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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 

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 

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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Unger, A., Hunstig, M., Meyer, T., Brökelmann, M., &; Sextro, W. (2018). Intelligent Production of Wire Bonds using Multi-Objective Optimization – Insights, Opportunities and Challenges. In In Proceedings of IMAPS 2018 – 51st Symposium on Microelectronics, Pasadena, CA, 2018 (Vol. Vol. 2018, No. 1, pp. 000572-000577.).https://doi.org/10.4071/2380-4505-2018.1.000572 

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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 

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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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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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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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