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204 Publications
2022 | Conference Paper | LibreCat-ID: 46304
R. P. Prager, M. Seiler, H. Trautmann, and P. Kerschke, “Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods,” in Parallel Problem Solving from Nature — PPSN XVII, 2022, pp. 3–17, doi: 10.1007/978-3-031-14714-2_1.
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| DOI
2022 | Conference Paper | LibreCat-ID: 46303
J. S. Pohl, D. Assenmacher, M. Seiler, H. Trautmann, and C. Grimme, “Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches,” in Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM), 2022, pp. 1–10, doi: 10.36190/2022.91.
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2022 | Journal Article | LibreCat-ID: 46309
L. Clever, J. S. Pohl, J. Bossek, P. Kerschke, and H. Trautmann, “Process-Oriented Stream Classification Pipeline: A Literature Review,” Applied Sciences, vol. 12, no. 8, pp. 1–44, 2022, doi: 10.3390/app12189094.
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2022 | Conference Paper | LibreCat-ID: 46305
J. Rook, H. Trautmann, J. Bossek, and C. Grimme, “On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems,” in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2022, pp. 356–359–356–359, doi: 10.1145/3520304.3528998.
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| DOI
2022 | Conference Paper | LibreCat-ID: 46302
J. Heins, J. Rook, L. Schäpermeier, P. Kerschke, J. Bossek, and H. Trautmann, “BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems,” in Parallel Problem Solving from Nature — PPSN XVII, 2022, pp. 192–206.
LibreCat
2021 | Journal Article | LibreCat-ID: 46318
C. Grimme et al., “Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization,” Computers & Operations Research, vol. 136, p. 105489, 2021, doi: https://doi.org/10.1016/j.cor.2021.105489.
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2021 | Conference Paper | LibreCat-ID: 46311
P. Aspar, P. Kerschke, V. Steinhoff, H. Trautmann, and C. Grimme, “Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective Space by Means of Multiobjectivization,” in Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings, 2021, pp. 311–322, doi: 10.1007/978-3-030-72062-9_25.
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2021 | Journal Article | LibreCat-ID: 46317
C. Coombs et al., “What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective,” International Journal of Information Management, vol. 58, 2021, doi: 10.1016/j.ijinfomgt.2021.102311.
LibreCat
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2021 | Conference Paper | LibreCat-ID: 48853
J. Bossek, A. Neumann, and F. Neumann, “Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms,” in Proceedings of the Genetic and Evolutionary Computation Conference, 2021, pp. 556–564, doi: 10.1145/3449639.3459364.
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2021 | Conference Paper | LibreCat-ID: 48855
J. Bossek, A. Neumann, and F. Neumann, “Exact Counting and~Sampling of Optima for the Knapsack Problem,” in Learning and Intelligent Optimization, 2021, pp. 40–54, doi: 10.1007/978-3-030-92121-7_4.
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2021 | Conference Paper | LibreCat-ID: 48860
J. Bossek and F. Neumann, “Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem,” in Proceedings of the Genetic and Evolutionary Computation Conference, 2021, pp. 198–206, doi: 10.1145/3449639.3459363.
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2021 | Book Chapter | LibreCat-ID: 48862
J. Bossek and D. Sudholt, “Do Additional Optima Speed up Evolutionary Algorithms?,” in Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, New York, NY, USA: Association for Computing Machinery, 2021, pp. 1–11.
LibreCat
2021 | Book Chapter | LibreCat-ID: 48881
J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, and P. Kerschke, “On the Potential of Normalized TSP Features for Automated Algorithm Selection,” in Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, New York, NY, USA: Association for Computing Machinery, 2021, pp. 1–15.
LibreCat
2021 | Conference Paper | LibreCat-ID: 48876
J. Bossek and M. Wagner, “Generating Instances with Performance Differences for More than Just Two Algorithms,” in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2021, pp. 1423–1432, doi: 10.1145/3449726.3463165.
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2021 | Conference Paper | LibreCat-ID: 48893
A. Nikfarjam, J. Bossek, A. Neumann, and F. Neumann, “Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem,” in Proceedings of the Genetic and Evolutionary Computation Conference, 2021, pp. 600–608, doi: 10.1145/3449639.3459384.
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2021 | Conference Paper | LibreCat-ID: 48891
A. Neumann, J. Bossek, and F. Neumann, “Diversifying Greedy Sampling and Evolutionary Diversity Optimisation for Constrained Monotone Submodular Functions,” in Proceedings of the Genetic and Evolutionary Computation Conference, 2021, pp. 261–269, doi: 10.1145/3449639.3459385.
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2021 | Book Chapter | LibreCat-ID: 48892
A. Nikfarjam, J. Bossek, A. Neumann, and F. Neumann, “Computing Diverse Sets of High Quality TSP Tours by EAX-based Evolutionary Diversity Optimisation,” in Proceedings of the 16th ACM}/SIGEVO Conference on Foundations of Genetic Algorithms, New York, NY, USA: Association for Computing Machinery, 2021, pp. 1–11.
LibreCat
2021 | Journal Article | LibreCat-ID: 48854
J. Bossek, F. Neumann, P. Peng, and D. Sudholt, “Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem,” Algorithmica, vol. 83, no. 10, pp. 3148–3179, 2021, doi: 10.1007/s00453-021-00838-3.
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2021 | Conference Paper | LibreCat-ID: 46315
R. P. Prager, M. Seiler, H. Trautmann, and P. Kerschke, “Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization,” in 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021, pp. 1–8, doi: 10.1109/SSCI50451.2021.9660174.
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| DOI
2021 | Conference Paper | LibreCat-ID: 46312
D. Assenmacher, M. Niemann, K. Müller, M. Seiler, D. M. Riehle, and H. Trautmann, “RP-Mod & RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets,” in Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021), 2021, pp. 1–14.
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