Multimodality in Multi-Objective Optimization — More Boon than Bane?
C. Grimme, P. Kerschke, H. Trautmann, in: K. Deb, E. Goodman, C.C.A. Coello, K. Klamroth, K. Miettinen, S. Mostaghim, P. Reed (Eds.), Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO), Springer, East Lansing, MI, USA, 2019, pp. 126–138.

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

Sliding to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal Multi-Objective Optimization
C. Grimme, P. Kerschke, M.T.M. Emmerich, M. Preuss, A.H. Deutz, H. Trautmann, in: AIP Conference Proceedings, AIP Publishing, Leiden, The Netherlands, 2019, pp. 020052-1-020052–4.

Prinzipien und Strategien der Textgliederung in den Zeitungen des 18. Jahrhunderts – Eine computerbasierte Korpusanalyse
M. Wille, in: F. Simmler, G. Baeva (Eds.), Textgliederungsprinzipien. Ihre Kennzeichnungsformen Und Funktionen Vom 8. Bis 18. Jahrhundert. Akten Zum Internationalen Kongress Vom 22. Bis 24. Juni 2017 an Der Universität St. Petersburg, Weidler, Berlin, 2019, pp. 507–531.

Textgliederungsprinzipien und Beziehungsgestaltung im Stammbuch von 1559–1878
B.-M. Schuster, in: F. Simmler, G. Baeva (Eds.), Textgliederungsprinzipien. Ihre Kennzeichnungsformen und Funktionen in Texten vom 8. bis 18. Jahrhundert. Akten zum Internationalen Kongress an der Staatlichen Universität St. Petersburg vom 22. bis 24. Juni 2017, WEIDLER, Berlin , 2019, pp. 293–312.

Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-package flacco
P. Kerschke, H. Trautmann, in: N. Bauer, K. Ickstadt, K. Lübke, G. Szepannek, H. Trautmann, M. Vichi (Eds.), Applications in Statistical Computing, Springer, 2019, pp. 93–123.

Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning
P. Kerschke, H. Trautmann, Evolutionary Computation (ECJ) 27 (2019) 99–127.

Search Dynamics on Multimodal Multi-Objective Problems
P. Kerschke, H. Wang, M. Preuss, C. Grimme, A. Deutz, H. Trautmann, M. Emmerich, Evolutionary Computation (ECJ) 27 (2019) 577–609.

Unterstützung des Lernens für kritische Situationen: Potenzial von Augmented Reality für die Instandsetzung auf See
I. Gräßler, J. Pottebaum, P. Taplick, D. Roesmann, D. Preuß, in: U. Lukas, K. Bauer, T. Dolereit (Eds.), Go-3D 2019 “Mit 3D Richtung Maritim 4.0” - Tagungsband zur Konferenz Go-3D 2019, Fraunhofer Verlag, 2019, pp. 45–57.

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.

Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators
J. Bossek, P. Kerschke, A. Neumann, M. Wagner, F. Neumann, H. Trautmann, in: Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, Association for Computing Machinery, New York, NY, USA, 2019, pp. 58–71.

Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring
J. Bossek, F. Neumann, P. Peng, D. Sudholt, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2019, pp. 1443–1451.

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.

Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-criteria Shortest Path Problems
J. Bossek, C. Grimme, in: R. Battiti, M. Brunato, I. Kotsireas, P.M. Pardalos (Eds.), Learning and Intelligent Optimization, Springer International Publishing, Cham, 2019, pp. 184–198.

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.

Process Parameter Optimization to Improve the Mechanical Properties of Arburg Plastic Freeformed Components
E. Moritzer, A. Hirsch, F. Hecker, in: 30th Annual International Solid Freeform Fabrication Symposium, 2019, pp. 705–714.

Ergodic Rate for Fading Interference Channels with Proper and Improper Gaussian Signaling
M. Soleymani, I. Santamaria, C. Lameiro, P.J. Schreier, Entropy 21 (2019).

Conceptualizing smart service systems
D. Beverungen, O. Müller, M. Matzner, J. Mendling, J. vom Brocke, Electronic Markets 29 (2019) 7–18.

A tuned hybrid intelligent fruit fly optimization algorithm for fuzzy rule generation and classification
S.M. Mousavi, M. Tavana, N. Alikar, M. Zandieh, Neural Computing and Applications 31 (2019) 873–885.

An improved particle swarm optimization model for solving homogeneous discounted series-parallel redundancy allocation problems
S.M. Mousavi, N. Alikar, M. Tavana, D. Di Caprio, Journal of Intelligent Manufacturing 30 (2019) 1175–1194.