Jakob Bossek

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

85 Publications

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[85]
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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[84]
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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[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
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
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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
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
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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
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[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: 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. 356–359–356–359). Association for Computing Machinery. https://doi.org/10.1145/3520304.3528998
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[68]
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
 
[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
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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
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[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
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[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
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[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
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
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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
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[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
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[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
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
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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.
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
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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
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[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
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[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
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[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
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[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
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[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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[85]
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
 
[84]
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
 
[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: 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. 356–359–356–359). Association for Computing Machinery. https://doi.org/10.1145/3520304.3528998
LibreCat | DOI
 
[68]
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
 
[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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