Symmetry Preservation in Swarms of Oblivious Robots with Limited  Visibility
R. Gerlach, S. von der Gracht, C. Hahn, J. Harbig, P. Kling, in: S. Bonomi, L. Galletta,  Etienne Rivière,  Valerio Schiavoni (Eds.), 28th International Conference on Principles of Distributed Systems (OPODIS 2024), Schloss Dagstuhl -- Leibniz-Zentrum für Informatik, 2025.

Exploratory Landscape Analysis for Mixed-Variable Problems
R.P. Prager, H. Trautmann, IEEE Transactions on Evolutionary Computation (2024) 1–1.

Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time
D. Leffrang, K. Bösch, O. Müller, in: Hawaii International Conference on System Sciences, 2023.

AI Washing: The Framing Effect of Labels on Algorithmic Advice Utilization
D. Leffrang, in: International Conference on Information Systems, 2023.

The Broken Leg of Algorithm Appreciation: An Experimental Study on the Effect of Unobserved Variables on Advice Utilization
D. Leffrang, in: Wirtschaftsinformatik Conference, 2023.

A study on the effects of normalized TSP features for automated algorithm selection
J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, P. Kerschke, Theoretical Computer Science 940 (2023) 123–145.

Normative Diversität? Nahe Zukunft bei Sibylle Berg und Marc-Uwe Kling
S. Schulte Eickholt, in: L. Cosan, O.K. Bazarkaya, H. Tekin (Eds.), Germanistik im Wandel 1. Neue Einsichten und Perspektiven in der Literaturwissenschaft, Logos, Berlin, 2023, pp. 65–75.

On the Potential of Normalized TSP Features for Automated Algorithm Selection
J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, P. Kerschke, in: Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, Association for Computing Machinery, New York, NY, USA, 2021, pp. 1–15.

Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem
M. Seiler, J. Pohl, J. Bossek, P. Kerschke, H. Trautmann, in: Parallel Problem Solving from Nature (PPSN XVI), Springer-Verlag, Berlin, Heidelberg, 2020, pp. 48–64.

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.

Using High Performance Computing for Unrelated Parallel Machine Scheduling with Sequence-Dependent Setup Times: Development and Computational Evaluation of a Parallel Branch-and-Price Algorithm
G. Rauchecker, G. Schryen, Computers &; Operations Research (2019) 338–357.

Multi-Objective Performance Measurement: Alternatives to PA and Expected Running Time
J. Bossek, H. Trautmann, in: R. Battiti, M. Brunato, I. Kotsireas, P.M. Pardalos (Eds.), Learning and Intelligent Optimization, Springer International Publishing, Cham, 2019, pp. 215–219.

Breaking the $\'74ilde\'4fmega(\'73qrtn)$ Barrier: Fast Consensus under a Late Adversary
P. Robinson, C. Scheideler, A. Setzer, in: Proceedings of the 30th ACM Symposium on Parallelism in Algorithms and Architectures (SPAA), n.d.

Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers
P. Kerschke, J. Bossek, H. Trautmann, in: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, New York, NY, USA, 2018, pp. 1737–1744.

Leveraging TSP Solver Complementarity through Machine Learning
P. Kerschke, L. Kotthoff, J. Bossek, H.H. Hoos, H. Trautmann, Evolutionary Computation 26 (2018) 597–620.

A Massively Parallel Algorithm for the Approximate Calculation of Inverse p-th Roots of Large Sparse Matrices
M. Lass, S. Mohr, H. Wiebeler, T. Kühne, C. Plessl, in: Proc. Platform for Advanced Scientific Computing (PASC) Conference, ACM, New York, NY, USA, 2018.

Filtering Undesirable Flows in Networks
G. Polevoy, S. Trajanovski, P. Grosso, C. de Laat, in: Combinatorial Optimization and Applications: 11th International Conference, COCOA 2017, Shanghai, China, December 16-18, 2017, Proceedings, Part I, Springer International Publishing, Cham, 2017, pp. 3–17.

Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers
J. Bossek, H. Trautmann, in: P. Festa, M. Sellmann, J. Vanschoren (Eds.), Learning and Intelligent Optimization, Springer International Publishing, Cham, 2016, pp. 48–59.

PEM fuel cell prognostics using particle filter with model parameter adaptation
J.K. Kimotho, T. Meyer, W. Sextro, in: Prognostics and Health Management (PHM), 2014 IEEE Conference On, 2014, pp. 1–6.