Schön O, Götte R-S, Timmermann J. Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems. In: 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022). Vol 55. ; 2022:19-24. doi:https://doi.org/10.1016/j.ifacol.2022.07.282
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DOI: https://doi.org/10.1016/j.ifacol.2022.07.282

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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DOI: 10.1007/978-3-031-14714-2_14

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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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
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DOI: https://doi.org/10.1016/j.asoc.2019.105901

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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DOI: 10.1145/3377930.3390146

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: /record/48848
DOI: 10.1016/j.asoc.2019.105901

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
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DOI: 10.1007/978-3-030-12598-1_41

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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DOI: 10.1145/3321707.3321818

Unger A, Hunstig M, Meyer T, Brökelmann M, Sextro W. 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. ; 2018. doi:10.4071/2380-4505-2018.1.000572
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DOI: 10.4071/2380-4505-2018.1.000572

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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DOI: 10.1145/3205455.3205548

Stille KSC, Böcker J, Bettentrup R, Kaiser I. Hierarchisches Optimierungskonzept für die Laststeuerung von Haushaltsgeräten. In: ETG-Fachtagung “Von Smart Grids Zu Smart Markets.” VDE; 2015.
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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
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DOI: 10.1145/2739480.2754705