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213 Publications
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
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
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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
LibreCat
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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
LibreCat
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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.
LibreCat
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: 46364
Blot, A., Hoos, H., Jourdan, L., Marmion, M., & Trautmann, H. (2016). MO-ParamILS: A Multi-objective Automatic Algorithm Configuration Framework. In V. et al. Joaquin (Ed.), LION 2016: Learning and Intelligent Optimization (Vol. 10079, pp. 32–47). Springer International Publishing. https://doi.org/10.1007/978-3-319-50349-3_3
LibreCat
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2016 | Book Chapter | LibreCat-ID: 46363
Rudolph, G., Schütze, O., & Trautmann, H. (2016). On the Closest Averaged Hausdorff Archive for a Circularly Convex Pareto Front. In G. Squillero & P. Burelli (Eds.), Applications of Evolutionary Computation: 19$^th$ European Conference, EvoApplications 2016, Porto, Portugal, March 30 — April 1, 2016, Proceedings, Part II (pp. 42–55). Springer International Publishing. https://doi.org/10.1007/978-3-319-31153-1_4
LibreCat
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2016 | Conference Paper | LibreCat-ID: 46369
Kerschke, P., Wang, H., Preuss, M., Grimme, C., Deutz, A., Trautmann, H., & Emmerich, M. (2016). Towards Analyzing Multimodality of Multiobjective Landscapes. Proceedings of the 14$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XIV), 962–972. https://doi.org/10.1007/978-3-319-45823-6_90
LibreCat
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2016 | Conference Paper | LibreCat-ID: 46367
Kerschke, P., Preuss, M., Wessing, S., & Trautmann, H. (2016). Low-Budget Exploratory Landscape Analysis on Multiple Peaks Models. Proceedings of the 18$^th$ Annual Conference on Genetic and Evolutionary Computation, 229–236. https://doi.org/10.1145/2908812.2908845
LibreCat
| DOI
2016 | Journal Article | LibreCat-ID: 46371
Rudolph, G., Schütze, O., Grimme, C., Domínguez-Medina, C., & Trautmann, H. (2016). Optimal averaged Hausdorff archives for bi-objective problems: theoretical and numerical results. Computational Optimization and Applications (Comput. Optim. Appl.), 64(2), 589–618. https://doi.org/10.1007/s10589-015-9815-8
LibreCat
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2016 | Journal Article | LibreCat-ID: 46372
Schütze, O., Sosa, H. V., Trautmann, H., & Rudolph, G. (2016). The Hypervolume based Directed Search Method for Multi-Objective Optimization Problems. Journal of Heuristics, 22(3), 273–300. https://doi.org/10.1007/s10732-016-9310-0
LibreCat
| DOI
2016 | Conference Paper | LibreCat-ID: 46368
Kerschke, P., & Trautmann, H. (2016). The R-Package FLACCO for Exploratory Landscape Analysis with Applications to Multi-Objective Optimization Problems. Proceedings of the IEEE Congress on Evolutionary Computation (CEC). https://doi.org/10.1109/CEC.2016.7748359
LibreCat
| DOI
2016 | Journal Article | LibreCat-ID: 46370
Neumann, F., & Trautmann, H. (2016). Working Group Report: Bridging the Gap Between Experiments and Theory Using Feature-Based Run-Time Analysis; Theory of Evolutionary Algorithms (Dagstuhl Seminar 15211). Dagstuhl Reports, 5(5), 78–79. https://doi.org/10.4230/DagRep.5.5.57
LibreCat
| DOI
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
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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
LibreCat
| DOI
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
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
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2015 | Conference Paper | LibreCat-ID: 46373
Chinnov, A., Kerschke, P., Meske, C., Stieglitz, S., & Trautmann, H. (2015). An Overview of Topic Discovery in Twitter Communication through Social Media Analytics. Proceedings of the 20$^th$ Americas Conference on Information Systems (AMCIS ’15), 1–10.
LibreCat
2015 | Conference Paper | LibreCat-ID: 46375
Kerschke, P., Preuss, M., Wessing, S., & Trautmann, H. (2015). Detecting Funnel Structures by Means of Exploratory Landscape Analysis. In S. Silva (Ed.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’15) (pp. 265–272). ACM. https://doi.org/10.1145/2739480.2754642
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