An Evolutionary Approach for the Computation of -Locally Optimal Solutions for Multi-Objective Multimodal Optimization
C. Hernández, A.E. Rodriguez-Fernandez, L. Schäpermeier, O. Cuate, H. Trautmann, O. Schütze, IEEE Transactions on Evolutionary Computation (2025) 1–1.

Finding ?5-Locally Optimal Solutions for Multi-Objective Multimodal Optimization
A.E. Rodriguez-Fernandez, L. Schäpermeier, C. Hernández, P. Kerschke, H. Trautmann, O. Schütze, IEEE Transactions on Evolutionary Computation (2024) 1–1.

BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems
J. Heins, J. Rook, L. Schäpermeier, P. Kerschke, J. Bossek, H. Trautmann, in: G. Rudolph, A.V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, T. Tusar (Eds.), Parallel Problem Solving from Nature (PPSN XVII), Springer International Publishing, Cham, 2022, pp. 192–206.

On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems
J. Rook, H. Trautmann, J. Bossek, C. Grimme, in: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, New York, NY, USA, 2022, pp. 356–359.

Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems
O. Schön, R.-S. Götte, J. Timmermann, in: 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022), 2022, pp. 19–24.

Dynamic Bi-Objective Routing of Multiple Vehicles
J. Bossek, C. Grimme, H. Trautmann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2020, pp. 166–174.

A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms
J. Bossek, P. Kerschke, H. Trautmann, Applied Soft Computing 88 (2020).

A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms
J. Bossek, P. Kerschke, H. Trautmann, Applied Soft Computing 88 (2020) 105901.

Bi-Objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm
J. Bossek, C. Grimme, S. Meisel, G. Rudolph, H. Trautmann, in: K. Deb, E. Goodman, C.A. Coello Coello, K. Klamroth, K. Miettinen, S. Mostaghim, P. Reed (Eds.), Evolutionary Multi-Criterion Optimization (EMO), Springer International Publishing, Cham, 2019, pp. 516–528.

On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem
J. Bossek, C. Grimme, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2019, pp. 516–523.

Intelligent Production of Wire Bonds using Multi-Objective Optimization – Insights, Opportunities and Challenges
A. Unger, M. Hunstig, T. Meyer, M. Brökelmann, W. Sextro, in: In Proceedings of IMAPS 2018 – 51st Symposium on Microelectronics, Pasadena, CA, 2018, 2018.

Local Search Effects in Bi-Objective Orienteering
J. Bossek, C. Grimme, S. Meisel, G. Rudolph, H. Trautmann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2018, pp. 585–592.

Hierarchisches Optimierungskonzept für die Laststeuerung von Haushaltsgeräten
K.S.C. Stille, J. Böcker, R. Bettentrup, I. Kaiser, in: ETG-Fachtagung “Von Smart Grids Zu Smart Markets,” VDE, Kassel, 2015.

Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle
S. Meisel, C. Grimme, J. Bossek, M. Wölck, G. Rudolph, H. Trautmann, in: Proceedings of the Genetic and Evolutionary Computation Conference , Association for Computing Machinery, New York, NY, USA, 2015, pp. 425–432.