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85 Publications


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
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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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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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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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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
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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
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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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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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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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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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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
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2017 | Journal Article | LibreCat-ID: 48865
Bossek, J. (2017). Smoof: Single- and Multi-Objective Optimization Test Functions. The R Journal, 9(1), 103–113.
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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
 

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
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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
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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
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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
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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
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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
 

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
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