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J. Bossek, F. Neumann, P. Peng, and D. Sudholt, “More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization,”10.1145/3377930.3390174__in Proceedings of the Genetic and Evolutionary Computation Conference, 2020, pp. 1277–1285, doi:10.1145/3377930.3390174 .
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J. Bossek, C. Doerr, P. Kerschke, A. Neumann, and F. Neumann, “Evolving Sampling Strategies for One-Shot Optimization Tasks,”10.1007/978-3-030-58112-1_8__in Parallel Problem Solving from Nature (PPSN XVI), 2020, pp. 111–124, doi:10.1007/978-3-030-58112-1_8 .
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J. Bossek, K. Casel, P. Kerschke, and F. Neumann, “The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics,”10.1145/3377930.3390243__in Proceedings of the Genetic and Evolutionary Computation Conference, 2020, pp. 1286–1294, doi:10.1145/3377930.3390243 .
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J. Bossek, C. Grimme, and H. Trautmann, “Dynamic Bi-Objective Routing of Multiple Vehicles,”10.1145/3377930.3390146__in Proceedings of the Genetic and Evolutionary Computation Conference, 2020, pp. 166–174, doi:10.1145/3377930.3390146 .
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J. Bossek, P. Kerschke, and H. Trautmann, “Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection,”10.1109/CEC48606.2020.9185613__in 2020 IEEE Congress on Evolutionary Computation (CEC), 2020, pp. 1–8, doi:10.1109/CEC48606.2020.9185613 .
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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,”10.1145/3377930.3390155__in Proceedings of the Genetic and Evolutionary Computation Conference, 2020, pp. 778–786, doi:10.1145/3377930.3390155 .
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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,”10.1007/978-3-030-58112-1_24__in Parallel Problem Solving from Nature (PPSN XVI), 2020, pp. 346–359, doi:10.1007/978-3-030-58112-1_24 .
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J. Bossek, C. Grimme, G. Rudolph, and H. Trautmann, “Towards Decision Support in Dynamic Bi-Objective Vehicle Routing,”10.1109/CEC48606.2020.9185778__in 2020 IEEE Congress on Evolutionary Computation (CEC), 2020, pp. 1–8, doi:10.1109/CEC48606.2020.9185778 .
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A. V. Do, J. Bossek, A. Neumann, and F. Neumann, “Evolving Diverse Sets of Tours for the Travelling Salesperson Problem,”10.1145/3377930.3389844__in Proceedings of the Genetic and Evolutionary Computation Conference, 2020, pp. 681–689, doi:10.1145/3377930.3389844 .
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V. Roostapour, J. Bossek, and F. Neumann, “Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem,”10.1145/3377930.3390168__in Proceedings of the 2020 Genetic and Evolutionary Computation Conference, 2020, pp. 551–559, doi:10.1145/3377930.3390168 .
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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,”10.1007/978-3-030-58112-1_4__in Parallel Problem Solving from Nature (PPSN XVI), 2020, pp. 48–64, doi:10.1007/978-3-030-58112-1_4 .
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J. Bossek, P. Kerschke, and H. Trautmann, “A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms,”10.1016/j.asoc.2019.105901__Applied Soft Computing, vol. 88, no. C, 2020, doi:10.1016/j.asoc.2019.105901 .
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