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197 Publications
2020 | Conference Paper | LibreCat-ID: 46321
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. Disinformation in Open Online Media. Lecture Notes in Computer Science. Springer; 2020:101–114.
LibreCat
2020 | Conference Paper | LibreCat-ID: 46326
Carnein M, Trautmann H, Bifet A, Pfahringer B. confStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms. In: Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020). ; 2020:80–95. doi:10.1007/978-3-030-53552-0_10
LibreCat
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2020 | Conference Paper | LibreCat-ID: 46327
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. Disinformation in Open Online Media. ; 2020:48–58.
LibreCat
2020 | Conference Paper | LibreCat-ID: 46329
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. Social Computing and Social Media. Participation, User Experience, Consumer Experience, and Applications of Social Computing. Springer International Publishing; 2020:71–86.
LibreCat
2020 | Journal Article | LibreCat-ID: 46333
Assenmacher D, Clever L, Frischlich L, Quandt T, Trautmann H, Grimme C. Demystifying Social Bots: On the Intelligence of Automated Social Media Actors. Social Media + Society. 2020;6(3):2056305120939264. doi:10.1177/2056305120939264
LibreCat
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2020 | Conference Paper | LibreCat-ID: 46332
Steinhoff V, Kerschke P, Aspar P, Trautmann H, Grimme C. Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent. In: Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI). ; 2020:2445–2452. doi:10.1109/SSCI47803.2020.9308259
LibreCat
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2020 | Conference Paper | LibreCat-ID: 48847
Bossek J, Neumann F, Peng P, Sudholt D. More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization. In: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO ’20. Association for Computing Machinery; 2020:1277–1285. doi:10.1145/3377930.3390174
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2020 | Conference Paper | LibreCat-ID: 48849
Bossek J, Doerr C, Kerschke P, Neumann A, Neumann F. Evolving Sampling Strategies for One-Shot Optimization Tasks. In: Parallel Problem Solving from Nature (PPSN XVI). Springer-Verlag; 2020:111–124. doi:10.1007/978-3-030-58112-1_8
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2020 | Conference Paper | LibreCat-ID: 48851
Bossek J, Casel K, Kerschke P, Neumann F. The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics. In: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO ’20. Association for Computing Machinery; 2020:1286–1294. doi:10.1145/3377930.3390243
LibreCat
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2020 | Conference Paper | LibreCat-ID: 48845
Bossek J, Grimme C, Trautmann H. Dynamic Bi-Objective Routing of Multiple Vehicles. In: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO ’20. Association for Computing Machinery; 2020:166–174. doi:10.1145/3377930.3390146
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2020 | Conference Paper | LibreCat-ID: 48844
Bossek J, Kerschke P, Trautmann H. Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection. In: 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE Press; 2020:1–8. doi:10.1109/CEC48606.2020.9185613
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2020 | Conference Paper | LibreCat-ID: 48850
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: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO ’20. Association for Computing Machinery; 2020:778–786. doi:10.1145/3377930.3390155
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2020 | Conference Paper | LibreCat-ID: 48852
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: Parallel Problem Solving from Nature (PPSN XVI). Springer-Verlag; 2020:346–359. doi:10.1007/978-3-030-58112-1_24
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2020 | Conference Paper | LibreCat-ID: 48846
Bossek J, Grimme C, Rudolph G, Trautmann H. Towards Decision Support in Dynamic Bi-Objective Vehicle Routing. In: 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE Press; 2020:1–8. doi:10.1109/CEC48606.2020.9185778
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2020 | Conference Paper | LibreCat-ID: 48879
Do AV, Bossek J, Neumann A, Neumann F. Evolving Diverse Sets of Tours for the Travelling Salesperson Problem. In: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO’20. Association for Computing Machinery; 2020:681–689. doi:10.1145/3377930.3389844
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2020 | Conference Paper | LibreCat-ID: 48895
Roostapour V, Bossek J, Neumann F. Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem. In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference. {GECCO} ’20. Association for Computing Machinery; 2020:551–559. doi:10.1145/3377930.3390168
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2020 | Conference Paper | LibreCat-ID: 48897
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: Parallel Problem Solving from {Nature} (PPSN XVI). Springer-Verlag; 2020:48–64. doi:10.1007/978-3-030-58112-1_4
LibreCat
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2020 | Journal Article | LibreCat-ID: 48848
Bossek J, Kerschke P, Trautmann H. A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms. Applied Soft Computing. 2020;88(C). doi:10.1016/j.asoc.2019.105901
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2020 | Journal Article | LibreCat-ID: 48836
Bartz-Beielstein T, Doerr C, van den Berg D, et al. Benchmarking in Optimization: Best Practice and Open Issues. Corr. Published online 2020.
LibreCat
2020 | Conference Paper | LibreCat-ID: 46331
Seiler M, Trautmann H, Kerschke P. Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN). ; 2020:1–8. doi:10.1109/IJCNN48605.2020.9207338
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