@article{46347,
  abstract     = {{We continue recent work on the definition of multimodality in multiobjective optimization (MO) and the introduction of a test bed for multimodal MO problems. This goes beyond well-known diversity maintenance approaches but instead focuses on the landscape topology induced by the objective functions. More general multimodal MO problems are considered by allowing ellipsoid contours for single-objective subproblems. An experimental analysis compares two MO algorithms, one that explicitly relies on hypervolume gradient approximation, and one that is based on local search, both on a selection of generated example problems. We do not focus on performance but on the interaction induced by the problems and algorithms, which can be described by means of specific characteristics explicitly designed for the multimodal MO setting. Furthermore, we widen the scope of our analysis by additionally applying visualization techniques in the decision space. This strengthens and extends the foundations for Exploratory Landscape Analysis (ELA) in MO.}},
  author       = {{Kerschke, Pascal and Wang, Hao and Preuss, Mike and Grimme, Christian and Deutz, André and Trautmann, Heike and Emmerich, Michael}},
  journal      = {{Evolutionary Computation (ECJ)}},
  number       = {{4}},
  pages        = {{577–609}},
  title        = {{{Search Dynamics on Multimodal Multi-Objective Problems}}},
  doi          = {{10.1162/evco_a_00234}},
  volume       = {{27}},
  year         = {{2019}},
}

@inproceedings{48841,
  abstract     = {{We tackle a bi-objective dynamic orienteering problem where customer requests arise as time passes by. The goal is to minimize the tour length traveled by a single delivery vehicle while simultaneously keeping the number of dismissed dynamic customers to a minimum. We propose a dynamic Evolutionary Multi-Objective Algorithm which is grounded on insights gained from a previous series of work on an a-posteriori version of the problem, where all request times are known in advance. In our experiments, we simulate different decision maker strategies and evaluate the development of the Pareto-front approximations on exemplary problem instances. It turns out, that despite severely reduced computational budget and no oracle-knowledge of request times the dynamic EMOA is capable of producing approximations which partially dominate the results of the a-posteriori EMOA and dynamic integer linear programming strategies.}},
  author       = {{Bossek, Jakob and Grimme, Christian and Meisel, Stephan and Rudolph, Günter and Trautmann, Heike}},
  booktitle    = {{Evolutionary Multi-Criterion Optimization (EMO)}},
  editor       = {{Deb, Kalyanmoy and Goodman, Erik and Coello Coello, Carlos A. and Klamroth, Kathrin and Miettinen, Kaisa and Mostaghim, Sanaz and Reed, Patrick}},
  isbn         = {{978-3-030-12598-1}},
  keywords     = {{Combinatorial optimization, Dynamic optimization, Metaheuristics, Multi-objective optimization, Vehicle routing}},
  pages        = {{516–528}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Bi-Objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm}}},
  doi          = {{10.1007/978-3-030-12598-1_41}},
  year         = {{2019}},
}

@inproceedings{48842,
  abstract     = {{Evolutionary algorithms have successfully been applied to evolve problem instances that exhibit a significant difference in performance for a given algorithm or a pair of algorithms inter alia for the Traveling Salesperson Problem (TSP). Creating a large variety of instances is crucial for successful applications in the blooming field of algorithm selection. In this paper, we introduce new and creative mutation operators for evolving instances of the TSP. We show that adopting those operators in an evolutionary algorithm allows for the generation of benchmark sets with highly desirable properties: (1) novelty by clear visual distinction to established benchmark sets in the field, (2) visual and quantitative diversity in the space of TSP problem characteristics, and (3) significant performance differences with respect to the restart versions of heuristic state-of-the-art TSP solvers EAX and LKH. The important aspect of diversity is addressed and achieved solely by the proposed mutation operators and not enforced by explicit diversity preservation.}},
  author       = {{Bossek, Jakob and Kerschke, Pascal and Neumann, Aneta and Wagner, Markus and Neumann, Frank and Trautmann, Heike}},
  booktitle    = {{Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms}},
  isbn         = {{978-1-4503-6254-2}},
  keywords     = {{benchmarking, instance features, optimization, problem generation, traveling salesperson problem}},
  pages        = {{58–71}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators}}},
  doi          = {{10.1145/3299904.3340307}},
  year         = {{2019}},
}

@inproceedings{48843,
  abstract     = {{We contribute to the theoretical understanding of randomized search heuristics for dynamic problems. We consider the classical graph coloring problem 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. This includes the (1+1) EA and RLS in a setting where the number of colors is bounded and we are minimizing the number of conflicts as well as iterated local search algorithms that use an unbounded color palette and aim to use the smallest colors and - as a consequence - 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. Furthermore, we show how to speed up computations by using problem specific operators concentrating on parts of the graph where changes have occurred.}},
  author       = {{Bossek, Jakob and Neumann, Frank and Peng, Pan and Sudholt, Dirk}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference}},
  isbn         = {{978-1-4503-6111-8}},
  keywords     = {{dynamic optimization, evolutionary algorithms, running time analysis, theory}},
  pages        = {{1443–1451}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring}}},
  doi          = {{10.1145/3321707.3321792}},
  year         = {{2019}},
}

@inproceedings{48840,
  abstract     = {{Research has shown that for many single-objective graph problems where optimum solutions are composed of low weight sub-graphs, such as the minimum spanning tree problem (MST), mutation operators favoring low weight edges show superior performance. Intuitively, similar observations should hold for multi-criteria variants of such problems. In this work, we focus on the multi-criteria MST problem. A thorough experimental study is conducted where we estimate the probability of edges being part of non-dominated spanning trees as a function of the edges’ non-domination level or domination count, respectively. Building on gained insights, we propose several biased one-edge-exchange mutation operators that differ in the used edge-selection probability distribution (biased towards edges of low rank). Our empirical analysis shows that among different graph types (dense and sparse) and edge weight types (both uniformly random and combinations of Euclidean and uniformly random) biased edge-selection strategies perform superior in contrast to the baseline uniform edge-selection. Our findings are in particular strong for dense graphs.}},
  author       = {{Bossek, Jakob and Grimme, Christian and Neumann, Frank}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference}},
  isbn         = {{978-1-4503-6111-8}},
  keywords     = {{biased mutation, combinatorial optimization, minimum spanning tree, multi-objective optimization}},
  pages        = {{516–523}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem}}},
  doi          = {{10.1145/3321707.3321818}},
  year         = {{2019}},
}

@inproceedings{48858,
  abstract     = {{The $$\textbackslash mathcal NP$$-hard multi-criteria shortest path problem (mcSPP) is of utmost practical relevance, e.~g., in navigation system design and logistics. We address the problem of approximating the Pareto-front of the mcSPP with sum objectives. We do so by proposing a new mutation operator for multi-objective evolutionary algorithms that solves single-objective versions of the shortest path problem on subgraphs. A rigorous empirical benchmark on a diverse set of problem instances shows the effectiveness of the approach in comparison to a well-known mutation operator in terms of convergence speed and approximation quality. In addition, we glance at the neighbourhood structure and similarity of obtained Pareto-optimal solutions and derive promising directions for future work.}},
  author       = {{Bossek, Jakob and Grimme, Christian}},
  booktitle    = {{Learning and Intelligent Optimization}},
  editor       = {{Battiti, Roberto and Brunato, Mauro and Kotsireas, Ilias and Pardalos, Panos M.}},
  isbn         = {{978-3-030-05348-2}},
  pages        = {{184–198}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-criteria Shortest Path Problems}}},
  doi          = {{10.1007/978-3-030-05348-2_17}},
  year         = {{2019}},
}

@inproceedings{48870,
  abstract     = {{The edge coloring problem asks for an assignment of colors to edges of a graph such that no two incident edges share the same color and the number of colors is minimized. It is known that all graphs with maximum degree {$\Delta$} can be colored with {$\Delta$} or {$\Delta$} + 1 colors, but it is NP-hard to determine whether {$\Delta$} colors are sufficient. We present the first runtime analysis of evolutionary algorithms (EAs) for the edge coloring problem. Simple EAs such as RLS and (1+1) EA efficiently find (2{$\Delta$} - 1)-colorings on arbitrary graphs and optimal colorings for even and odd cycles, paths, star graphs and arbitrary trees. A partial analysis for toroids also suggests efficient runtimes in bipartite graphs with many cycles. Experiments support these findings and investigate additional graph classes such as hypercubes, complete graphs and complete bipartite graphs. Theoretical and experimental results suggest that simple EAs find optimal colorings for all these graph classes in expected time O({$\Delta\mathscrl$}2m log m), where m is the number of edges and {$\mathscrl$} is the length of the longest simple path in the graph.}},
  author       = {{Bossek, Jakob and Sudholt, Dirk}},
  booktitle    = {{Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms}},
  isbn         = {{978-1-4503-6254-2}},
  keywords     = {{edge coloring problem, runtime analysis}},
  pages        = {{102–115}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{Time Complexity Analysis of RLS and (1 + 1) EA for the Edge Coloring Problem}}},
  doi          = {{10.1145/3299904.3340311}},
  year         = {{2019}},
}

@inproceedings{48875,
  abstract     = {{A multiobjective perspective onto common performance measures such as the PAR10 score or the expected runtime of single-objective stochastic solvers is presented by directly investigating the tradeoff between the fraction of failed runs and the average runtime. Multi-objective indicators operating in the bi-objective space allow for an overall performance comparison on a set of instances paving the way for instance-based automated algorithm selection techniques.}},
  author       = {{Bossek, Jakob and Trautmann, Heike}},
  booktitle    = {{Learning and Intelligent Optimization}},
  editor       = {{Battiti, Roberto and Brunato, Mauro and Kotsireas, Ilias and Pardalos, Panos M.}},
  isbn         = {{978-3-030-05348-2}},
  keywords     = {{Algorithm selection, Performance measurement}},
  pages        = {{215–219}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time}}},
  doi          = {{10.1007/978-3-030-05348-2_19}},
  year         = {{2019}},
}

@article{48877,
  abstract     = {{OpenML is an online machine learning platform where researchers can easily share data, machine learning tasks and experiments as well as organize them online to work and collaborate more efficiently. In this paper, we present an R package to interface with the OpenML platform and illustrate its usage in combination with the machine learning R package mlr (Bischl et al. J Mach Learn Res 17(170):1—5, 2016). We show how the OpenML package allows R users to easily search, download and upload data sets and machine learning tasks. Furthermore, we also show how to upload results of experiments, share them with others and download results from other users. Beyond ensuring reproducibility of results, the OpenML platform automates much of the drudge work, speeds up research, facilitates collaboration and increases the users’ visibility online.}},
  author       = {{Casalicchio, Giuseppe and Bossek, Jakob and Lang, Michel and Kirchhoff, Dominik and Kerschke, Pascal and Hofner, Benjamin and Seibold, Heidi and Vanschoren, Joaquin and Bischl, Bernd}},
  issn         = {{0943-4062}},
  journal      = {{Computational Statistics}},
  keywords     = {{Databases, Machine learning, R, Reproducible research}},
  number       = {{3}},
  pages        = {{977–991}},
  title        = {{{OpenML: An R Package to Connect to the Machine Learning Platform OpenML}}},
  doi          = {{10.1007/s00180-017-0742-2}},
  volume       = {{34}},
  year         = {{2019}},
}

@misc{52478,
  author       = {{Mehta, Jinay D}},
  title        = {{{Multithreaded Software/Hardware Programming with ReconOS/freeRTOS on a Reconfigurable System-on-Chip}}},
  year         = {{2019}},
}

@inproceedings{46339,
  abstract     = {{Evolutionary algorithms have successfully been applied to evolve problem instances that exhibit a significant difference in performance for a given algorithm or a pair of algorithms inter alia for the Traveling Salesperson Problem (TSP). Creating a large variety of instances is crucial for successful applications in the blooming field of algorithm selection. In this paper, we introduce new and creative mutation operators for evolving instances of the TSP. We show that adopting those operators in an evolutionary algorithm allows for the generation of benchmark sets with highly desirable properties: (1) novelty by clear visual distinction to established benchmark sets in the field, (2) visual and quantitative diversity in the space of TSP problem characteristics, and (3) significant performance differences with respect to the restart versions of heuristic state-of-the-art TSP solvers EAX and LKH. The important aspect of diversity is addressed and achieved solely by the proposed mutation operators and not enforced by explicit diversity preservation.}},
  author       = {{Bossek, Jakob and Kerschke, Pascal and Neumann, Aneta and Wagner, Markus and Neumann, Frank and Trautmann, Heike}},
  booktitle    = {{Proceedings of the 15$^th$ ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA XV)}},
  editor       = {{Friedrich, Tobias and Doerr, Carola and Arnold, Dirk}},
  pages        = {{58–71}},
  title        = {{{Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators}}},
  doi          = {{10.1145/3299904.3340307}},
  year         = {{2019}},
}

@inproceedings{46338,
  abstract     = {{We tackle a bi-objective dynamic orienteering problem where customer requests arise as time passes by. The goal is to minimize the tour length traveled by a single delivery vehicle while simultaneously keeping the number of dismissed dynamic customers to a minimum. We propose a dynamic Evolutionary Multi-Objective Algorithm which is grounded on insights gained from a previous series of work on an a-posteriori version of the problem, where all request times are known in advance. In our experiments, we simulate different decision maker strategies and evaluate the development of the Pareto-front approximations on exemplary problem instances. It turns out, that despite severely reduced computational budget and no oracle-knowledge of request times the dynamic EMOA is capable of producing approximations which partially dominate the results of the a-posteriori EMOA and dynamic integer linear programming strategies.}},
  author       = {{Bossek, Jakob and Grimme, Christian and Meisel, Stephan and Rudolph, Günter and Trautmann, Heike}},
  booktitle    = {{Evolutionary Multi-Criterion Optimization (EMO)}},
  editor       = {{Deb, Kalyanmoy and Goodman, Erik and Coello, Coello Carlos A. and Klamroth, Kathrin and Miettinen, Kaisa and Mostaghim, Sanaz and Reed, Patrick}},
  isbn         = {{978-3-030-12597-4}},
  pages        = {{516–528}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm}}},
  doi          = {{10.1007/978-3-030-12598-1_41}},
  volume       = {{11411}},
  year         = {{2019}},
}

@inproceedings{46337,
  abstract     = {{A multiobjective perspective onto common performance measures such as the PAR10 score or the expected runtime of single-objective stochastic solvers is presented by directly investigating the tradeoff between the fraction of failed runs and the average runtime. Multi-objective indicators operating in the bi-objective space allow for an overall performance comparison on a set of instances paving the way for instance-based automated algorithm selection techniques.}},
  author       = {{Bossek, Jakob and Trautmann, Heike}},
  booktitle    = {{Learning and Intelligent Optimization}},
  editor       = {{Battiti, R and Brunato, M and Kotsireas, I and Pardalos, P}},
  isbn         = {{978-3-030-05347-5}},
  pages        = {{215–219}},
  publisher    = {{Springer}},
  title        = {{{Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time}}},
  volume       = {{11353}},
  year         = {{2019}},
}

@inproceedings{10108,
  abstract     = {{Recent years have seen the development of numerous tools for the analysis of taint flows in Android apps. Taint analyses aim at detecting data leaks, accidentally or by purpose programmed into apps. Often, such tools specialize in the treatment of specific features impeding precise taint analysis (like reflection or inter-app communication). This multitude of tools, their specific applicability and their various combination options complicate the selection of a tool (or multiple tools) when faced with an analysis instance, even for knowledgeable users, and hence hinders the successful adoption of taint analyses.

In this work, we thus present CoDiDroid, a framework for cooperative Android app analysis. CoDiDroid (1) allows users to ask questions about flows in apps in varying degrees of detail, (2) automatically generates subtasks for answering such questions, (3) distributes tasks onto analysis tools (currently DroidRA, FlowDroid, HornDroid, IC3 and two novel tools) and (4) at the end merges tool answers on subtasks into an overall answer. Thereby, users are freed from having to learn about the use and functionality of all these tools while still being able to leverage their capabilities. Moreover, we experimentally show that cooperation among tools pays off with respect to effectiveness, precision and scalability.}},
  author       = {{Pauck, Felix and Wehrheim, Heike}},
  booktitle    = {{Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering}},
  isbn         = {{978-1-4503-5572-8}},
  keywords     = {{Android Taint Analysis, Cooperation, Precision, Tools}},
  pages        = {{374--384}},
  title        = {{{Together Strong: Cooperative Android App Analysis}}},
  doi          = {{10.1145/3338906.3338915}},
  year         = {{2019}},
}

@inproceedings{13874,
  author       = {{Isenberg, Tobias and Jakobs, Marie-Christine and Pauck, Felix and Wehrheim, Heike}},
  booktitle    = {{Tests and Proofs - 13th International Conference, {TAP} 2019, Held as Part of the Third World Congress on Formal Methods 2019, Porto, Portugal, October 9-11, 2019, Proceedings}},
  pages        = {{3--20}},
  title        = {{{When Are Software Verification Results Valid for Approximate Hardware?}}},
  doi          = {{10.1007/978-3-030-31157-5_1}},
  year         = {{2019}},
}

@article{20243,
  author       = {{Rohlfing, Katharina and Leonardi, Giuseppe and Nomikou, Iris and Rączaszek-Leonardi, Joanna and Hüllermeier, Eyke}},
  journal      = {{IEEE Transactions on Cognitive and Developmental Systems}},
  title        = {{{Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches}}},
  doi          = {{10.1109/TCDS.2019.2892991}},
  year         = {{2019}},
}

@inproceedings{13297,
  author       = {{Gharibian, Sevag and Parekh, Ojas}},
  booktitle    = {{Proceedings of the 22nd International Workshop on Approximation Algorithms for Combinatorial Optimization Problems (APPROX)}},
  pages        = {{31:1--31:17}},
  title        = {{{Almost Optimal Classical Approximation Algorithms for a Quantum Generalization of Max-Cut}}},
  doi          = {{10.4230/LIPICS.APPROX-RANDOM.2019.31}},
  volume       = {{145}},
  year         = {{2019}},
}

@article{13558,
  author       = {{Gharibian, Sevag and Yirka, Justin }},
  journal      = {{Quantum}},
  pages        = {{189}},
  title        = {{{The complexity of simulating local measurements on quantum systems}}},
  doi          = {{10.22331/q-2019-09-30-189}},
  volume       = {{3}},
  year         = {{2019}},
}

@inproceedings{9913,
  abstract     = {{Reconfigurable hardware has received considerable attention as a platform that enables dynamic hardware updates and thus is able to adapt new configurations at runtime. However, due to their dynamic nature, e.g., field-programmable gate arrays (FPGA) are subject to a constant possibility of attacks, since each new configuration might be compromised. Trojans for reconfigurable hardware that evade state-of-the-art detection techniques and even formal verification, are thus a large threat to these devices. One such stealthy hardware Trojan, that is inserted and activated in two stages by compromised electronic design automation (EDA) tools, has recently been presented and shown to evade all forms of classical pre-configuration detection techniques. This paper presents a successful pre-configuration countermeasure against this ``Malicious Look-up-table (LUT)''-hardware Trojan, by employing bitstream-level Proof-Carrying Hardware (PCH). We show that the method is able to alert innocent module creators to infected EDA tools, and to prohibit malicious ones to sell infected modules to unsuspecting customers.}},
  author       = {{Ahmed, Qazi Arbab and Wiersema, Tobias and Platzner, Marco}},
  booktitle    = {{Applied Reconfigurable Computing}},
  editor       = {{Hochberger, Christian and Nelson, Brent and Koch, Andreas and Woods, Roger and Diniz, Pedro}},
  isbn         = {{978-3-030-17227-5}},
  location     = {{Darmstadt, Germany}},
  pages        = {{127--136}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Proof-Carrying Hardware Versus the Stealthy Malicious LUT Hardware Trojan}}},
  doi          = {{10.1007/978-3-030-17227-5_10}},
  volume       = {{11444}},
  year         = {{2019}},
}

@misc{45231,
  author       = {{N., N.}},
  title        = {{{Conception of a digital Sales Bonus System based on Blockchain Technology}}},
  year         = {{2019}},
}