Jakob Bossek

Fakultät für Elektrotechnik, Informatik und Mathematik
jakob.bossek@uni-paderborn.de
ID
102979

90 Publications

Mark all

[90]
2025 | Journal Article | LibreCat-ID: 59073
Rook, J. G., Benjamins, C., Bossek, J., Trautmann, H., Hoos, H. H., & Lindauer, M. (2025). MO-SMAC: Multi-objective Sequential Model-based Algorithm Configuration. Evolutionary Computation, 1–25. https://doi.org/10.1162/evco_a_00371
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[89]
2025 | Conference Paper | LibreCat-ID: 60812
Preuß, O. L., Mensendiek, C., Rook, J., Bossek, J., & Trautmann, H. (2025). Automated Algorithm Configuration and Systematic Benchmarking for Heterogeneous MNK-Landscapes. In B. Filipic (Ed.), Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2025, NH Malaga Hotel, Malaga, Spain, July 14-18, 2025 (pp. 58–66). ACM. https://doi.org/10.1145/3712256.3726481
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[88]
2025 | Conference Paper | LibreCat-ID: 63703
Hoffbauer, T., Hoos, H. H., & Bossek, J. (2025). KernelMatmul: Scaling Gaussian Processes to Large Time Series. In T. Walsh, J. Shah, & Z. Kolter (Eds.), AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25 - March 4, 2025, Philadelphia, PA, USA (pp. 17223–17230). AAAI Press. https://doi.org/10.1609/AAAI.V39I16.33893
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[87]
2025 | Conference Paper | LibreCat-ID: 63704
Wittner, D., & Bossek, J. (2025). Cluster Prevention in Evolutionary Diversity Optimization for Parallel Machine Scheduling. In B. Filipic (Ed.), Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2025, NH Malaga Hotel, Malaga, Spain, July 14-18, 2025 (pp. 340–348). ACM. https://doi.org/10.1145/3712256.3726357
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[86]
2024 | Conference Paper | LibreCat-ID: 52749
Seiler, M., Rook, J., Heins, J., Preuß, O. L., Bossek, J., & Trautmann, H. (2024). Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP. 2023 IEEE Symposium Series on Computational Intelligence (SSCI). https://doi.org/10.1109/ssci52147.2023.10372008
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[85]
2024 | Conference Paper | LibreCat-ID: 63706
Schmidbauer, M., Opris, A., Bossek, J., Neumann, F., & Sudholt, D. (2024). Guiding Quality Diversity on Monotone Submodular Functions: Customising the Feature Space by Adding Boolean Conjunctions. In X. Li & J. Handl (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2024, Melbourne, VIC, Australia, July 14-18, 2024. ACM. https://doi.org/10.1145/3638529.3654160
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[84]
2024 | Conference Paper | LibreCat-ID: 63705
Bossek, J., & Grimme, C. (2024). Generalised Kruskal Mutation for the Multi-Objective Minimum Spanning Tree Problem. In X. Li & J. Handl (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2024, Melbourne, VIC, Australia, July 14-18, 2024. ACM. https://doi.org/10.1145/3638529.3654165
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[83]
2023 | Conference Paper | LibreCat-ID: 48869
Bossek, J., Neumann, A., & Neumann, F. (2023). On the Impact of Basic Mutation Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson Problem. Proceedings of the Genetic and Evolutionary Computation Conference, 248–256. https://doi.org/10.1145/3583131.3590384
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[82]
2023 | Conference Paper | LibreCat-ID: 48872
Bossek, J., & Sudholt, D. (2023). Runtime Analysis of Quality Diversity Algorithms. Proceedings of the Genetic and Evolutionary Computation Conference, 1546–1554. https://doi.org/10.1145/3583131.3590383
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[81]
2023 | Conference Paper | LibreCat-ID: 48886
Marrero, A., Segredo, E., Hart, E., Bossek, J., & Neumann, A. (2023). Generating Diverse and Discriminatory Knapsack Instances by Searching for Novelty in Variable Dimensions of Feature-Space. Proceedings of the Genetic} and Evolutionary Computation Conference, 312–320. https://doi.org/10.1145/3583131.3590504
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[80]
2023 | Journal Article | LibreCat-ID: 48871
Bossek, J., & Sudholt, D. (2023). Do Additional Target Points Speed Up Evolutionary Algorithms? Theoretical Computer Science, 113757. https://doi.org/10.1016/j.tcs.2023.113757
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[79]
2023 | Journal Article | LibreCat-ID: 48859
Bossek, J., & Grimme, C. (2023). On Single-Objective Sub-Graph-Based Mutation for Solving the Bi-Objective Minimum Spanning Tree Problem. Evolutionary Computation, 1–35. https://doi.org/10.1162/evco_a_00335
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[78]
2023 | Journal Article | LibreCat-ID: 46310
Heins, J., Bossek, J., Pohl, J., Seiler, M., Trautmann, H., & Kerschke, P. (2023). A study on the effects of normalized TSP features for automated algorithm selection. Theoretical Computer Science, 940, 123–145. https://doi.org/10.1016/j.tcs.2022.10.019
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[77]
2023 | Conference Paper | LibreCat-ID: 48898
Seiler, M., Rook, J., Heins, J., Preuß, O. L., Bossek, J., & Trautmann, H. (n.d.). Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP. 2023 IEEE Symposium Series on Computational Intelligence (SSCI), 361–368. https://doi.org/10.1109/SSCI52147.2023.10372008
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[76]
2022 | Conference Paper | LibreCat-ID: 48861
Bossek, J., & Neumann, F. (2022). Exploring the Feature Space of TSP Instances Using Quality Diversity. Proceedings of the Genetic and Evolutionary Computation Conference, 186–194. https://doi.org/10.1145/3512290.3528851
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[75]
2022 | Conference Paper | LibreCat-ID: 48868
Bossek, J., Neumann, A., & Neumann, F. (2022). Evolutionary Diversity Optimization for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA. Proceedings of the Genetic and Evolutionary Computation Conference Companion, 824–842. https://doi.org/10.1145/3520304.3533626
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[74]
2022 | Conference Paper | LibreCat-ID: 48882
Heins, J., Rook, J., Schäpermeier, L., Kerschke, P., Bossek, J., & Trautmann, H. (2022). BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems. In G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, & T. Tusar (Eds.), Parallel Problem Solving from Nature (PPSN XVII) (pp. 192–206). Springer International Publishing. https://doi.org/10.1007/978-3-031-14714-2_14
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[73]
2022 | Conference Paper | LibreCat-ID: 48894
Nikfarjam, A., Neumann, A., Bossek, J., & Neumann, F. (2022). Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem. In G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, & T. Tu\v sar (Eds.), Parallel Problem Solving from Nature (PPSN XVII) (pp. 237–249). Springer International Publishing. https://doi.org/10.1007/978-3-031-14714-2_17
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[72]
2022 | Journal Article | LibreCat-ID: 48878
Clever, L., Pohl, J. S., Bossek, J., Kerschke, P., & Trautmann, H. (2022). Process-Oriented Stream Classification Pipeline: A Literature Review. Applied Sciences, 12(18), 9094. https://doi.org/10.3390/app12189094
LibreCat | DOI
 
[71]
2022 | Conference Paper | LibreCat-ID: 48896
Rook, J., Trautmann, H., Bossek, J., & Grimme, C. (2022). On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems. Proceedings of the Genetic and Evolutionary Computation Conference Companion, 356–359. https://doi.org/10.1145/3520304.3528998
LibreCat | DOI
 
[70]
2022 | Journal Article | LibreCat-ID: 46309
Clever, L., Pohl, J. S., Bossek, J., Kerschke, P., & Trautmann, H. (2022). Process-Oriented Stream Classification Pipeline: A Literature Review. Applied Sciences, 12(8), 1–44. https://doi.org/10.3390/app12189094
LibreCat | DOI
 
[69]
2022 | Conference Paper | LibreCat-ID: 46302
Heins, J., Rook, J., Schäpermeier, L., Kerschke, P., Bossek, J., & Trautmann, H. (2022). BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems. In G. Rudolph, A. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, & T. Tušar (Eds.), Parallel Problem Solving from Nature — PPSN XVII (pp. 192–206). Springer International Publishing.
LibreCat
 
[68]
2022 | Conference Paper | LibreCat-ID: 46305
Rook, J., Trautmann, H., Bossek, J., & Grimme, C. (2022). On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems. In J. Fieldsend & M. Wagner (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 356–359). Association for Computing Machinery. https://doi.org/10.1145/3520304.3528998
LibreCat | DOI
 
[67]
2021 | Conference Paper | LibreCat-ID: 48853
Bossek, J., Neumann, A., & Neumann, F. (2021). Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms. Proceedings of the Genetic and Evolutionary Computation Conference, 556–564. https://doi.org/10.1145/3449639.3459364
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[66]
2021 | Conference Paper | LibreCat-ID: 48855
Bossek, J., Neumann, A., & Neumann, F. (2021). Exact Counting and~Sampling of Optima for the Knapsack Problem. Learning and Intelligent Optimization, 40–54. https://doi.org/10.1007/978-3-030-92121-7_4
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[65]
2021 | Conference Paper | LibreCat-ID: 48860
Bossek, J., & Neumann, F. (2021). Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem. Proceedings of the Genetic and Evolutionary Computation Conference, 198–206. https://doi.org/10.1145/3449639.3459363
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[64]
2021 | Book Chapter | LibreCat-ID: 48862
Bossek, J., & Sudholt, D. (2021). Do Additional Optima Speed up Evolutionary Algorithms? In Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (pp. 1–11). Association for Computing Machinery.
LibreCat
 
[63]
2021 | Book Chapter | LibreCat-ID: 48881
Heins, J., Bossek, J., Pohl, J., Seiler, M., Trautmann, H., & Kerschke, P. (2021). On the Potential of Normalized TSP Features for Automated Algorithm Selection. In Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (pp. 1–15). Association for Computing Machinery.
LibreCat
 
[62]
2021 | Conference Paper | LibreCat-ID: 48876
Bossek, J., & Wagner, M. (2021). Generating Instances with Performance Differences for More than Just Two Algorithms. Proceedings of the Genetic and Evolutionary Computation Conference Companion, 1423–1432. https://doi.org/10.1145/3449726.3463165
LibreCat | DOI
 
[61]
2021 | Conference Paper | LibreCat-ID: 48893
Nikfarjam, A., Bossek, J., Neumann, A., & Neumann, F. (2021). Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem. Proceedings of the Genetic and Evolutionary Computation Conference, 600–608. https://doi.org/10.1145/3449639.3459384
LibreCat | DOI
 
[60]
2021 | Conference Paper | LibreCat-ID: 48891
Neumann, A., Bossek, J., & Neumann, F. (2021). Diversifying Greedy Sampling and Evolutionary Diversity Optimisation for Constrained Monotone Submodular Functions. Proceedings of the Genetic and Evolutionary Computation Conference, 261–269. https://doi.org/10.1145/3449639.3459385
LibreCat | DOI
 
[59]
2021 | Book Chapter | LibreCat-ID: 48892
Nikfarjam, A., Bossek, J., Neumann, A., & Neumann, F. (2021). 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 (pp. 1–11). Association for Computing Machinery.
LibreCat
 
[58]
2021 | Journal Article | LibreCat-ID: 48854
Bossek, J., Neumann, F., Peng, P., & Sudholt, D. (2021). Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem. Algorithmica, 83(10), 3148–3179. https://doi.org/10.1007/s00453-021-00838-3
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[57]
2021 | Conference Paper | LibreCat-ID: 46313
Heins, J., Bossek, J., Pohl, J., Seiler, M., Trautmann, H., & Kerschke, P. (2021). On the Potential of Normalized TSP Features for Automated Algorithm Selection. In for Computing Machinery Association (Ed.), Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of genetic Algorithms (FOGA XVI) (pp. 1–15). Association for Computing Machinery. https://doi.org/10.1145/3450218.3477308
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[56]
2020 | Conference Paper | LibreCat-ID: 48847
Bossek, J., Neumann, F., Peng, P., & Sudholt, D. (2020). More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization. Proceedings of the Genetic and Evolutionary Computation Conference, 1277–1285. https://doi.org/10.1145/3377930.3390174
LibreCat | DOI
 
[55]
2020 | Conference Paper | LibreCat-ID: 48849
Bossek, J., Doerr, C., Kerschke, P., Neumann, A., & Neumann, F. (2020). Evolving Sampling Strategies for One-Shot Optimization Tasks. Parallel Problem Solving from Nature (PPSN XVI), 111–124. https://doi.org/10.1007/978-3-030-58112-1_8
LibreCat | DOI
 
[54]
2020 | Conference Paper | LibreCat-ID: 48851
Bossek, J., Casel, K., Kerschke, P., & Neumann, F. (2020). The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics. Proceedings of the Genetic and Evolutionary Computation Conference, 1286–1294. https://doi.org/10.1145/3377930.3390243
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[53]
2020 | Conference Paper | LibreCat-ID: 48845
Bossek, J., Grimme, C., & Trautmann, H. (2020). Dynamic Bi-Objective Routing of Multiple Vehicles. Proceedings of the Genetic and Evolutionary Computation Conference, 166–174. https://doi.org/10.1145/3377930.3390146
LibreCat | DOI
 
[52]
2020 | Conference Paper | LibreCat-ID: 48844
Bossek, J., Kerschke, P., & Trautmann, H. (2020). Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection. 2020 IEEE Congress on Evolutionary Computation (CEC), 1–8. https://doi.org/10.1109/CEC48606.2020.9185613
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[51]
2020 | Conference Paper | LibreCat-ID: 48850
Bossek, J., Doerr, C., & Kerschke, P. (2020). Initial Design Strategies and Their Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB. Proceedings of the Genetic and Evolutionary Computation Conference, 778–786. https://doi.org/10.1145/3377930.3390155
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[50]
2020 | Conference Paper | LibreCat-ID: 48852
Bossek, J., Neumann, A., & Neumann, F. (2020). Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions. Parallel Problem Solving from Nature (PPSN XVI), 346–359. https://doi.org/10.1007/978-3-030-58112-1_24
LibreCat | DOI
 
[49]
2020 | Conference Paper | LibreCat-ID: 48846
Bossek, J., Grimme, C., Rudolph, G., & Trautmann, H. (2020). Towards Decision Support in Dynamic Bi-Objective Vehicle Routing. 2020 IEEE Congress on Evolutionary Computation (CEC), 1–8. https://doi.org/10.1109/CEC48606.2020.9185778
LibreCat | DOI
 
[48]
2020 | Conference Paper | LibreCat-ID: 48879
Do, A. V., Bossek, J., Neumann, A., & Neumann, F. (2020). Evolving Diverse Sets of Tours for the Travelling Salesperson Problem. Proceedings of the Genetic and Evolutionary Computation Conference, 681–689. https://doi.org/10.1145/3377930.3389844
LibreCat | DOI
 
[47]
2020 | Conference Paper | LibreCat-ID: 48895
Roostapour, V., Bossek, J., & Neumann, F. (2020). Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem. Proceedings of the 2020 Genetic and Evolutionary Computation Conference, 551–559. https://doi.org/10.1145/3377930.3390168
LibreCat | DOI
 
[46]
2020 | Conference Paper | LibreCat-ID: 48897
Seiler, M., Pohl, J., Bossek, J., Kerschke, P., & Trautmann, H. (2020). Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. Parallel Problem Solving from {Nature} (PPSN XVI), 48–64. https://doi.org/10.1007/978-3-030-58112-1_4
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[45]
2020 | Journal Article | LibreCat-ID: 48848
Bossek, J., Kerschke, P., & Trautmann, H. (2020). A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms. Applied Soft Computing, 88(C). https://doi.org/10.1016/j.asoc.2019.105901
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[44]
2020 | Journal Article | LibreCat-ID: 48836
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., & Weise, T. (2020). Benchmarking in Optimization: Best Practice and Open Issues. Corr.
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[43]
2020 | Conference Paper | LibreCat-ID: 46330
Seiler, M., Pohl, J., Bossek, J., Kerschke, P., & Trautmann, H. (2020). Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. In T. Bäck, M. Preuss, A. Deutz, H. Wang, C. Doerr, M. Emmerich, & H. Trautmann (Eds.), Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI) (pp. 48–64). https://doi.org/10.1007/978-3-030-58112-1_4
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[42]
2020 | Journal Article | LibreCat-ID: 46334
Bossek, J., Kerschke, P., & Trautmann, H. (2020). A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms. Applied Soft Computing, 88, 105901. https://doi.org/10.1016/j.asoc.2019.105901
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[41]
2020 | Conference Paper | LibreCat-ID: 46322
Bossek, J., Grimme, C., Rudolph, G., & Trautmann, H. (2020). Towards Decision Support in Dynamic Bi-Objective Vehicle Routing. Proceedings of the IEEE Congress on Evolutionary Computation (CEC), 1–8. https://doi.org/10.1109/CEC48606.2020.9185778
LibreCat | DOI
 
[40]
2020 | Conference Paper | LibreCat-ID: 46324
Bossek, J., Kerschke, P., & Trautmann, H. (2020). Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection. Proceedings of the IEEE Congress on Evolutionary Computation (CEC), 1–8.
LibreCat
 
[39]
2020 | Conference Paper | LibreCat-ID: 46323
Bossek, J., Grimme, C., & Trautmann, H. (2020). Dynamic Bi-Objective Routing of Multiple Vehicles. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20), 166–174.
LibreCat
 
[38]
2019 | Conference Paper | LibreCat-ID: 48841
Bossek, J., Grimme, C., Meisel, S., Rudolph, G., & Trautmann, H. (2019). Bi-Objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm. In K. Deb, E. Goodman, C. A. Coello Coello, K. Klamroth, K. Miettinen, S. Mostaghim, & P. Reed (Eds.), Evolutionary Multi-Criterion Optimization (EMO) (pp. 516–528). Springer International Publishing. https://doi.org/10.1007/978-3-030-12598-1_41
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[37]
2019 | Conference Paper | LibreCat-ID: 48842
Bossek, J., Kerschke, P., Neumann, A., Wagner, M., Neumann, F., & Trautmann, H. (2019). Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators. Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 58–71. https://doi.org/10.1145/3299904.3340307
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[36]
2019 | Conference Paper | LibreCat-ID: 48843
Bossek, J., Neumann, F., Peng, P., & Sudholt, D. (2019). Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring. Proceedings of the Genetic and Evolutionary Computation Conference, 1443–1451. https://doi.org/10.1145/3321707.3321792
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[35]
2019 | Conference Paper | LibreCat-ID: 48840
Bossek, J., Grimme, C., & Neumann, F. (2019). On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem. Proceedings of the Genetic and Evolutionary Computation Conference, 516–523. https://doi.org/10.1145/3321707.3321818
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[34]
2019 | Conference Paper | LibreCat-ID: 48858
Bossek, J., & Grimme, C. (2019). Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-criteria Shortest Path Problems. In R. Battiti, M. Brunato, I. Kotsireas, & P. M. Pardalos (Eds.), Learning and Intelligent Optimization (pp. 184–198). Springer International Publishing. https://doi.org/10.1007/978-3-030-05348-2_17
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[33]
2019 | Conference Paper | LibreCat-ID: 48870
Bossek, J., & Sudholt, D. (2019). Time Complexity Analysis of RLS and (1 + 1) EA for the Edge Coloring Problem. Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 102–115. https://doi.org/10.1145/3299904.3340311
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[32]
2019 | Conference Paper | LibreCat-ID: 48875
Bossek, J., & Trautmann, H. (2019). Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time. In R. Battiti, M. Brunato, I. Kotsireas, & P. M. Pardalos (Eds.), Learning and Intelligent Optimization (pp. 215–219). Springer International Publishing. https://doi.org/10.1007/978-3-030-05348-2_19
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[31]
2019 | Journal Article | LibreCat-ID: 48877
Casalicchio, G., Bossek, J., Lang, M., Kirchhoff, D., Kerschke, P., Hofner, B., Seibold, H., Vanschoren, J., & Bischl, B. (2019). OpenML: An R Package to Connect to the Machine Learning Platform OpenML. Computational Statistics, 34(3), 977–991. https://doi.org/10.1007/s00180-017-0742-2
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[30]
2019 | Conference Paper | LibreCat-ID: 46339
Bossek, J., Kerschke, P., Neumann, A., Wagner, M., Neumann, F., & Trautmann, H. (2019). Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators. In T. Friedrich, C. Doerr, & D. Arnold (Eds.), Proceedings of the 15$^th$ ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA XV) (pp. 58–71). https://doi.org/10.1145/3299904.3340307
LibreCat | DOI
 
[29]
2019 | Conference Paper | LibreCat-ID: 46338
Bossek, J., Grimme, C., Meisel, S., Rudolph, G., & Trautmann, H. (2019). Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm. In K. Deb, E. Goodman, C. C. A. Coello, K. Klamroth, K. Miettinen, S. Mostaghim, & P. Reed (Eds.), Evolutionary Multi-Criterion Optimization (EMO) (Vol. 11411, pp. 516–528). Springer International Publishing. https://doi.org/10.1007/978-3-030-12598-1_41
LibreCat | DOI
 
[28]
2019 | Conference Paper | LibreCat-ID: 46337
Bossek, J., & Trautmann, H. (2019). Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time. In R. Battiti, M. Brunato, I. Kotsireas, & P. Pardalos (Eds.), Learning and Intelligent Optimization (Vol. 11353, pp. 215–219). Springer.
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[27]
2018 | Conference Paper | LibreCat-ID: 48839
Bossek, J., Grimme, C., Meisel, S., Rudolph, G., & Trautmann, H. (2018). Local Search Effects in Bi-Objective Orienteering. Proceedings of the Genetic and Evolutionary Computation Conference, 585–592. https://doi.org/10.1145/3205455.3205548
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[26]
2018 | Conference Paper | LibreCat-ID: 48867
Bossek, J. (2018). Performance Assessment of Multi-Objective Evolutionary Algorithms with the R Package ecr. Proceedings of the Genetic and Evolutionary Computation Conference Companion, 1350–1356. https://doi.org/10.1145/3205651.3208312
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[25]
2018 | Conference Paper | LibreCat-ID: 48885
Kerschke, P., Bossek, J., & Trautmann, H. (2018). Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers. Proceedings of the Genetic and Evolutionary Computation Conference Companion, 1737–1744. https://doi.org/10.1145/3205651.3208233
LibreCat | DOI
 
[24]
2018 | Book | LibreCat-ID: 48880
Grimme, C., & Bossek, J. (2018). Einführung in die Optimierung - Konzepte, Methoden und Anwendungen. Springer Vieweg. https://doi.org/10.1007/978-3-658-21151-6
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[23]
2018 | Journal Article | LibreCat-ID: 48884
Kerschke, P., Kotthoff, L., Bossek, J., Hoos, H. H., & Trautmann, H. (2018). Leveraging TSP Solver Complementarity through Machine Learning. Evolutionary Computation, 26(4), 597–620. https://doi.org/10.1162/evco_a_00215
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[22]
2018 | Journal Article | LibreCat-ID: 48866
Bossek, J. (2018). Grapherator: A Modular Multi-Step Graph Generator. Journal of Open Source Software, 3(22), 528. https://doi.org/10.21105/joss.00528
LibreCat | DOI
 
[21]
2018 | Conference Paper | LibreCat-ID: 46348
Bossek, J., Grimme, C., Meisel, S., Rudolph, G., & Trautmann, H. (2018). Local Search Effects in Bi-Objective Orienteering. Proceedings of the Genetic and Evolutionary Computation Conference, 585–592. https://doi.org/10.1145/3205455.3205548
LibreCat | DOI
 
[20]
2018 | Journal Article | LibreCat-ID: 46352
Kerschke, P., Kotthoff, L., Bossek, J., Hoos, H. H., & Trautmann, H. (2018). Leveraging TSP Solver Complementarity through Machine Learning. Evolutionary Computation (ECJ), 26(4), 597–620. https://doi.org/10.1162/evco_a_00215
LibreCat | DOI
 
[19]
2018 | Conference Paper | LibreCat-ID: 46349
Kerschke, P., Bossek, J., & Trautmann, H. (2018). Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’18) Companion, 1737–1744. https://doi.org/10.1145/3205651.3208233
LibreCat | DOI
 
[18]
2017 | Conference Paper | LibreCat-ID: 48863
Bossek, J. (2017). Ecr 2.0: A Modular Framework for Evolutionary Computation in R. Proceedings of the Genetic and Evolutionary Computation Conference Companion, 1187–1193. https://doi.org/10.1145/3067695.3082470
LibreCat | DOI
 
[17]
2017 | Conference Paper | LibreCat-ID: 48857
Bossek, J., & Grimme, C. (2017). A Pareto-Beneficial Sub-Tree Mutation for the Multi-Criteria Minimum Spanning Tree Problem. 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 1–8. https://doi.org/10.1109/SSCI.2017.8285183
LibreCat | DOI
 
[16]
2017 | Conference Paper | LibreCat-ID: 48856
Bossek, J., & Grimme, C. (2017). An Extended Mutation-Based Priority-Rule Integration Concept for Multi-Objective Machine Scheduling. 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 1–8. https://doi.org/10.1109/SSCI.2017.8285224
LibreCat | DOI
 
[15]
2017 | Journal Article | LibreCat-ID: 48864
Bossek, J. (2017). mcMST: A Toolbox for the Multi-Criteria Minimum Spanning Tree Problem. Journal of Open Source Software, 2(17), 374. https://doi.org/10.21105/joss.00374
LibreCat | DOI
 
[14]
2017 | Journal Article | LibreCat-ID: 48865
Bossek, J. (2017). Smoof: Single- and Multi-Objective Optimization Test Functions. The R Journal, 9(1), 103–113.
LibreCat
 
[13]
2017 | Journal Article | LibreCat-ID: 48837
Bischl, B., Richter, J., Bossek, J., Horn, D., Thomas, J., & Lang, M. (2017). mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions. CoRR.
LibreCat
 
[12]
2016 | Conference Paper | LibreCat-ID: 48873
Bossek, J., & Trautmann, H. (2016). Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers. In P. Festa, M. Sellmann, & J. Vanschoren (Eds.), Learning and Intelligent Optimization (pp. 48–59). Springer International Publishing. https://doi.org/10.1007/978-3-319-50349-3_4
LibreCat | DOI
 
[11]
2016 | Conference Paper | LibreCat-ID: 48874
Bossek, J., & Trautmann, H. (2016). Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference. Proceedings of the XV International Conference of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037, 3–12. https://doi.org/10.1007/978-3-319-49130-1_1
LibreCat | DOI
 
[10]
2016 | Conference Paper | LibreCat-ID: 46365
Bossek, J., & Trautmann, H. (2016). Evolving Instances for Maximizing Performance Differences of State-of-The-Art Inexact TSP Solvers. In P. Festa, M. Sellmann, & J. Vanschoren (Eds.), Learning and Intelligent Optimization (Vol. 10079, pp. 48–59). Springer International Publishing. https://doi.org/10.1007/978-3-319-50349-3_4
LibreCat | DOI
 
[9]
2016 | Conference Paper | LibreCat-ID: 46366
Bossek, J., & Trautmann, H. (2016). Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference. In G. Adorni, S. Cagnoni, M. Gori, & M. Maratea (Eds.), AI*IA 2016 Advances in Artificial Intelligence (Vol. 10037, pp. 3–12). Springer. https://doi.org/10.1007/978-3-319-49130-1_1
LibreCat | DOI
 
[8]
2015 | Conference Paper | LibreCat-ID: 48838
Bossek, J., Bischl, B., Wagner, T., & Rudolph, G. (2015). Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement. Proceedings of the Genetic and Evolutionary Computation Conference, 1319–1326. https://doi.org/10.1145/2739480.2754673
LibreCat | DOI
 
[7]
2015 | Conference Paper | LibreCat-ID: 48887
Meisel, S., Grimme, C., Bossek, J., Wölck, M., Rudolph, G., & Trautmann, H. (2015). Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle. Proceedings of the Genetic and Evolutionary Computation Conference , 425–432. https://doi.org/10.1145/2739480.2754705
LibreCat | DOI
 
[6]
2015 | Conference Paper | LibreCat-ID: 46377
Meisel, S., Grimme, C., Bossek, J., Wölck, M., Rudolph, G., & Trautmann, H. (2015). Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’15), 425–432. https://doi.org/10.1145/2739480.2754705
LibreCat | DOI
 
[5]
2013 | Journal Article | LibreCat-ID: 48889
Mersmann, O., Bischl, B., Trautmann, H., Wagner, M., Bossek, J., & Neumann, F. (2013). A Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling Salesperson Problem. Annals of Mathematics and Artificial Intelligence, 69(2), 151–182. https://doi.org/10.1007/s10472-013-9341-2
LibreCat | DOI
 
[4]
2013 | Journal Article | LibreCat-ID: 46394
Mersmann, O., Bischl, B., Trautmann, H., Wagner, M., Bossek, J., & Neumann, F. (2013). A Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling Salesman Problem. Annals of Mathematics and Artificial Intelligence, 69, 151–182.
LibreCat
 
[3]
2012 | Conference Paper | LibreCat-ID: 48890
Mersmann, O., Bischl, B., Bossek, J., Trautmann, H., Wagner, M., & Neumann, F. (2012). Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness. Revised Selected Papers of the 6th International Conference on Learning and Intelligent Optimization - Volume 7219, 115–129.
LibreCat
 
[2]
2012 | Book Chapter | LibreCat-ID: 48888
Mersmann, O., Bischl, B., Bossek, J., Trautmann, H., Wagner, M., & Neumann, F. (2012). Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness. In Learning and Intelligent Optimization (Vol. 7219, pp. 115–129). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-34413-8_9
LibreCat | DOI
 
[1]
2012 | Conference Paper | LibreCat-ID: 46398
Mersmann, O., Bischl, B., Bossek, J., Trautmann, H., Wagner, M., & Neumann, F. (2012). Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness. In Y. Hamadi & M. Schoenauer (Eds.), Learning and Intelligent Optimization (pp. 115–129). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-34413-8_9
LibreCat | DOI
 
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[90]
2025 | Journal Article | LibreCat-ID: 59073
Rook, J. G., Benjamins, C., Bossek, J., Trautmann, H., Hoos, H. H., & Lindauer, M. (2025). MO-SMAC: Multi-objective Sequential Model-based Algorithm Configuration. Evolutionary Computation, 1–25. https://doi.org/10.1162/evco_a_00371
LibreCat | DOI
 
[89]
2025 | Conference Paper | LibreCat-ID: 60812
Preuß, O. L., Mensendiek, C., Rook, J., Bossek, J., & Trautmann, H. (2025). Automated Algorithm Configuration and Systematic Benchmarking for Heterogeneous MNK-Landscapes. In B. Filipic (Ed.), Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2025, NH Malaga Hotel, Malaga, Spain, July 14-18, 2025 (pp. 58–66). ACM. https://doi.org/10.1145/3712256.3726481
LibreCat | DOI
 
[88]
2025 | Conference Paper | LibreCat-ID: 63703
Hoffbauer, T., Hoos, H. H., & Bossek, J. (2025). KernelMatmul: Scaling Gaussian Processes to Large Time Series. In T. Walsh, J. Shah, & Z. Kolter (Eds.), AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25 - March 4, 2025, Philadelphia, PA, USA (pp. 17223–17230). AAAI Press. https://doi.org/10.1609/AAAI.V39I16.33893
LibreCat | DOI
 
[87]
2025 | Conference Paper | LibreCat-ID: 63704
Wittner, D., & Bossek, J. (2025). Cluster Prevention in Evolutionary Diversity Optimization for Parallel Machine Scheduling. In B. Filipic (Ed.), Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2025, NH Malaga Hotel, Malaga, Spain, July 14-18, 2025 (pp. 340–348). ACM. https://doi.org/10.1145/3712256.3726357
LibreCat | DOI
 
[86]
2024 | Conference Paper | LibreCat-ID: 52749
Seiler, M., Rook, J., Heins, J., Preuß, O. L., Bossek, J., & Trautmann, H. (2024). Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP. 2023 IEEE Symposium Series on Computational Intelligence (SSCI). https://doi.org/10.1109/ssci52147.2023.10372008
LibreCat | DOI
 
[85]
2024 | Conference Paper | LibreCat-ID: 63706
Schmidbauer, M., Opris, A., Bossek, J., Neumann, F., & Sudholt, D. (2024). Guiding Quality Diversity on Monotone Submodular Functions: Customising the Feature Space by Adding Boolean Conjunctions. In X. Li & J. Handl (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2024, Melbourne, VIC, Australia, July 14-18, 2024. ACM. https://doi.org/10.1145/3638529.3654160
LibreCat | DOI
 
[84]
2024 | Conference Paper | LibreCat-ID: 63705
Bossek, J., & Grimme, C. (2024). Generalised Kruskal Mutation for the Multi-Objective Minimum Spanning Tree Problem. In X. Li & J. Handl (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2024, Melbourne, VIC, Australia, July 14-18, 2024. ACM. https://doi.org/10.1145/3638529.3654165
LibreCat | DOI
 
[83]
2023 | Conference Paper | LibreCat-ID: 48869
Bossek, J., Neumann, A., & Neumann, F. (2023). On the Impact of Basic Mutation Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson Problem. Proceedings of the Genetic and Evolutionary Computation Conference, 248–256. https://doi.org/10.1145/3583131.3590384
LibreCat | DOI
 
[82]
2023 | Conference Paper | LibreCat-ID: 48872
Bossek, J., & Sudholt, D. (2023). Runtime Analysis of Quality Diversity Algorithms. Proceedings of the Genetic and Evolutionary Computation Conference, 1546–1554. https://doi.org/10.1145/3583131.3590383
LibreCat | DOI
 
[81]
2023 | Conference Paper | LibreCat-ID: 48886
Marrero, A., Segredo, E., Hart, E., Bossek, J., & Neumann, A. (2023). Generating Diverse and Discriminatory Knapsack Instances by Searching for Novelty in Variable Dimensions of Feature-Space. Proceedings of the Genetic} and Evolutionary Computation Conference, 312–320. https://doi.org/10.1145/3583131.3590504
LibreCat | DOI
 
[80]
2023 | Journal Article | LibreCat-ID: 48871
Bossek, J., & Sudholt, D. (2023). Do Additional Target Points Speed Up Evolutionary Algorithms? Theoretical Computer Science, 113757. https://doi.org/10.1016/j.tcs.2023.113757
LibreCat | DOI
 
[79]
2023 | Journal Article | LibreCat-ID: 48859
Bossek, J., & Grimme, C. (2023). On Single-Objective Sub-Graph-Based Mutation for Solving the Bi-Objective Minimum Spanning Tree Problem. Evolutionary Computation, 1–35. https://doi.org/10.1162/evco_a_00335
LibreCat | DOI
 
[78]
2023 | Journal Article | LibreCat-ID: 46310
Heins, J., Bossek, J., Pohl, J., Seiler, M., Trautmann, H., & Kerschke, P. (2023). A study on the effects of normalized TSP features for automated algorithm selection. Theoretical Computer Science, 940, 123–145. https://doi.org/10.1016/j.tcs.2022.10.019
LibreCat | DOI
 
[77]
2023 | Conference Paper | LibreCat-ID: 48898
Seiler, M., Rook, J., Heins, J., Preuß, O. L., Bossek, J., & Trautmann, H. (n.d.). Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP. 2023 IEEE Symposium Series on Computational Intelligence (SSCI), 361–368. https://doi.org/10.1109/SSCI52147.2023.10372008
LibreCat | DOI
 
[76]
2022 | Conference Paper | LibreCat-ID: 48861
Bossek, J., & Neumann, F. (2022). Exploring the Feature Space of TSP Instances Using Quality Diversity. Proceedings of the Genetic and Evolutionary Computation Conference, 186–194. https://doi.org/10.1145/3512290.3528851
LibreCat | DOI
 
[75]
2022 | Conference Paper | LibreCat-ID: 48868
Bossek, J., Neumann, A., & Neumann, F. (2022). Evolutionary Diversity Optimization for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA. Proceedings of the Genetic and Evolutionary Computation Conference Companion, 824–842. https://doi.org/10.1145/3520304.3533626
LibreCat | DOI
 
[74]
2022 | Conference Paper | LibreCat-ID: 48882
Heins, J., Rook, J., Schäpermeier, L., Kerschke, P., Bossek, J., & Trautmann, H. (2022). BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems. In G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, & T. Tusar (Eds.), Parallel Problem Solving from Nature (PPSN XVII) (pp. 192–206). Springer International Publishing. https://doi.org/10.1007/978-3-031-14714-2_14
LibreCat | DOI
 
[73]
2022 | Conference Paper | LibreCat-ID: 48894
Nikfarjam, A., Neumann, A., Bossek, J., & Neumann, F. (2022). Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem. In G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, & T. Tu\v sar (Eds.), Parallel Problem Solving from Nature (PPSN XVII) (pp. 237–249). Springer International Publishing. https://doi.org/10.1007/978-3-031-14714-2_17
LibreCat | DOI
 
[72]
2022 | Journal Article | LibreCat-ID: 48878
Clever, L., Pohl, J. S., Bossek, J., Kerschke, P., & Trautmann, H. (2022). Process-Oriented Stream Classification Pipeline: A Literature Review. Applied Sciences, 12(18), 9094. https://doi.org/10.3390/app12189094
LibreCat | DOI
 
[71]
2022 | Conference Paper | LibreCat-ID: 48896
Rook, J., Trautmann, H., Bossek, J., & Grimme, C. (2022). On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems. Proceedings of the Genetic and Evolutionary Computation Conference Companion, 356–359. https://doi.org/10.1145/3520304.3528998
LibreCat | DOI
 
[70]
2022 | Journal Article | LibreCat-ID: 46309
Clever, L., Pohl, J. S., Bossek, J., Kerschke, P., & Trautmann, H. (2022). Process-Oriented Stream Classification Pipeline: A Literature Review. Applied Sciences, 12(8), 1–44. https://doi.org/10.3390/app12189094
LibreCat | DOI
 
[69]
2022 | Conference Paper | LibreCat-ID: 46302
Heins, J., Rook, J., Schäpermeier, L., Kerschke, P., Bossek, J., & Trautmann, H. (2022). BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems. In G. Rudolph, A. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, & T. Tušar (Eds.), Parallel Problem Solving from Nature — PPSN XVII (pp. 192–206). Springer International Publishing.
LibreCat
 
[68]
2022 | Conference Paper | LibreCat-ID: 46305
Rook, J., Trautmann, H., Bossek, J., & Grimme, C. (2022). On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems. In J. Fieldsend & M. Wagner (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 356–359). Association for Computing Machinery. https://doi.org/10.1145/3520304.3528998
LibreCat | DOI
 
[67]
2021 | Conference Paper | LibreCat-ID: 48853
Bossek, J., Neumann, A., & Neumann, F. (2021). Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms. Proceedings of the Genetic and Evolutionary Computation Conference, 556–564. https://doi.org/10.1145/3449639.3459364
LibreCat | DOI
 
[66]
2021 | Conference Paper | LibreCat-ID: 48855
Bossek, J., Neumann, A., & Neumann, F. (2021). Exact Counting and~Sampling of Optima for the Knapsack Problem. Learning and Intelligent Optimization, 40–54. https://doi.org/10.1007/978-3-030-92121-7_4
LibreCat | DOI
 
[65]
2021 | Conference Paper | LibreCat-ID: 48860
Bossek, J., & Neumann, F. (2021). Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem. Proceedings of the Genetic and Evolutionary Computation Conference, 198–206. https://doi.org/10.1145/3449639.3459363
LibreCat | DOI
 
[64]
2021 | Book Chapter | LibreCat-ID: 48862
Bossek, J., & Sudholt, D. (2021). Do Additional Optima Speed up Evolutionary Algorithms? In Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (pp. 1–11). Association for Computing Machinery.
LibreCat
 
[63]
2021 | Book Chapter | LibreCat-ID: 48881
Heins, J., Bossek, J., Pohl, J., Seiler, M., Trautmann, H., & Kerschke, P. (2021). On the Potential of Normalized TSP Features for Automated Algorithm Selection. In Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (pp. 1–15). Association for Computing Machinery.
LibreCat
 
[62]
2021 | Conference Paper | LibreCat-ID: 48876
Bossek, J., & Wagner, M. (2021). Generating Instances with Performance Differences for More than Just Two Algorithms. Proceedings of the Genetic and Evolutionary Computation Conference Companion, 1423–1432. https://doi.org/10.1145/3449726.3463165
LibreCat | DOI
 
[61]
2021 | Conference Paper | LibreCat-ID: 48893
Nikfarjam, A., Bossek, J., Neumann, A., & Neumann, F. (2021). Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem. Proceedings of the Genetic and Evolutionary Computation Conference, 600–608. https://doi.org/10.1145/3449639.3459384
LibreCat | DOI
 
[60]
2021 | Conference Paper | LibreCat-ID: 48891
Neumann, A., Bossek, J., & Neumann, F. (2021). Diversifying Greedy Sampling and Evolutionary Diversity Optimisation for Constrained Monotone Submodular Functions. Proceedings of the Genetic and Evolutionary Computation Conference, 261–269. https://doi.org/10.1145/3449639.3459385
LibreCat | DOI
 
[59]
2021 | Book Chapter | LibreCat-ID: 48892
Nikfarjam, A., Bossek, J., Neumann, A., & Neumann, F. (2021). 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 (pp. 1–11). Association for Computing Machinery.
LibreCat
 
[58]
2021 | Journal Article | LibreCat-ID: 48854
Bossek, J., Neumann, F., Peng, P., & Sudholt, D. (2021). Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem. Algorithmica, 83(10), 3148–3179. https://doi.org/10.1007/s00453-021-00838-3
LibreCat | DOI
 
[57]
2021 | Conference Paper | LibreCat-ID: 46313
Heins, J., Bossek, J., Pohl, J., Seiler, M., Trautmann, H., & Kerschke, P. (2021). On the Potential of Normalized TSP Features for Automated Algorithm Selection. In for Computing Machinery Association (Ed.), Proceedings of the 16$^th$ ACM/SIGEVO Conference on Foundations of genetic Algorithms (FOGA XVI) (pp. 1–15). Association for Computing Machinery. https://doi.org/10.1145/3450218.3477308
LibreCat | DOI
 
[56]
2020 | Conference Paper | LibreCat-ID: 48847
Bossek, J., Neumann, F., Peng, P., & Sudholt, D. (2020). More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization. Proceedings of the Genetic and Evolutionary Computation Conference, 1277–1285. https://doi.org/10.1145/3377930.3390174
LibreCat | DOI
 
[55]
2020 | Conference Paper | LibreCat-ID: 48849
Bossek, J., Doerr, C., Kerschke, P., Neumann, A., & Neumann, F. (2020). Evolving Sampling Strategies for One-Shot Optimization Tasks. Parallel Problem Solving from Nature (PPSN XVI), 111–124. https://doi.org/10.1007/978-3-030-58112-1_8
LibreCat | DOI
 
[54]
2020 | Conference Paper | LibreCat-ID: 48851
Bossek, J., Casel, K., Kerschke, P., & Neumann, F. (2020). The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics. Proceedings of the Genetic and Evolutionary Computation Conference, 1286–1294. https://doi.org/10.1145/3377930.3390243
LibreCat | DOI
 
[53]
2020 | Conference Paper | LibreCat-ID: 48845
Bossek, J., Grimme, C., & Trautmann, H. (2020). Dynamic Bi-Objective Routing of Multiple Vehicles. Proceedings of the Genetic and Evolutionary Computation Conference, 166–174. https://doi.org/10.1145/3377930.3390146
LibreCat | DOI
 
[52]
2020 | Conference Paper | LibreCat-ID: 48844
Bossek, J., Kerschke, P., & Trautmann, H. (2020). Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection. 2020 IEEE Congress on Evolutionary Computation (CEC), 1–8. https://doi.org/10.1109/CEC48606.2020.9185613
LibreCat | DOI
 
[51]
2020 | Conference Paper | LibreCat-ID: 48850
Bossek, J., Doerr, C., & Kerschke, P. (2020). Initial Design Strategies and Their Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB. Proceedings of the Genetic and Evolutionary Computation Conference, 778–786. https://doi.org/10.1145/3377930.3390155
LibreCat | DOI
 
[50]
2020 | Conference Paper | LibreCat-ID: 48852
Bossek, J., Neumann, A., & Neumann, F. (2020). Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions. Parallel Problem Solving from Nature (PPSN XVI), 346–359. https://doi.org/10.1007/978-3-030-58112-1_24
LibreCat | DOI
 
[49]
2020 | Conference Paper | LibreCat-ID: 48846
Bossek, J., Grimme, C., Rudolph, G., & Trautmann, H. (2020). Towards Decision Support in Dynamic Bi-Objective Vehicle Routing. 2020 IEEE Congress on Evolutionary Computation (CEC), 1–8. https://doi.org/10.1109/CEC48606.2020.9185778
LibreCat | DOI
 
[48]
2020 | Conference Paper | LibreCat-ID: 48879
Do, A. V., Bossek, J., Neumann, A., & Neumann, F. (2020). Evolving Diverse Sets of Tours for the Travelling Salesperson Problem. Proceedings of the Genetic and Evolutionary Computation Conference, 681–689. https://doi.org/10.1145/3377930.3389844
LibreCat | DOI
 
[47]
2020 | Conference Paper | LibreCat-ID: 48895
Roostapour, V., Bossek, J., & Neumann, F. (2020). Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem. Proceedings of the 2020 Genetic and Evolutionary Computation Conference, 551–559. https://doi.org/10.1145/3377930.3390168
LibreCat | DOI
 
[46]
2020 | Conference Paper | LibreCat-ID: 48897
Seiler, M., Pohl, J., Bossek, J., Kerschke, P., & Trautmann, H. (2020). Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. Parallel Problem Solving from {Nature} (PPSN XVI), 48–64. https://doi.org/10.1007/978-3-030-58112-1_4
LibreCat | DOI
 
[45]
2020 | Journal Article | LibreCat-ID: 48848
Bossek, J., Kerschke, P., & Trautmann, H. (2020). A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms. Applied Soft Computing, 88(C). https://doi.org/10.1016/j.asoc.2019.105901
LibreCat | DOI
 
[44]
2020 | Journal Article | LibreCat-ID: 48836
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., & Weise, T. (2020). Benchmarking in Optimization: Best Practice and Open Issues. Corr.
LibreCat
 
[43]
2020 | Conference Paper | LibreCat-ID: 46330
Seiler, M., Pohl, J., Bossek, J., Kerschke, P., & Trautmann, H. (2020). Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. In T. Bäck, M. Preuss, A. Deutz, H. Wang, C. Doerr, M. Emmerich, & H. Trautmann (Eds.), Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI) (pp. 48–64). https://doi.org/10.1007/978-3-030-58112-1_4
LibreCat | DOI
 
[42]
2020 | Journal Article | LibreCat-ID: 46334
Bossek, J., Kerschke, P., & Trautmann, H. (2020). A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms. Applied Soft Computing, 88, 105901. https://doi.org/10.1016/j.asoc.2019.105901
LibreCat | DOI
 
[41]
2020 | Conference Paper | LibreCat-ID: 46322
Bossek, J., Grimme, C., Rudolph, G., & Trautmann, H. (2020). Towards Decision Support in Dynamic Bi-Objective Vehicle Routing. Proceedings of the IEEE Congress on Evolutionary Computation (CEC), 1–8. https://doi.org/10.1109/CEC48606.2020.9185778
LibreCat | DOI
 
[40]
2020 | Conference Paper | LibreCat-ID: 46324
Bossek, J., Kerschke, P., & Trautmann, H. (2020). Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection. Proceedings of the IEEE Congress on Evolutionary Computation (CEC), 1–8.
LibreCat
 
[39]
2020 | Conference Paper | LibreCat-ID: 46323
Bossek, J., Grimme, C., & Trautmann, H. (2020). Dynamic Bi-Objective Routing of Multiple Vehicles. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20), 166–174.
LibreCat
 
[38]
2019 | Conference Paper | LibreCat-ID: 48841
Bossek, J., Grimme, C., Meisel, S., Rudolph, G., & Trautmann, H. (2019). Bi-Objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm. In K. Deb, E. Goodman, C. A. Coello Coello, K. Klamroth, K. Miettinen, S. Mostaghim, & P. Reed (Eds.), Evolutionary Multi-Criterion Optimization (EMO) (pp. 516–528). Springer International Publishing. https://doi.org/10.1007/978-3-030-12598-1_41
LibreCat | DOI
 
[37]
2019 | Conference Paper | LibreCat-ID: 48842
Bossek, J., Kerschke, P., Neumann, A., Wagner, M., Neumann, F., & Trautmann, H. (2019). Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators. Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 58–71. https://doi.org/10.1145/3299904.3340307
LibreCat | DOI
 
[36]
2019 | Conference Paper | LibreCat-ID: 48843
Bossek, J., Neumann, F., Peng, P., & Sudholt, D. (2019). Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring. Proceedings of the Genetic and Evolutionary Computation Conference, 1443–1451. https://doi.org/10.1145/3321707.3321792
LibreCat | DOI
 
[35]
2019 | Conference Paper | LibreCat-ID: 48840
Bossek, J., Grimme, C., & Neumann, F. (2019). On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem. Proceedings of the Genetic and Evolutionary Computation Conference, 516–523. https://doi.org/10.1145/3321707.3321818
LibreCat | DOI
 
[34]
2019 | Conference Paper | LibreCat-ID: 48858
Bossek, J., & Grimme, C. (2019). Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-criteria Shortest Path Problems. In R. Battiti, M. Brunato, I. Kotsireas, & P. M. Pardalos (Eds.), Learning and Intelligent Optimization (pp. 184–198). Springer International Publishing. https://doi.org/10.1007/978-3-030-05348-2_17
LibreCat | DOI
 
[33]
2019 | Conference Paper | LibreCat-ID: 48870
Bossek, J., & Sudholt, D. (2019). Time Complexity Analysis of RLS and (1 + 1) EA for the Edge Coloring Problem. Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 102–115. https://doi.org/10.1145/3299904.3340311
LibreCat | DOI
 
[32]
2019 | Conference Paper | LibreCat-ID: 48875
Bossek, J., & Trautmann, H. (2019). Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time. In R. Battiti, M. Brunato, I. Kotsireas, & P. M. Pardalos (Eds.), Learning and Intelligent Optimization (pp. 215–219). Springer International Publishing. https://doi.org/10.1007/978-3-030-05348-2_19
LibreCat | DOI
 
[31]
2019 | Journal Article | LibreCat-ID: 48877
Casalicchio, G., Bossek, J., Lang, M., Kirchhoff, D., Kerschke, P., Hofner, B., Seibold, H., Vanschoren, J., & Bischl, B. (2019). OpenML: An R Package to Connect to the Machine Learning Platform OpenML. Computational Statistics, 34(3), 977–991. https://doi.org/10.1007/s00180-017-0742-2
LibreCat | DOI
 
[30]
2019 | Conference Paper | LibreCat-ID: 46339
Bossek, J., Kerschke, P., Neumann, A., Wagner, M., Neumann, F., & Trautmann, H. (2019). Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators. In T. Friedrich, C. Doerr, & D. Arnold (Eds.), Proceedings of the 15$^th$ ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA XV) (pp. 58–71). https://doi.org/10.1145/3299904.3340307
LibreCat | DOI
 
[29]
2019 | Conference Paper | LibreCat-ID: 46338
Bossek, J., Grimme, C., Meisel, S., Rudolph, G., & Trautmann, H. (2019). Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm. In K. Deb, E. Goodman, C. C. A. Coello, K. Klamroth, K. Miettinen, S. Mostaghim, & P. Reed (Eds.), Evolutionary Multi-Criterion Optimization (EMO) (Vol. 11411, pp. 516–528). Springer International Publishing. https://doi.org/10.1007/978-3-030-12598-1_41
LibreCat | DOI
 
[28]
2019 | Conference Paper | LibreCat-ID: 46337
Bossek, J., & Trautmann, H. (2019). Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time. In R. Battiti, M. Brunato, I. Kotsireas, & P. Pardalos (Eds.), Learning and Intelligent Optimization (Vol. 11353, pp. 215–219). Springer.
LibreCat
 
[27]
2018 | Conference Paper | LibreCat-ID: 48839
Bossek, J., Grimme, C., Meisel, S., Rudolph, G., & Trautmann, H. (2018). Local Search Effects in Bi-Objective Orienteering. Proceedings of the Genetic and Evolutionary Computation Conference, 585–592. https://doi.org/10.1145/3205455.3205548
LibreCat | DOI
 
[26]
2018 | Conference Paper | LibreCat-ID: 48867
Bossek, J. (2018). Performance Assessment of Multi-Objective Evolutionary Algorithms with the R Package ecr. Proceedings of the Genetic and Evolutionary Computation Conference Companion, 1350–1356. https://doi.org/10.1145/3205651.3208312
LibreCat | DOI
 
[25]
2018 | Conference Paper | LibreCat-ID: 48885
Kerschke, P., Bossek, J., & Trautmann, H. (2018). Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers. Proceedings of the Genetic and Evolutionary Computation Conference Companion, 1737–1744. https://doi.org/10.1145/3205651.3208233
LibreCat | DOI
 
[24]
2018 | Book | LibreCat-ID: 48880
Grimme, C., & Bossek, J. (2018). Einführung in die Optimierung - Konzepte, Methoden und Anwendungen. Springer Vieweg. https://doi.org/10.1007/978-3-658-21151-6
LibreCat | DOI
 
[23]
2018 | Journal Article | LibreCat-ID: 48884
Kerschke, P., Kotthoff, L., Bossek, J., Hoos, H. H., & Trautmann, H. (2018). Leveraging TSP Solver Complementarity through Machine Learning. Evolutionary Computation, 26(4), 597–620. https://doi.org/10.1162/evco_a_00215
LibreCat | DOI
 
[22]
2018 | Journal Article | LibreCat-ID: 48866
Bossek, J. (2018). Grapherator: A Modular Multi-Step Graph Generator. Journal of Open Source Software, 3(22), 528. https://doi.org/10.21105/joss.00528
LibreCat | DOI
 
[21]
2018 | Conference Paper | LibreCat-ID: 46348
Bossek, J., Grimme, C., Meisel, S., Rudolph, G., & Trautmann, H. (2018). Local Search Effects in Bi-Objective Orienteering. Proceedings of the Genetic and Evolutionary Computation Conference, 585–592. https://doi.org/10.1145/3205455.3205548
LibreCat | DOI
 
[20]
2018 | Journal Article | LibreCat-ID: 46352
Kerschke, P., Kotthoff, L., Bossek, J., Hoos, H. H., & Trautmann, H. (2018). Leveraging TSP Solver Complementarity through Machine Learning. Evolutionary Computation (ECJ), 26(4), 597–620. https://doi.org/10.1162/evco_a_00215
LibreCat | DOI
 
[19]
2018 | Conference Paper | LibreCat-ID: 46349
Kerschke, P., Bossek, J., & Trautmann, H. (2018). Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’18) Companion, 1737–1744. https://doi.org/10.1145/3205651.3208233
LibreCat | DOI
 
[18]
2017 | Conference Paper | LibreCat-ID: 48863
Bossek, J. (2017). Ecr 2.0: A Modular Framework for Evolutionary Computation in R. Proceedings of the Genetic and Evolutionary Computation Conference Companion, 1187–1193. https://doi.org/10.1145/3067695.3082470
LibreCat | DOI
 
[17]
2017 | Conference Paper | LibreCat-ID: 48857
Bossek, J., & Grimme, C. (2017). A Pareto-Beneficial Sub-Tree Mutation for the Multi-Criteria Minimum Spanning Tree Problem. 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 1–8. https://doi.org/10.1109/SSCI.2017.8285183
LibreCat | DOI
 
[16]
2017 | Conference Paper | LibreCat-ID: 48856
Bossek, J., & Grimme, C. (2017). An Extended Mutation-Based Priority-Rule Integration Concept for Multi-Objective Machine Scheduling. 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 1–8. https://doi.org/10.1109/SSCI.2017.8285224
LibreCat | DOI
 
[15]
2017 | Journal Article | LibreCat-ID: 48864
Bossek, J. (2017). mcMST: A Toolbox for the Multi-Criteria Minimum Spanning Tree Problem. Journal of Open Source Software, 2(17), 374. https://doi.org/10.21105/joss.00374
LibreCat | DOI
 
[14]
2017 | Journal Article | LibreCat-ID: 48865
Bossek, J. (2017). Smoof: Single- and Multi-Objective Optimization Test Functions. The R Journal, 9(1), 103–113.
LibreCat
 
[13]
2017 | Journal Article | LibreCat-ID: 48837
Bischl, B., Richter, J., Bossek, J., Horn, D., Thomas, J., & Lang, M. (2017). mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions. CoRR.
LibreCat
 
[12]
2016 | Conference Paper | LibreCat-ID: 48873
Bossek, J., & Trautmann, H. (2016). Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers. In P. Festa, M. Sellmann, & J. Vanschoren (Eds.), Learning and Intelligent Optimization (pp. 48–59). Springer International Publishing. https://doi.org/10.1007/978-3-319-50349-3_4
LibreCat | DOI
 
[11]
2016 | Conference Paper | LibreCat-ID: 48874
Bossek, J., & Trautmann, H. (2016). Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference. Proceedings of the XV International Conference of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037, 3–12. https://doi.org/10.1007/978-3-319-49130-1_1
LibreCat | DOI
 
[10]
2016 | Conference Paper | LibreCat-ID: 46365
Bossek, J., & Trautmann, H. (2016). Evolving Instances for Maximizing Performance Differences of State-of-The-Art Inexact TSP Solvers. In P. Festa, M. Sellmann, & J. Vanschoren (Eds.), Learning and Intelligent Optimization (Vol. 10079, pp. 48–59). Springer International Publishing. https://doi.org/10.1007/978-3-319-50349-3_4
LibreCat | DOI
 
[9]
2016 | Conference Paper | LibreCat-ID: 46366
Bossek, J., & Trautmann, H. (2016). Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference. In G. Adorni, S. Cagnoni, M. Gori, & M. Maratea (Eds.), AI*IA 2016 Advances in Artificial Intelligence (Vol. 10037, pp. 3–12). Springer. https://doi.org/10.1007/978-3-319-49130-1_1
LibreCat | DOI
 
[8]
2015 | Conference Paper | LibreCat-ID: 48838
Bossek, J., Bischl, B., Wagner, T., & Rudolph, G. (2015). Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement. Proceedings of the Genetic and Evolutionary Computation Conference, 1319–1326. https://doi.org/10.1145/2739480.2754673
LibreCat | DOI
 
[7]
2015 | Conference Paper | LibreCat-ID: 48887
Meisel, S., Grimme, C., Bossek, J., Wölck, M., Rudolph, G., & Trautmann, H. (2015). Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle. Proceedings of the Genetic and Evolutionary Computation Conference , 425–432. https://doi.org/10.1145/2739480.2754705
LibreCat | DOI
 
[6]
2015 | Conference Paper | LibreCat-ID: 46377
Meisel, S., Grimme, C., Bossek, J., Wölck, M., Rudolph, G., & Trautmann, H. (2015). Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’15), 425–432. https://doi.org/10.1145/2739480.2754705
LibreCat | DOI
 
[5]
2013 | Journal Article | LibreCat-ID: 48889
Mersmann, O., Bischl, B., Trautmann, H., Wagner, M., Bossek, J., & Neumann, F. (2013). A Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling Salesperson Problem. Annals of Mathematics and Artificial Intelligence, 69(2), 151–182. https://doi.org/10.1007/s10472-013-9341-2
LibreCat | DOI
 
[4]
2013 | Journal Article | LibreCat-ID: 46394
Mersmann, O., Bischl, B., Trautmann, H., Wagner, M., Bossek, J., & Neumann, F. (2013). A Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling Salesman Problem. Annals of Mathematics and Artificial Intelligence, 69, 151–182.
LibreCat
 
[3]
2012 | Conference Paper | LibreCat-ID: 48890
Mersmann, O., Bischl, B., Bossek, J., Trautmann, H., Wagner, M., & Neumann, F. (2012). Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness. Revised Selected Papers of the 6th International Conference on Learning and Intelligent Optimization - Volume 7219, 115–129.
LibreCat
 
[2]
2012 | Book Chapter | LibreCat-ID: 48888
Mersmann, O., Bischl, B., Bossek, J., Trautmann, H., Wagner, M., & Neumann, F. (2012). Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness. In Learning and Intelligent Optimization (Vol. 7219, pp. 115–129). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-34413-8_9
LibreCat | DOI
 
[1]
2012 | Conference Paper | LibreCat-ID: 46398
Mersmann, O., Bischl, B., Bossek, J., Trautmann, H., Wagner, M., & Neumann, F. (2012). Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness. In Y. Hamadi & M. Schoenauer (Eds.), Learning and Intelligent Optimization (pp. 115–129). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-34413-8_9
LibreCat | DOI
 
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