@inproceedings{24247, author = {{Moritzer, Elmar and Hillemeyer, Johannes}}, booktitle = {{73rd Annual Assembly of International Institute of Welding (IIW) and International Conference}}, title = {{{Material Specific Predicting of the Optimal Joining Parameters for the Screw Blind Rivet Joining Process}}}, year = {{2020}}, } @inbook{49378, author = {{Kammeyer, Katharina}}, booktitle = {{Inklusive Religionspädagogik der Vielfalt. Konzeptionelle Grundlagen und didaktische Konkretionen}}, editor = {{Knauth, Thorsten and Möller, Rainer and Pithan, Annabelle}}, publisher = {{Waxmann}}, title = {{{Differenz}}}, volume = {{42}}, year = {{2020}}, } @inproceedings{16790, author = {{Krings, Sarah Claudia and Yigitbas, Enes and Jovanovikj, Ivan and Sauer, Stefan and Engels, Gregor}}, booktitle = {{Proceedings of the 12th ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS 2020)}}, isbn = {{978-1-4503-7984-7/20/06}}, title = {{{Development Framework for Context-Aware Augmented Reality Applications}}}, doi = {{10.1145/3393672.3398640}}, year = {{2020}}, } @article{47828, author = {{Hagemann, Philipp and Kreißl, Steffen and Reinke, Paul and Wagner, Alexander}}, journal = {{Tierstudien}}, pages = {{137--147}}, publisher = {{Jessica Ullrich, Stefan Rieger}}, title = {{{"[...] eine Sammelstelle für Tierseelenkunde". Wie eine Wissenschaftszeitschrift um 1900 die Mensch/Tier-Grenze neu zu ordnen versucht}}}, volume = {{18}}, year = {{2020}}, } @inbook{26021, author = {{Sloane, Hannah and Kremer, H.-Hugo}}, booktitle = {{Mentoring in Hochschuldidaktik und -praxis - Eine Reflexion wissenschaftlicher Erkenntnisse und praktischer Erfahrungen}}, editor = {{Fuge, Juliane and Kremer, H.-Hugo}}, pages = {{41--58}}, publisher = {{Eusl-Verlagsgesellschaft mbH}}, title = {{{Peer Mentoring an der Fakultät für Wirtschaftswissenschaften - Einblicke in ein Schulungskonzept für Peer Mentor*innen}}}, volume = {{2}}, year = {{2020}}, } @inbook{49533, author = {{Meyer zu Hörste-Bührer, Raphaela}}, booktitle = {{Schriftbindung evangelischer Theologie, Forum Theologische Literaturzeitung 37}}, editor = {{Focken, F.-E. and van Oorschot, F. and Breu, C.}}, pages = {{267–302}}, title = {{{Gotteswort und Menschenwort.}}}, year = {{2020}}, } @inbook{49535, author = {{Meyer zu Hörste-Bührer, Raphaela}}, booktitle = {{Schriftbindung evangelischer Theologie, Forum Theologische Literaturzeitung 37}}, editor = {{Focken, F.-E. and van Oorschot , F. and Breu, C.}}, pages = {{303–341}}, title = {{{Autorität der Schrift im Verhältnis zu Rezeptionsprozessen.}}}, year = {{2020}}, } @inbook{49532, author = {{Meyer zu Hörste-Bührer, Raphaela}}, booktitle = {{Schriftbindung evangelischer Theologie, Forum Theologische Literaturzeitung 37}}, editor = {{Focken, F.-E. and van Oorschot, F. and Breu, C.}}, pages = {{342–391}}, title = {{{Schrift und Schriftauslegung in relationaler Perspektive.}}}, year = {{2020}}, } @inbook{49534, author = {{Meyer zu Hörste-Bührer, Raphaela}}, booktitle = {{Schriftbindung evangelischer Theologie, Forum Theologische Literaturzeitung 37}}, editor = {{Focken, F.-E. and van Oorschot, F. and Breu, C.}}, location = {{Leipzig}}, pages = {{95–122}}, title = {{{Pluralität und Einheit der Schrift.}}}, year = {{2020}}, } @book{49522, editor = {{Focken, F.-E. and van Oorschot, F. and Breu, C. and Bührer, W. and Stamer, T. and Zeller, K. and Ziethe, C.}}, title = {{{Schriftbindung evangelischer Theologie. Theorieelemente aus interdisziplinären Gesprächen.}}}, year = {{2020}}, } @article{49531, author = {{Meyer zu Hörste-Bührer, Raphaela}}, journal = {{ides quaerens intellectum. Festschrift für Walter Dietz.}}, pages = {{141 – 156}}, title = {{{Die Gewalt der Ethik? Überlegungen zu Ausübung und Legitimation struktureller Gewalt durch die theologische Ethik.}}}, year = {{2020}}, } @article{49544, author = {{Meyer zu Hörste-Bührer, Raphaela}}, journal = {{Zeitschrift für Evangelische Ethik 64/1 }}, pages = {{60--65}}, title = {{{Digitale antike Ethik – Gründung des „Journals of Ethics in Antiquity and Christianity (JEAC)“}}}, year = {{2020}}, } @inproceedings{48847, abstract = {{Dynamic optimization problems have gained significant attention in evolutionary computation as evolutionary algorithms (EAs) can easily adapt to changing environments. We show that EAs can solve the graph coloring problem for bipartite graphs more efficiently by using dynamic optimization. In our approach the graph instance is given incrementally such that the EA can reoptimize its coloring when a new edge introduces a conflict. We show that, when edges are inserted in a way that preserves graph connectivity, Randomized Local Search (RLS) efficiently finds a proper 2-coloring for all bipartite graphs. This includes graphs for which RLS and other EAs need exponential expected time in a static optimization scenario. We investigate different ways of building up the graph by popular graph traversals such as breadth-first-search and depth-first-search and analyse the resulting runtime behavior. We further show that offspring populations (e. g. a (1 + {$\lambda$}) RLS) lead to an exponential speedup in {$\lambda$}. Finally, an island model using 3 islands succeeds in an optimal time of {$\Theta$}(m) on every m-edge bipartite graph, outperforming offspring populations. This is the first example where an island model guarantees a speedup that is not bounded in the number of islands.}}, author = {{Bossek, Jakob and Neumann, Frank and Peng, Pan and Sudholt, Dirk}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-7128-5}}, keywords = {{dynamic optimization, evolutionary algorithms, running time analysis, theory}}, pages = {{1277–1285}}, publisher = {{Association for Computing Machinery}}, title = {{{More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization}}}, doi = {{10.1145/3377930.3390174}}, year = {{2020}}, } @inproceedings{48849, abstract = {{One-shot optimization tasks require to determine the set of solution candidates prior to their evaluation, i.e., without possibility for adaptive sampling. We consider two variants, classic one-shot optimization (where our aim is to find at least one solution of high quality) and one-shot regression (where the goal is to fit a model that resembles the true problem as well as possible). For both tasks it seems intuitive that well-distributed samples should perform better than uniform or grid-based samples, since they show a better coverage of the decision space. In practice, quasi-random designs such as Latin Hypercube Samples and low-discrepancy point sets are indeed very commonly used designs for one-shot optimization tasks. We study in this work how well low star discrepancy correlates with performance in one-shot optimization. Our results confirm an advantage of low-discrepancy designs, but also indicate the correlation between discrepancy values and overall performance is rather weak. We then demonstrate that commonly used designs may be far from optimal. More precisely, we evolve 24 very specific designs that each achieve good performance on one of our benchmark problems. Interestingly, we find that these specifically designed samples yield surprisingly good performance across the whole benchmark set. Our results therefore give strong indication that significant performance gains over state-of-the-art one-shot sampling techniques are possible, and that evolutionary algorithms can be an efficient means to evolve these.}}, author = {{Bossek, Jakob and Doerr, Carola and Kerschke, Pascal and Neumann, Aneta and Neumann, Frank}}, booktitle = {{Parallel Problem Solving from Nature (PPSN XVI)}}, isbn = {{978-3-030-58111-4}}, keywords = {{Continuous optimization, Fully parallel search, One-shot optimization, Regression, Surrogate-assisted optimization}}, pages = {{111–124}}, publisher = {{Springer-Verlag}}, title = {{{Evolving Sampling Strategies for One-Shot Optimization Tasks}}}, doi = {{10.1007/978-3-030-58112-1_8}}, year = {{2020}}, } @inproceedings{48851, abstract = {{Several important optimization problems in the area of vehicle routing can be seen as variants of the classical Traveling Salesperson Problem (TSP). In the area of evolutionary computation, the Traveling Thief Problem (TTP) has gained increasing interest over the last 5 years. In this paper, we investigate the effect of weights on such problems, in the sense that the cost of traveling increases with respect to the weights of nodes already visited during a tour. This provides abstractions of important TSP variants such as the Traveling Thief Problem and time dependent TSP variants, and allows to study precisely the increase in difficulty caused by weight dependence. We provide a 3.59-approximation for this weight dependent version of TSP with metric distances and bounded positive weights. Furthermore, we conduct experimental investigations for simple randomized local search with classical mutation operators and two variants of the state-of-the-art evolutionary algorithm EAX adapted to the weighted TSP. Our results show the impact of the node weights on the position of the nodes in the resulting tour.}}, author = {{Bossek, Jakob and Casel, Katrin and Kerschke, Pascal and Neumann, Frank}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-7128-5}}, keywords = {{dynamic optimization, evolutionary algorithms, running time analysis, theory}}, pages = {{1286–1294}}, publisher = {{Association for Computing Machinery}}, title = {{{The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics}}}, doi = {{10.1145/3377930.3390243}}, year = {{2020}}, } @inproceedings{48845, abstract = {{In practice, e.g. in delivery and service scenarios, Vehicle-Routing-Problems (VRPs) often imply repeated decision making on dynamic customer requests. As in classical VRPs, tours have to be planned short while the number of serviced customers has to be maximized at the same time resulting in a multi-objective problem. Beyond that, however, dynamic requests lead to the need for re-planning of not yet realized tour parts, while already realized tour parts are irreversible. In this paper we study this type of bi-objective dynamic VRP including sequential decision making and concurrent realization of decisions. We adopt a recently proposed Dynamic Evolutionary Multi-Objective Algorithm (DEMOA) for a related VRP problem and extend it to the more realistic (here considered) scenario of multiple vehicles. We empirically show that our DEMOA is competitive with a multi-vehicle offline and clairvoyant variant of the proposed DEMOA as well as with the dynamic single-vehicle approach proposed earlier.}}, author = {{Bossek, Jakob and Grimme, Christian and Trautmann, Heike}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-7128-5}}, keywords = {{decision making, dynamic optimization, evolutionary algorithms, multi-objective optimization, vehicle routing}}, pages = {{166–174}}, publisher = {{Association for Computing Machinery}}, title = {{{Dynamic Bi-Objective Routing of Multiple Vehicles}}}, doi = {{10.1145/3377930.3390146}}, year = {{2020}}, } @inproceedings{48844, abstract = {{The Traveling-Salesperson-Problem (TSP) is arguably one of the best-known NP-hard combinatorial optimization problems. The two sophisticated heuristic solvers LKH and EAX and respective (restart) variants manage to calculate close-to optimal or even optimal solutions, also for large instances with several thousand nodes in reasonable time. In this work we extend existing benchmarking studies by addressing anytime behaviour of inexact TSP solvers based on empirical runtime distributions leading to an increased understanding of solver behaviour and the respective relation to problem hardness. It turns out that performance ranking of solvers is highly dependent on the focused approximation quality. Insights on intersection points of performances offer huge potential for the construction of hybridized solvers depending on instance features. Moreover, instance features tailored to anytime performance and corresponding performance indicators will highly improve automated algorithm selection models by including comprehensive information on solver quality.}}, author = {{Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}}, booktitle = {{2020 IEEE Congress on Evolutionary Computation (CEC)}}, pages = {{1–8}}, publisher = {{IEEE Press}}, title = {{{Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection}}}, doi = {{10.1109/CEC48606.2020.9185613}}, year = {{2020}}, } @inproceedings{48850, abstract = {{Sequential model-based optimization (SMBO) approaches are algorithms for solving problems that require computationally or otherwise expensive function evaluations. The key design principle of SMBO is a substitution of the true objective function by a surrogate, which is used to propose the point(s) to be evaluated next. SMBO algorithms are intrinsically modular, leaving the user with many important design choices. Significant research efforts go into understanding which settings perform best for which type of problems. Most works, however, focus on the choice of the model, the acquisition function, and the strategy used to optimize the latter. The choice of the initial sampling strategy, however, receives much less attention. Not surprisingly, quite diverging recommendations can be found in the literature. We analyze in this work how the size and the distribution of the initial sample influences the overall quality of the efficient global optimization (EGO) algorithm, a well-known SMBO approach. While, overall, small initial budgets using Halton sampling seem preferable, we also observe that the performance landscape is rather unstructured. We furthermore identify several situations in which EGO performs unfavorably against random sampling. Both observations indicate that an adaptive SMBO design could be beneficial, making SMBO an interesting test-bed for automated algorithm design.}}, author = {{Bossek, Jakob and Doerr, Carola and Kerschke, Pascal}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-7128-5}}, keywords = {{continuous black-box optimization, design of experiments, initial design, sequential model-based optimization}}, pages = {{778–786}}, publisher = {{Association for Computing Machinery}}, title = {{{Initial Design Strategies and Their Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB}}}, doi = {{10.1145/3377930.3390155}}, year = {{2020}}, } @inproceedings{48852, abstract = {{The Traveling Salesperson Problem (TSP) is one of the best-known combinatorial optimisation problems. However, many real-world problems are composed of several interacting components. The Traveling Thief Problem (TTP) addresses such interactions by combining two combinatorial optimisation problems, namely the TSP and the Knapsack Problem (KP). Recently, a new problem called the node weight dependent Traveling Salesperson Problem (W-TSP) has been introduced where nodes have weights that influence the cost of the tour. In this paper, we compare W-TSP and TTP. We investigate the structure of the optimised tours for W-TSP and TTP and the impact of using each others fitness function. Our experimental results suggest (1) that the W-TSP often can be solved better using the TTP fitness function and (2) final W-TSP and TTP solutions show different distributions when compared with optimal TSP or weighted greedy solutions.}}, author = {{Bossek, Jakob and Neumann, Aneta and Neumann, Frank}}, booktitle = {{Parallel Problem Solving from Nature (PPSN XVI)}}, isbn = {{978-3-030-58111-4}}, keywords = {{Evolutionary algorithms, Node weight dependent TSP, Traveling Thief Problem}}, pages = {{346–359}}, publisher = {{Springer-Verlag}}, title = {{{Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions}}}, doi = {{10.1007/978-3-030-58112-1_24}}, year = {{2020}}, } @inproceedings{48846, abstract = {{We consider a dynamic bi-objective vehicle routing problem, where a subset of customers ask for service over time. Therein, the distance traveled by a single vehicle and the number of unserved dynamic requests is minimized by a dynamic evolutionary multi-objective algorithm (DEMOA), which operates on discrete time windows (eras). A decision is made at each era by a decision-maker, thus any decision depends on irreversible decisions made in foregoing eras. To understand effects of sequences of decision-making and interactions/dependencies between decisions made, we conduct a series of experiments. More precisely, we fix a set of decision-maker preferences D and the number of eras n{$<$}inf{$>$}t{$<$}/inf{$>$} and analyze all $|D|\^{n_t}$ combinations of decision-maker options. We find that for random uniform instances (a) the final selected solutions mainly depend on the final decision and not on the decision history, (b) solutions are quite robust with respect to the number of unvisited dynamic customers, and (c) solutions of the dynamic approach can even dominate solutions obtained by a clairvoyant EMOA. In contrast, for instances with clustered customers, we observe a strong dependency on decision-making history as well as more variance in solution diversity.}}, author = {{Bossek, Jakob and Grimme, Christian and Rudolph, Günter and Trautmann, Heike}}, booktitle = {{2020 IEEE Congress on Evolutionary Computation (CEC)}}, pages = {{1–8}}, publisher = {{IEEE Press}}, title = {{{Towards Decision Support in Dynamic Bi-Objective Vehicle Routing}}}, doi = {{10.1109/CEC48606.2020.9185778}}, year = {{2020}}, }