TY - CONF AU - Krings, Sarah Claudia AU - Yigitbas, Enes AU - Jovanovikj, Ivan AU - Sauer, Stefan AU - Engels, Gregor ID - 16790 SN - 978-1-4503-7984-7/20/06 T2 - Proceedings of the 12th ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS 2020) TI - Development Framework for Context-Aware Augmented Reality Applications ER - TY - JOUR AU - Hagemann, Philipp AU - Kreißl, Steffen AU - Reinke, Paul AU - Wagner, Alexander ID - 47828 JF - Tierstudien TI - "[...] eine Sammelstelle für Tierseelenkunde". Wie eine Wissenschaftszeitschrift um 1900 die Mensch/Tier-Grenze neu zu ordnen versucht VL - 18 ER - TY - CHAP AU - Sloane, Hannah AU - Kremer, H.-Hugo ED - Fuge, Juliane ED - Kremer, H.-Hugo ID - 26021 T2 - Mentoring in Hochschuldidaktik und -praxis - Eine Reflexion wissenschaftlicher Erkenntnisse und praktischer Erfahrungen TI - Peer Mentoring an der Fakultät für Wirtschaftswissenschaften - Einblicke in ein Schulungskonzept für Peer Mentor*innen VL - 2 ER - TY - CHAP AU - Meyer zu Hörste-Bührer, Raphaela ED - Focken, F.-E. ED - van Oorschot, F. ED - Breu, C. ID - 49533 T2 - Schriftbindung evangelischer Theologie, Forum Theologische Literaturzeitung 37 TI - Gotteswort und Menschenwort. ER - TY - CHAP AU - Meyer zu Hörste-Bührer, Raphaela ED - Focken, F.-E. ED - van Oorschot , F. ED - Breu, C. ID - 49535 T2 - Schriftbindung evangelischer Theologie, Forum Theologische Literaturzeitung 37 TI - Autorität der Schrift im Verhältnis zu Rezeptionsprozessen. ER - TY - CHAP AU - Meyer zu Hörste-Bührer, Raphaela ED - Focken, F.-E. ED - van Oorschot, F. ED - Breu, C. ID - 49532 T2 - Schriftbindung evangelischer Theologie, Forum Theologische Literaturzeitung 37 TI - Schrift und Schriftauslegung in relationaler Perspektive. ER - TY - CHAP AU - Meyer zu Hörste-Bührer, Raphaela ED - Focken, F.-E. ED - van Oorschot, F. ED - Breu, C. ID - 49534 T2 - Schriftbindung evangelischer Theologie, Forum Theologische Literaturzeitung 37 TI - Pluralität und Einheit der Schrift. ER - TY - BOOK ED - Focken, F.-E. ED - van Oorschot, F. ED - Breu, C. ED - Bührer, W. ED - Stamer, T. ED - Zeller, K. ED - Ziethe, C. ID - 49522 TI - Schriftbindung evangelischer Theologie. Theorieelemente aus interdisziplinären Gesprächen. ER - TY - JOUR AU - Meyer zu Hörste-Bührer, Raphaela ID - 49531 JF - ides quaerens intellectum. Festschrift für Walter Dietz. TI - Die Gewalt der Ethik? Überlegungen zu Ausübung und Legitimation struktureller Gewalt durch die theologische Ethik. ER - TY - JOUR AU - Meyer zu Hörste-Bührer, Raphaela ID - 49544 JF - Zeitschrift für Evangelische Ethik 64/1 TI - Digitale antike Ethik – Gründung des „Journals of Ethics in Antiquity and Christianity (JEAC)“ ER - TY - CONF AB - 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. AU - Bossek, Jakob AU - Neumann, Frank AU - Peng, Pan AU - Sudholt, Dirk ID - 48847 KW - dynamic optimization KW - evolutionary algorithms KW - running time analysis KW - theory SN - 978-1-4503-7128-5 T2 - Proceedings of the Genetic and Evolutionary Computation Conference TI - More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization ER - TY - CONF AB - 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. AU - Bossek, Jakob AU - Doerr, Carola AU - Kerschke, Pascal AU - Neumann, Aneta AU - Neumann, Frank ID - 48849 KW - Continuous optimization KW - Fully parallel search KW - One-shot optimization KW - Regression KW - Surrogate-assisted optimization SN - 978-3-030-58111-4 T2 - Parallel Problem Solving from Nature (PPSN XVI) TI - Evolving Sampling Strategies for One-Shot Optimization Tasks ER - TY - CONF AB - 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. AU - Bossek, Jakob AU - Casel, Katrin AU - Kerschke, Pascal AU - Neumann, Frank ID - 48851 KW - dynamic optimization KW - evolutionary algorithms KW - running time analysis KW - theory SN - 978-1-4503-7128-5 T2 - Proceedings of the Genetic and Evolutionary Computation Conference TI - The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics ER - TY - CONF AB - 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. AU - Bossek, Jakob AU - Grimme, Christian AU - Trautmann, Heike ID - 48845 KW - decision making KW - dynamic optimization KW - evolutionary algorithms KW - multi-objective optimization KW - vehicle routing SN - 978-1-4503-7128-5 T2 - Proceedings of the Genetic and Evolutionary Computation Conference TI - Dynamic Bi-Objective Routing of Multiple Vehicles ER - TY - CONF AB - 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. AU - Bossek, Jakob AU - Kerschke, Pascal AU - Trautmann, Heike ID - 48844 T2 - 2020 IEEE Congress on Evolutionary Computation (CEC) TI - Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection ER - TY - CONF AB - 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. AU - Bossek, Jakob AU - Doerr, Carola AU - Kerschke, Pascal ID - 48850 KW - continuous black-box optimization KW - design of experiments KW - initial design KW - sequential model-based optimization SN - 978-1-4503-7128-5 T2 - Proceedings of the Genetic and Evolutionary Computation Conference TI - Initial Design Strategies and Their Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB ER - TY - CONF AB - 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. AU - Bossek, Jakob AU - Neumann, Aneta AU - Neumann, Frank ID - 48852 KW - Evolutionary algorithms KW - Node weight dependent TSP KW - Traveling Thief Problem SN - 978-3-030-58111-4 T2 - Parallel Problem Solving from Nature (PPSN XVI) TI - Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions ER - TY - CONF AB - 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. AU - Bossek, Jakob AU - Grimme, Christian AU - Rudolph, Günter AU - Trautmann, Heike ID - 48846 T2 - 2020 IEEE Congress on Evolutionary Computation (CEC) TI - Towards Decision Support in Dynamic Bi-Objective Vehicle Routing ER - TY - CONF AB - Evolving diverse sets of high quality solutions has gained increasing interest in the evolutionary computation literature in recent years. With this paper, we contribute to this area of research by examining evolutionary diversity optimisation approaches for the classical Traveling Salesperson Problem (TSP). We study the impact of using different diversity measures for a given set of tours and the ability of evolutionary algorithms to obtain a diverse set of high quality solutions when adopting these measures. Our studies show that a large variety of diverse high quality tours can be achieved by using our approaches. Furthermore, we compare our approaches in terms of theoretical properties and the final set of tours obtained by the evolutionary diversity optimisation algorithm. AU - Do, Anh Viet AU - Bossek, Jakob AU - Neumann, Aneta AU - Neumann, Frank ID - 48879 KW - diversity maximisation KW - evolutionary algorithms KW - travelling salesperson problem SN - 978-1-4503-7128-5 T2 - Proceedings of the Genetic and Evolutionary Computation Conference TI - Evolving Diverse Sets of Tours for the Travelling Salesperson Problem ER - TY - CONF AB - Evolutionary algorithms (EAs) are general-purpose problem solvers that usually perform an unbiased search. This is reasonable and desirable in a black-box scenario. For combinatorial optimization problems, often more knowledge about the structure of optimal solutions is given, which can be leveraged by means of biased search operators. We consider the Minimum Spanning Tree (MST) problem in a single- and multi-objective version, and introduce a biased mutation, which puts more emphasis on the selection of edges of low rank in terms of low domination number. We present example graphs where the biased mutation can significantly speed up the expected runtime until (Pareto-)optimal solutions are found. On the other hand, we demonstrate that bias can lead to exponential runtime if "heavy" edges are necessarily part of an optimal solution. However, on general graphs in the single-objective setting, we show that a combined mutation operator which decides for unbiased or biased edge selection in each step with equal probability exhibits a polynomial upper bound - as unbiased mutation - in the worst case and benefits from bias if the circumstances are favorable. AU - Roostapour, Vahid AU - Bossek, Jakob AU - Neumann, Frank ID - 48895 KW - biased mutation KW - evolutionary algorithms KW - minimum spanning tree problem KW - runtime analysis SN - 978-1-4503-7128-5 T2 - Proceedings of the 2020 Genetic and Evolutionary Computation Conference TI - Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem ER -