Jakob Bossek

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

85 Publications

Mark all

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