first_name":"Janina","last_name":"Pohl","full_name":"Pohl, Janina"},{"id":"102979","full_name":"Bossek, Jakob","last_name":"Bossek","first_name":"Jakob","orcid":"0000-0002-4121-4668"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"full_name":"Trautmann, Heike","first_name":"Heike","last_name":"Trautmann"}],"date_updated":"2023-12-13T10:49:45Z","date_created":"2023-11-14T15:59:00Z","keyword":["Automated algorithm selection","Deep learning","Feature-based approaches","Traveling Salesperson Problem"],"type":"conference","department":[{"_id":"819"}],"publication":"Parallel Problem Solving from {Nature} (PPSN XVI)","extern":"1","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 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."}]},{"keyword":["Algorithm selection","Combinatorial optimization","Multi-objective optimization","Performance measurement","Traveling Salesperson Problem"],"type":"journal_article","department":[{"_id":"819"}],"date_created":"2023-11-14T15:58:53Z","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 \\textendash both to be minimized \\textendash 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. \\textbullet The multi-objective perspective is naturally generalizable to multiple objectives. \\textbullet Proof of relationship between HV and the PAR in the considered bi-objective space. \\textbullet New insights into complementary behavior of stochastic optimization algorithms."}],"publication":"Applied Soft Computing","issue":"C","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, no. C, 2020, doi:<a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">10.1016/j.asoc.2019.105901</a>.","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>(C). <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, no. C, 2020, doi: <a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">10.1016/j.asoc.2019.105901</a>.","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, no. C (2020). <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).","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(C). doi:<a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">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\">10.1016/j.asoc.2019.105901</a>}, number={C}, journal={Applied Soft Computing}, author={Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}, year={2020} }"},"doi":"10.1016/j.asoc.2019.105901","user_id":"102979","volume":88,"_id":"48848","language":[{"iso":"eng"}],"date_updated":"2023-12-13T10:52:17Z","intvolume":"        88","title":"A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms","status":"public","year":"2020","publication_identifier":{"issn":["1568-4946"]},"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"},{"full_name":"Trautmann, Heike","first_name":"Heike","last_name":"Trautmann"}]},{"date_created":"2023-11-14T15:58:51Z","department":[{"_id":"819"}],"type":"journal_article","citation":{"apa":"Bartz-Beielstein, T., Doerr, C., van den Berg, D., Bossek, J., Chandrasekaran, S., Eftimov, T., Fischbach, A., Kerschke, P., Cava, W. L., Lopez-Ibanez, M., Malan, K. M., Moore, J. H., Naujoks, B., Orzechowski, P., Volz, V., Wagner, M., &#38; Weise, T. (2020). Benchmarking in Optimization: Best Practice and Open Issues. <i>Corr</i>.","ieee":"T. Bartz-Beielstein <i>et al.</i>, “Benchmarking in Optimization: Best Practice and Open Issues,” <i>Corr</i>, 2020.","chicago":"Bartz-Beielstein, Thomas, Carola Doerr, Daan van den Berg, Jakob Bossek, Sowmya Chandrasekaran, Tome Eftimov, Andreas Fischbach, et al. “Benchmarking in Optimization: Best Practice and Open Issues.” <i>Corr</i>, 2020.","short":"T. Bartz-Beielstein, C. Doerr, D. van den Berg, J. Bossek, S. Chandrasekaran, T. Eftimov, A. Fischbach, P. Kerschke, W.L. Cava, M. Lopez-Ibanez, K.M. Malan, J.H. Moore, B. Naujoks, P. Orzechowski, V. Volz, M. Wagner, T. Weise, Corr (2020).","mla":"Bartz-Beielstein, Thomas, et al. “Benchmarking in Optimization: Best Practice and Open Issues.” <i>Corr</i>, 2020.","ama":"Bartz-Beielstein T, Doerr C, van den Berg D, et al. Benchmarking in Optimization: Best Practice and Open Issues. <i>Corr</i>. Published online 2020.","bibtex":"@article{Bartz-Beielstein_Doerr_van den Berg_Bossek_Chandrasekaran_Eftimov_Fischbach_Kerschke_Cava_Lopez-Ibanez_et al._2020, title={Benchmarking in Optimization: Best Practice and Open Issues}, journal={Corr}, author={Bartz-Beielstein, Thomas and Doerr, Carola and van den Berg, Daan and Bossek, Jakob and Chandrasekaran, Sowmya and Eftimov, Tome and Fischbach, Andreas and Kerschke, Pascal and Cava, William La and Lopez-Ibanez, Manuel and et al.}, year={2020} }"},"publication":"Corr","language":[{"iso":"eng"}],"_id":"48836","user_id":"102979","author":[{"last_name":"Bartz-Beielstein","first_name":"Thomas","full_name":"Bartz-Beielstein, Thomas"},{"first_name":"Carola","last_name":"Doerr","full_name":"Doerr, Carola"},{"full_name":"van den Berg, Daan","first_name":"Daan","last_name":"van den Berg"},{"full_name":"Bossek, Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","first_name":"Jakob","id":"102979"},{"last_name":"Chandrasekaran","first_name":"Sowmya","full_name":"Chandrasekaran, Sowmya"},{"first_name":"Tome","last_name":"Eftimov","full_name":"Eftimov, Tome"},{"first_name":"Andreas","last_name":"Fischbach","full_name":"Fischbach, Andreas"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"last_name":"Cava","first_name":"William La","full_name":"Cava, William La"},{"full_name":"Lopez-Ibanez, Manuel","last_name":"Lopez-Ibanez","first_name":"Manuel"},{"last_name":"Malan","first_name":"Katherine M.","full_name":"Malan, Katherine M."},{"last_name":"Moore","first_name":"Jason H.","full_name":"Moore, Jason H."},{"full_name":"Naujoks, Boris","first_name":"Boris","last_name":"Naujoks"},{"first_name":"Patryk","last_name":"Orzechowski","full_name":"Orzechowski, Patryk"},{"last_name":"Volz","first_name":"Vanessa","full_name":"Volz, Vanessa"},{"full_name":"Wagner, Markus","first_name":"Markus","last_name":"Wagner"},{"full_name":"Weise, Thomas","last_name":"Weise","first_name":"Thomas"}],"title":"Benchmarking in Optimization: Best Practice and Open Issues","status":"public","year":"2020","date_updated":"2023-12-13T10:52:24Z"},{"status":"public","year":"2020","title":"Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries","author":[{"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"}],"date_updated":"2024-06-07T07:11:53Z","page":"1–8","_id":"46331","language":[{"iso":"eng"}],"user_id":"15504","doi":"10.1109/IJCNN48605.2020.9207338","publication":"Proceedings of the International Joint Conference on Neural Networks (IJCNN)","citation":{"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>.","short":"M. Seiler, H. Trautmann, P. Kerschke, in: Proceedings of the International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 2020, pp. 1–8.","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>."},"abstract":[{"lang":"eng","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."}],"date_created":"2023-08-04T07:39:48Z","place":"Glasgow, UK","type":"conference","department":[{"_id":"34"},{"_id":"819"}]}]