Rüsing, M., Sanna, S., Neufeld, S., Berth, G., Schmidt, W. G., Zrenner, A., Yu, H., Wang, Y., &; Zhang, H. (2016). Vibrational properties of LiNb1xTaxO3 mixed crystals. Physical Review B.https://doi.org/10.1103/physrevb.93.184305 
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DOI: 10.1103/physrevb.93.184305

Euler, D., Sloane, P. F. E., Fäckeler, S., Jenert, T. J., Losch, S., Meier, C., Meier, K., Rüschen, E., &; Schröder, H. (2016). Kommunales Bildungsmanagement - Kernkomponenten und Gelingensbedingungen. EUSL.
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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: /record/46364
DOI: 10.1007/978-3-319-50349-3_3

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 
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DOI: 10.1007/978-3-319-31153-1_4

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: /record/46365
DOI: 10.1007/978-3-319-50349-3_4

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: /record/46366
DOI: 10.1007/978-3-319-49130-1_1

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 
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DOI: 10.1007/978-3-319-45823-6_90

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 
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DOI: 10.1145/2908812.2908845

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: /record/46371
DOI: 10.1007/s10589-015-9815-8

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: /record/46372
DOI: 10.1007/s10732-016-9310-0

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 
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DOI: 10.1109/CEC.2016.7748359

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 
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DOI: 10.4230/DagRep.5.5.57

Habernal, I., &; Gurevych, I. (2016). What makes a convincing argument? Empirical analysis and detecting            attributes of convincingness in Web argumentation. Proceedings of the 2016 Conference on Empirical Methods in Natural          Language Processing.https://doi.org/10.18653/v1/d16-1129 
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DOI: 10.18653/v1/d16-1129

Wille, M. (2016). Von der Kanzleisprache zur Pressesprache im “Hamburgischen Correspondenten” und seinen Vorgängern (1712 - 1801). In R. Hünecke &; S. Aehnelt (Eds.), Kanzlei und Sprachkultur. Beiträge der 8. Tagung des Arbeitskreises Historische Kanzleisprachenforschung, Dresden 3. bis 5. September 2015: Vol. Band 9 (pp. 277–295). Edition Praesens.
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Schmalenstroeer, J., &; Haeb-Umbach, R. (2016). Investigations into Bluetooth Low Energy Localization Precision Limits. 24th European Signal Processing Conference (EUSIPCO 2016).
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Oll, J., Hahn, R., Reimsbach, D., &; Kotzian, P. (2016). Tackling Complexity in Business and Society Research: The Methodological and Thematic Potential of Factorial Surveys. Business &;amp; Society, 57(1), 26–59.https://doi.org/10.1177/0007650316645337 
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DOI: 10.1177/0007650316645337

Ziegele, F., Roessler, I., &; Mordhorst, L. (2016). Hochschultyp im Wandel? Zur zukünftigen Rolle der Fachhochschule im deutschen Hochschulsystem. In J. Cai &; H. Lackner (Eds.), Jahrbuch Angewandte Hochschulbildung 2016 (pp. 159–174). Springer.
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Krause, D., &; Effenberg, A. (2016). Zur Einrichtung einer Ethikkommission in der Deutschen Vereinigung für Sportwissenschaft. Ze-Phir, 23(1), 12–13.
LibreCat: /record/48718

Diederich, J. (2016). Willi wills wissen: Wie wird man Astronaut? Wie sieht die Welt im Weltraum aus? Geschichte erleben und verstehen: “Als Oma und Opa Kinder waren,” 295.296.
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Becher, A., &; Diederich, J. (2016). “Erzähl mir von früher!” – Kinder befragen Zeitzeugen. Geschichte erleben und verstehen: “Als Oma und Opa Kinder waren,” 295.296.
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