@inbook{49487, author = {{Malancu, Natalia and Florea, Alexandra}}, booktitle = {{Handbook of Citizenship and Migration}}, editor = {{Giugni, Marco and Grasso, Maria}}, title = {{{Chapter 5: Quantitative methodological approaches to citizenship and migration}}}, doi = {{https://doi.org/10.4337/9781789903133.00011}}, year = {{2021}}, } @techreport{47098, author = {{Alt, Marius and Gallier, Carlo and Sturm, Bodo and Kesternich, Martin}}, publisher = {{ZEW Policy Brief 21-09}}, title = {{{Ausblick auf die COP26 in Glasgow, Eine schrittweise Erhöhung der Klimaschutzbeiträge reicht nicht – ein Klimaklub sollte mitgedacht werden}}}, year = {{2021}}, } @techreport{47100, author = {{Frick, Marc and Conzelmann, Annabell and von Graevenitz, Kathrine and Kesternich, Martin and Wagner, Ulrich and Rausch, Sebastian}}, title = {{{Transparente Klimabilanzen - Information für klimafreundliches Handeln}}}, year = {{2021}}, } @article{49530, author = {{Meyer zu Hörste-Bührer, Raphaela}}, journal = {{Römerbrief und Tageszeitung! Politik in der Theologie Karl Barths.}}, pages = {{133--154}}, title = {{{Barth for Future? Eine Barth-Relektüre vor dem Hintergrund der Bewegung „Fridays for Future“.}}}, year = {{2021}}, } @inproceedings{48853, abstract = {{In practise, it is often desirable to provide the decision-maker with a rich set of diverse solutions of decent quality instead of just a single solution. In this paper we study evolutionary diversity optimization for the knapsack problem (KP). Our goal is to evolve a population of solutions that all have a profit of at least (1 - {$ϵ$}) {$\cdot$} OPT, where OPT is the value of an optimal solution. Furthermore, they should differ in structure with respect to an entropy-based diversity measure. To this end we propose a simple ({$\mu$} + 1)-EA with initial approximate solutions calculated by a well-known FPTAS for the KP. We investigate the effect of different standard mutation operators and introduce biased mutation and crossover which puts strong probability on flipping bits of low and/or high frequency within the population. An experimental study on different instances and settings shows that the proposed mutation operators in most cases perform slightly inferior in the long term, but show strong benefits if the number of function evaluations is severely limited.}}, author = {{Bossek, Jakob and Neumann, Aneta and Neumann, Frank}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-8350-9}}, keywords = {{evolutionary algorithms, evolutionary diversity optimization, knapsack problem, tailored operators}}, pages = {{556–564}}, publisher = {{Association for Computing Machinery}}, title = {{{Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms}}}, doi = {{10.1145/3449639.3459364}}, year = {{2021}}, } @inproceedings{48855, abstract = {{Computing sets of high quality solutions has gained increasing interest in recent years. In this paper, we investigate how to obtain sets of optimal solutions for the classical knapsack problem. We present an algorithm to count exactly the number of optima to a zero-one knapsack problem instance. In addition, we show how to efficiently sample uniformly at random from the set of all global optima. In our experimental study, we investigate how the number of optima develops for classical random benchmark instances dependent on their generator parameters. We find that the number of global optima can increase exponentially for practically relevant classes of instances with correlated weights and profits which poses a justification for the considered exact counting problem.}}, author = {{Bossek, Jakob and Neumann, Aneta and Neumann, Frank}}, booktitle = {{Learning and Intelligent Optimization}}, isbn = {{978-3-030-92120-0}}, keywords = {{Dynamic programming, Exact counting, Sampling, Zero-one knapsack problem}}, pages = {{40–54}}, publisher = {{Springer-Verlag}}, title = {{{Exact Counting and~Sampling of Optima for the Knapsack Problem}}}, doi = {{10.1007/978-3-030-92121-7_4}}, year = {{2021}}, } @inproceedings{48860, abstract = {{In the area of evolutionary computation the calculation of diverse sets of high-quality solutions to a given optimization problem has gained momentum in recent years under the term evolutionary diversity optimization. Theoretical insights into the working principles of baseline evolutionary algorithms for diversity optimization are still rare. In this paper we study the well-known Minimum Spanning Tree problem (MST) in the context of diversity optimization where population diversity is measured by the sum of pairwise edge overlaps. Theoretical results provide insights into the fitness landscape of the MST diversity optimization problem pointing out that even for a population of {$\mu$} = 2 fitness plateaus (of constant length) can be reached, but nevertheless diverse sets can be calculated in polynomial time. We supplement our theoretical results with a series of experiments for the unconstrained and constraint case where all solutions need to fulfill a minimal quality threshold. Our results show that a simple ({$\mu$} + 1)-EA can effectively compute a diversified population of spanning trees of high quality.}}, author = {{Bossek, Jakob and Neumann, Frank}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-8350-9}}, keywords = {{evolutionary algorithms, evolutionary diversity optimization, minimum spanning tree, runtime analysis}}, pages = {{198–206}}, publisher = {{Association for Computing Machinery}}, title = {{{Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem}}}, doi = {{10.1145/3449639.3459363}}, year = {{2021}}, } @inbook{48862, abstract = {{Most runtime analyses of randomised search heuristics focus on the expected number of function evaluations to find a unique global optimum. We ask a fundamental question: if additional search points are declared optimal, or declared as desirable target points, do these additional optima speed up evolutionary algorithms? More formally, we analyse the expected hitting time of a target set OPT {$\cup$} S where S is a set of non-optimal search points and OPT is the set of optima and compare it to the expected hitting time of OPT. We show that the answer to our question depends on the number and placement of search points in S. For all black-box algorithms and all fitness functions we show that, if additional optima are placed randomly, even an exponential number of optima has a negligible effect on the expected optimisation time. Considering Hamming balls around all global optima gives an easier target for some algorithms and functions and can shift the phase transition with respect to offspring population sizes in the (1,{$\lambda$}) EA on One-Max. Finally, on functions where search trajectories typically join in a single search point, turning one search point into an optimum drastically reduces the expected optimisation time.}}, author = {{Bossek, Jakob and Sudholt, Dirk}}, booktitle = {{Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms}}, isbn = {{978-1-4503-8352-3}}, keywords = {{evolutionary algorithms, pseudo-boolean functions, runtime analysis, theory}}, pages = {{1–11}}, publisher = {{Association for Computing Machinery}}, title = {{{Do Additional Optima Speed up Evolutionary Algorithms?}}}, year = {{2021}}, } @inbook{48881, abstract = {{Classic automated algorithm selection (AS) for (combinatorial) optimization problems heavily relies on so-called instance features, i.e., numerical characteristics of the problem at hand ideally extracted with computationally low-demanding routines. For the traveling salesperson problem (TSP) a plethora of features have been suggested. Most of these features are, if at all, only normalized imprecisely raising the issue of feature values being strongly affected by the instance size. Such artifacts may have detrimental effects on algorithm selection models. We propose a normalization for two feature groups which stood out in multiple AS studies on the TSP: (a) features based on a minimum spanning tree (MST) and (b) a k-nearest neighbor graph (NNG) transformation of the input instance. To this end we theoretically derive minimum and maximum values for properties of MSTs and k-NNGs of Euclidean graphs. We analyze the differences in feature space between normalized versions of these features and their unnormalized counterparts. Our empirical investigations on various TSP benchmark sets point out that the feature scaling succeeds in eliminating the effect of the instance size. Eventually, a proof-of-concept AS-study shows promising results: models trained with normalized features tend to outperform those trained with the respective vanilla features.}}, author = {{Heins, Jonathan and Bossek, Jakob and Pohl, Janina and Seiler, Moritz and Trautmann, Heike and Kerschke, Pascal}}, booktitle = {{Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms}}, isbn = {{978-1-4503-8352-3}}, keywords = {{automated algorithm selection, graph theory, instance features, normalization, traveling salesperson problem (TSP)}}, pages = {{1–15}}, publisher = {{Association for Computing Machinery}}, title = {{{On the Potential of Normalized TSP Features for Automated Algorithm Selection}}}, year = {{2021}}, } @inproceedings{48876, abstract = {{In recent years, Evolutionary Algorithms (EAs) have frequently been adopted to evolve instances for optimization problems that pose difficulties for one algorithm while being rather easy for a competitor and vice versa. Typically, this is achieved by either minimizing or maximizing the performance difference or ratio which serves as the fitness function. Repeating this process is useful to gain insights into strengths/weaknesses of certain algorithms or to build a set of instances with strong performance differences as a foundation for automatic per-instance algorithm selection or configuration. We contribute to this branch of research by proposing fitness-functions to evolve instances that show large performance differences for more than just two algorithms simultaneously. As a proof-of-principle, we evolve instances of the multi-component Traveling Thief Problem (TTP) for three incomplete TTP-solvers. Our results point out that our strategies are promising, but unsurprisingly their success strongly relies on the algorithms’ performance complementarity.}}, author = {{Bossek, Jakob and Wagner, Markus}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference Companion}}, isbn = {{978-1-4503-8351-6}}, keywords = {{evolutionary algorithms, evolving instances, fitness function, instance hardness, traveling thief problem (TTP)}}, pages = {{1423–1432}}, publisher = {{Association for Computing Machinery}}, title = {{{Generating Instances with Performance Differences for More than Just Two Algorithms}}}, doi = {{10.1145/3449726.3463165}}, year = {{2021}}, } @inproceedings{48893, abstract = {{Computing diverse sets of high-quality solutions has gained increasing attention among the evolutionary computation community in recent years. It allows practitioners to choose from a set of high-quality alternatives. In this paper, we employ a population diversity measure, called the high-order entropy measure, in an evolutionary algorithm to compute a diverse set of high-quality solutions for the Traveling Salesperson Problem. In contrast to previous studies, our approach allows diversifying segments of tours containing several edges based on the entropy measure. We examine the resulting evolutionary diversity optimisation approach precisely in terms of the final set of solutions and theoretical properties. Experimental results show significant improvements compared to a recently proposed edge-based diversity optimisation approach when working with a large population of solutions or long segments.}}, author = {{Nikfarjam, Adel and Bossek, Jakob and Neumann, Aneta and Neumann, Frank}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-8350-9}}, keywords = {{evolutionary algorithms, evolutionary diversity optimisation, high-order entropy, traveling salesperson problem}}, pages = {{600–608}}, publisher = {{Association for Computing Machinery}}, title = {{{Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem}}}, doi = {{10.1145/3449639.3459384}}, year = {{2021}}, } @inproceedings{48891, abstract = {{Submodular functions allow to model many real-world optimisation problems. This paper introduces approaches for computing diverse sets of high quality solutions for submodular optimisation problems with uniform and knapsack constraints. We first present diversifying greedy sampling approaches and analyse them with respect to the diversity measured by entropy and the approximation quality of the obtained solutions. Afterwards, we introduce an evolutionary diversity optimisation (EDO) approach to further improve diversity of the set of solutions. We carry out experimental investigations on popular submodular benchmark problems and analyse trade-offs in terms of solution quality and diversity of the resulting solution sets.}}, author = {{Neumann, Aneta and Bossek, Jakob and Neumann, Frank}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-8350-9}}, keywords = {{evolutionary algorithms, evolutionary diversity optimisation, sub-modular functions}}, pages = {{261–269}}, publisher = {{Association for Computing Machinery}}, title = {{{Diversifying Greedy Sampling and Evolutionary Diversity Optimisation for Constrained Monotone Submodular Functions}}}, doi = {{10.1145/3449639.3459385}}, year = {{2021}}, } @inbook{48892, abstract = {{Evolutionary algorithms based on edge assembly crossover (EAX) constitute some of the best performing incomplete solvers for the well-known traveling salesperson problem (TSP). Often, it is desirable to compute not just a single solution for a given problem, but a diverse set of high quality solutions from which a decision maker can choose one for implementation. Currently, there are only a few approaches for computing a diverse solution set for the TSP. Furthermore, almost all of them assume that the optimal solution is known. In this paper, we introduce evolutionary diversity optimisation (EDO) approaches for the TSP that find a diverse set of tours when the optimal tour is known or unknown. We show how to adopt EAX to not only find a high-quality solution but also to maximise the diversity of the population. The resulting EAX-based EDO approach, termed EAX-EDO is capable of obtaining diverse high-quality tours when the optimal solution for the TSP is known or unknown. A comparison to existing approaches shows that they are clearly outperformed by EAX-EDO.}}, author = {{Nikfarjam, Adel and Bossek, Jakob and Neumann, Aneta and Neumann, Frank}}, booktitle = {{Proceedings of the 16th ACM}/SIGEVO Conference on Foundations of Genetic Algorithms}}, isbn = {{978-1-4503-8352-3}}, keywords = {{edge assembly crossover (EAX), evolutionary algorithms, evolutionary diversity optimisation (EDO), traveling salesperson problem (TSP)}}, pages = {{1–11}}, publisher = {{Association for Computing Machinery}}, title = {{{Computing Diverse Sets of High Quality TSP Tours by EAX-based Evolutionary Diversity Optimisation}}}, year = {{2021}}, } @article{48854, abstract = {{We contribute to the theoretical understanding of randomized search heuristics for dynamic problems. We consider the classical vertex coloring problem on graphs and investigate the dynamic setting where edges are added to the current graph. We then analyze the expected time for randomized search heuristics to recompute high quality solutions. The (1+1) Evolutionary Algorithm and RLS operate in a setting where the number of colors is bounded and we are minimizing the number of conflicts. Iterated local search algorithms use an unbounded color palette and aim to use the smallest colors and, consequently, the smallest number of colors. We identify classes of bipartite graphs where reoptimization is as hard as or even harder than optimization from scratch, i.e., starting with a random initialization. Even adding a single edge can lead to hard symmetry problems. However, graph classes that are hard for one algorithm turn out to be easy for others. In most cases our bounds show that reoptimization is faster than optimizing from scratch. We further show that tailoring mutation operators to parts of the graph where changes have occurred can significantly reduce the expected reoptimization time. In most settings the expected reoptimization time for such tailored algorithms is linear in the number of added edges. However, tailored algorithms cannot prevent exponential times in settings where the original algorithm is inefficient.}}, author = {{Bossek, Jakob and Neumann, Frank and Peng, Pan and Sudholt, Dirk}}, issn = {{0178-4617}}, journal = {{Algorithmica}}, keywords = {{Dynamic optimization, Evolutionary algorithms, Running time analysis}}, number = {{10}}, pages = {{3148–3179}}, title = {{{Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem}}}, doi = {{10.1007/s00453-021-00838-3}}, volume = {{83}}, year = {{2021}}, } @misc{49589, author = {{Fastlabend-Vargas, Daniel}}, booktitle = {{H-Soz-Kult }}, title = {{{Rezension zu: Dräger, Marco: Denkmäler im Geschichtsunterricht Frankfurt am Main 2021.}}}, year = {{2021}}, } @article{23469, abstract = {{The implementation of control systems in metal forming processes improves product quality and productivity. By controlling workpiece properties during the process, beneficial effects caused by forming can be exploited and integrated in the product design. The overall goal of this investigation is to produce tailored tubular parts with a defined locally graded microstructure by means of reverse flow forming. For this purpose, the proposed system aims to control both the desired geometry of the workpiece and additionally the formation of strain-induced α′-martensite content in the metastable austenitic stainless steel AISI 304 L. The paper introduces an overall control scheme, a geometry model for describing the process and changes in the dimensions of the workpiece, as well as a material model for the process-induced formation of martensite, providing equations based on empirical data. Moreover, measurement systems providing a closed feedback loop are presented, including a novel softsensor for in-situ measurements of the martensite content.}}, author = {{Riepold, Markus and Arian, Bahman and Vasquez, Julian Rozo and Homberg, Werner and Walther, Frank and Trächtler, Ansgar}}, issn = {{2666-9129}}, journal = {{Advances in Industrial and Manufacturing Engineering}}, title = {{{Model approaches for closed-loop property control for flow forming}}}, doi = {{10.1016/j.aime.2021.100057}}, year = {{2021}}, } @misc{49759, author = {{Huybrechts, Yves and Scholliers, Peter}}, publisher = {{BelgienNet}}, title = {{{"Searching for a Belgian culinary identity - Referat von Prof. Dr. Peter Scholliers" (VIDEO)}}}, year = {{2021}}, } @misc{49765, author = {{Huybrechts, Yves}}, publisher = {{BelgienNet}}, title = {{{"Der belgische Symbolismus - Interview mit dem Direktor der Alten Nationalgalerie, Dr. Ralph Gleis" (PODCAST)}}}, year = {{2021}}, } @misc{49752, author = {{Huybrechts, Yves}}, publisher = {{BelgienNet}}, title = {{{"Gebaute Träume - Wohnkultur in Flandern und Wallonien" (VIDEO)}}}, year = {{2021}}, } @inbook{49822, author = {{Taschl-Erber, Andrea and Lumesberger-Loisl, Barbara}}, booktitle = {{Wort-Schatz Bibel}}, pages = {{1--20}}, title = {{{Redeverbot für Frauen bei Paulus? Die Macht der Rezeption}}}, year = {{2021}}, }