@techreport{17019, abstract = {{The scientific impact of research papers is multi-dimensional and can be determined quantitatively by means of citation analysis and qualitatively by means of content analysis. Accounting for the widely acknowledged limitations of pure citation analysis, we adopt a knowledge-based perspective on scientific impact to develop a methodology for content-based citation analysis which allows determining how papers have enabled knowledge development in subsequent research (knowledge impact). As knowledge development differs between research genres, we develop a new knowledgebased citation analysis methodology for the genre of standalone literature reviews (LRs). We apply the suggested methodology to the IS business value domain by manually coding 22 LRs and 1,228 citing papers (CPs) and show that the results challenge the assumption that citations indicate knowledge impact. We derive implications for distinguishing knowledge impact from citation impact in the LR genre. Finally, we develop recommendations for authors of LRs, scientific evaluation committees and editorial boards of journals how to apply and benefit from the suggested methodology, and we discuss its efficiency and automatization.}}, author = {{Schryen, Guido and Wagner, Gerit and Benlian, Alexander}}, keywords = {{Scientific impact, knowledge impact, content-based citation analysis, methodology}}, title = {{{Distinguishing Knowledge Impact from Citation Impact: A Methodology for Analysing Knowledge Impact for the Literature Review Genre}}}, year = {{2020}}, } @inproceedings{17082, abstract = {{Data-parallel applications run on cluster of servers in a datacenter and their communication triggers correlated resource demand on multiple links that can be abstracted as coflow. They often desire predictable network performance, which can be passed to network via coflow abstraction for application-aware network scheduling. In this paper, we propose a heuristic and an optimization algorithm for predictable network performance such that they guarantee coflows completion within their deadlines. The algorithms also ensure high network utilization, i.e., it's work-conserving, and avoids starvation of coflows. We evaluate both algorithms via trace-driven simulation and show that they admit 1.1x more coflows than the Varys scheme while meeting their deadlines.}}, author = {{Hasnain, Asif and Karl, Holger}}, booktitle = {{2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID)}}, keywords = {{Coflow, Scheduling, Deadlines, Data centers}}, location = {{Melbourne, Australia}}, publisher = {{IEEE Computer Society}}, title = {{{Coflow Scheduling with Performance Guarantees for Data Center Applications}}}, doi = {{https://doi.org/10.1109/CCGrid49817.2020.00010}}, year = {{2020}}, } @article{10596, abstract = {{Multi-objective optimization is an active field of research that has many applications. Owing to its success and because decision-making processes are becoming more and more complex, there is a recent trend for incorporating many objectives into such problems. The challenge with such problems, however, is that the dimensions of the solution sets—the so-called Pareto sets and fronts—grow with the number of objectives. It is thus no longer possible to compute or to approximate the entire solution set of a given problem that contains many (e.g. more than three) objectives. On the other hand, the computation of single solutions (e.g. via scalarization methods) leads to unsatisfying results in many cases, even if user preferences are incorporated. In this article, the Pareto Explorer tool is presented—a global/local exploration tool for the treatment of many-objective optimization problems (MaOPs). In the first step, a solution of the problem is computed via a global search algorithm that ideally already includes user preferences. In the second step, a local search along the Pareto set/front of the given MaOP is performed in user specified directions. For this, several continuation-like procedures are proposed that can incorporate preferences defined in decision, objective, or in weight space. The applicability and usefulness of Pareto Explorer is demonstrated on benchmark problems as well as on an application from industrial laundry design.}}, author = {{Schütze, Oliver and Cuate, Oliver and Martín, Adanay and Peitz, Sebastian and Dellnitz, Michael}}, issn = {{0305-215X}}, journal = {{Engineering Optimization}}, number = {{5}}, pages = {{832--855}}, title = {{{Pareto Explorer: a global/local exploration tool for many-objective optimization problems}}}, doi = {{10.1080/0305215x.2019.1617286}}, volume = {{52}}, year = {{2020}}, } @article{15628, abstract = {{The three-dimensional (3D) crystal structures of the GAF3 domain of cyanobacteriochrome Slr1393 (Synechocystis PCC6803) carrying a phycocyanobilin chromophore could be solved in both 15-Z dark-adapted state, Pr, λmax = 649 nm, and 15-E photoproduct, Pg, λmax = 536 nm (resolution, 1.6 and 1.86 Å, respectively). The structural data allowed identifying the large spectral shift of the Pr-to-Pg conversion as resulting from an out-of-plane rotation of the chromophore’s peripheral rings and an outward movement of a short helix formed from a formerly unstructured loop. In addition, a third structure (2.1-Å resolution) starting from the photoproduct crystals allowed identification of elements that regulate the absorption maxima. In this peculiar form, generated during X-ray exposition, protein and chromophore conformation still resemble the photoproduct state, except for the D-ring already in 15-Z configuration and tilted out of plane akin the dark state. Due to its formation from the photoproduct, it might be considered an early conformational change initiating the parental state-recovering photocycle. The high quality and the distinct features of the three forms allowed for applying quantum-chemical calculations in the framework of multiscale modeling to rationalize the absorption maxima changes. A systematic analysis of the PCB chromophore in the presence and absence of the protein environment showed that the direct electrostatic effect is negligible on the spectral tuning. However, the protein forces the outer pyrrole rings of the chromophore to deviate from coplanarity, which is identified as the dominating factor for the color regulation.}}, author = {{Xu, Xiuling and Port, Astrid and Wiebeler, Christian and Zhao, Kai-Hong and Schapiro, Igor and Gärtner, Wolfgang}}, issn = {{0027-8424}}, journal = {{Proceedings of the National Academy of Sciences}}, title = {{{Structural Elements Regulating the Photochromicity in a Cyanobacteriochrome}}}, doi = {{10.1073/pnas.1910208117}}, year = {{2020}}, } @article{15022, author = {{Schryen, Guido}}, journal = {{European Journal of Operational Research}}, number = {{1}}, pages = {{1 -- 18}}, publisher = {{Elsevier}}, title = {{{Parallel computational optimization in operations research: A new integrative framework, literature review and research directions}}}, volume = {{287}}, year = {{2020}}, } @inproceedings{16213, abstract = {{Automated synthesis of approximate circuits via functional approximations is of prominent importance to provide efficiency in energy, runtime, and chip area required to execute an application. Approximate circuits are usually obtained either through analytical approximation methods leveraging approximate transformations such as bit-width scaling or via iterative search-based optimization methods when a library of approximate components, e.g., approximate adders and multipliers, is available. For the latter, exploring the extremely large design space is challenging in terms of both computations and quality of results. While the combination of both methods can create more room for further approximations, the \textit{Design Space Exploration}~(DSE) becomes a crucial issue. In this paper, we present such a hybrid synthesis methodology that applies a low-cost analytical method followed by parallel stochastic search-based optimization. We address the DSE challenge through efficient pruning of the design space and skipping unnecessary expensive testing and/or verification steps. The experimental results reveal up to 10.57x area savings in comparison with both purely analytical or search-based approaches. }}, author = {{Awais, Muhammad and Ghasemzadeh Mohammadi, Hassan and Platzner, Marco}}, booktitle = {{Proceedings of the 30th ACM Great Lakes Symposium on VLSI (GLSVLSI) 2020}}, location = {{Beijing, China}}, pages = {{421--426}}, publisher = {{ACM}}, title = {{{A Hybrid Synthesis Methodology for Approximate Circuits}}}, doi = {{10.1145/3386263.3406952}}, year = {{2020}}, } @inproceedings{16219, abstract = {{Network function virtualization (NFV) proposes to replace physical middleboxes with more flexible virtual network functions (VNFs). To dynamically adjust to everchanging traffic demands, VNFs have to be instantiated and their allocated resources have to be adjusted on demand. Deciding the amount of allocated resources is non-trivial. Existing optimization approaches often assume fixed resource requirements for each VNF instance. However, this can easily lead to either waste of resources or bad service quality if too many or too few resources are allocated. To solve this problem, we train machine learning models on real VNF data, containing measurements of performance and resource requirements. For each VNF, the trained models can then accurately predict the required resources to handle a certain traffic load. We integrate these machine learning models into an algorithm for joint VNF scaling and placement and evaluate their impact on resulting VNF placements. Our evaluation based on real-world data shows that using suitable machine learning models effectively avoids over- and underallocation of resources, leading to up to 12 times lower resource consumption and better service quality with up to 4.5 times lower total delay than using standard fixed resource allocation.}}, author = {{Schneider, Stefan Balthasar and Satheeschandran, Narayanan Puthenpurayil and Peuster, Manuel and Karl, Holger}}, booktitle = {{IEEE Conference on Network Softwarization (NetSoft)}}, location = {{Ghent, Belgium}}, publisher = {{IEEE}}, title = {{{Machine Learning for Dynamic Resource Allocation in Network Function Virtualization}}}, year = {{2020}}, } @inbook{16289, abstract = {{In the development of model predictive controllers for PDE-constrained problems, the use of reduced order models is essential to enable real-time applicability. Besides local linearization approaches, proper orthogonal decomposition (POD) has been most widely used in the past in order to derive such models. Due to the huge advances concerning both theory as well as the numerical approximation, a very promising alternative based on the Koopman operator has recently emerged. In this chapter, we present two control strategies for model predictive control of nonlinear PDEs using data-efficient approximations of the Koopman operator. In the first one, the dynamic control system is replaced by a small number of autonomous systems with different yet constant inputs. The control problem is consequently transformed into a switching problem. In the second approach, a bilinear surrogate model is obtained via a convex combination of these autonomous systems. Using a recent convergence result for extended dynamic mode decomposition (EDMD), convergence of the reduced objective function can be shown. We study the properties of these two strategies with respect to solution quality, data requirements, and complexity of the resulting optimization problem using the 1-dimensional Burgers equation and the 2-dimensional Navier–Stokes equations as examples. Finally, an extension for online adaptivity is presented.}}, author = {{Peitz, Sebastian and Klus, Stefan}}, booktitle = {{Lecture Notes in Control and Information Sciences}}, isbn = {{9783030357122}}, issn = {{0170-8643}}, pages = {{257--282}}, publisher = {{Springer}}, title = {{{Feedback Control of Nonlinear PDEs Using Data-Efficient Reduced Order Models Based on the Koopman Operator}}}, doi = {{10.1007/978-3-030-35713-9_10}}, volume = {{484}}, year = {{2020}}, } @inproceedings{29939, abstract = {{In this paper, a full-bridge modular multilevel converter (MMC) and two half-bridge-based MMCs are evaluated for high-current low-voltage e.g. 100 - 400V DC-applications such as electrolysis, arc welding or datacenters with DC-power distribution. Usually, modular multilevel converters are used in high-voltage DC-applications (HVDC) in the multiple kV-range, but to meet the needs of a high-current demand at low output voltage levels, the modular converter concept requires adaptations. In the proposed concept, the MMC is used to step-down the three-phase medium-voltage of 10kV, and provide up to 1 MW to the load. Therefore, each module is extended by an LLC resonant converter to adapt to the specific electrolyzers DC-voltage range of 142 - 220V and to provide galvanic isolation. The six-arm MMC converter with half-bridge modules can be simplified and optimized by removing three arms, and thus halving the number of modules. In addition, the module voltage ripple and capacitor losses are decreased by 22% and 30% respectively. By rearranging the components of the half-bridge MMC to build a MMC consisting of grid-side full-bridge modules, the voltage ripple is further reduced by 78% and capacitor losses by 64%, while ensuring identical costs and volume for all MMCs. Finally, the LLC resonant converter is designed for the most efficient full-bridge MMC. The LLC can not operate at resonance with a fixed nominal module voltage of 770V because the output voltage is varying between 142 - 220V. By decreasing the module voltage down to 600V, additional points of operation can be operated in resonance, and the remaining are closer to resonance. The option to decrease the module voltage down to 600V, increases the number of required modules per arm from 12 to 15, which requires to balance the losses of the LLCs and the grid-side stages.}}, author = {{Unruh, Roland and Schafmeister, Frank and Böcker, Joachim}}, booktitle = {{2020 22nd European Conference on Power Electronics and Applications (EPE'20 ECCE Europe)}}, keywords = {{Multilevel converters, Resonant converter, High voltage power converters, ZVS Converters, Combination MMC LLC}}, location = {{Lyon, France}}, publisher = {{IEEE}}, title = {{{Evaluation of MMCs for High-Power Low-Voltage DC-Applications in Combination with the Module LLC-Design}}}, doi = {{10.23919/epe20ecceeurope43536.2020.9215687}}, year = {{2020}}, } @unpublished{21623, abstract = {{Micro- and smart grids (MSG) play an important role both for integrating renewable energy sources in conventional electricity grids and for providing power supply in remote areas. Modern MSGs are largely driven by power electronic converters due to their high efficiency and flexibility. Nevertheless, controlling MSGs is a challenging task due to highest requirements on energy availability, safety and voltage quality within a wide range of different MSG topologies. This results in a high demand for comprehensive testing of new control concepts during their development phase and comparisons with the state of the art in order to ensure their feasibility. This applies in particular to data-driven control approaches from the field of reinforcement learning (RL), whose stability and operating behavior can hardly be evaluated a priori. Therefore, the OpenModelica Microgrid Gym (OMG) package, an open-source software toolbox for the simulation and control optimization of MSGs, is proposed. It is capable of modeling and simulating arbitrary MSG topologies and offers a Python-based interface for plug \& play controller testing. In particular, the standardized OpenAI Gym interface allows for easy RL-based controller integration. Besides the presentation of the OMG toolbox, application examples are highlighted including safe Bayesian optimization for low-level controller tuning.}}, author = {{Bode, Henrik and Heid, Stefan and Weber, Daniel and Hüllermeier, Eyke and Wallscheid, Oliver}}, booktitle = {{arXiv:2005.04869}}, title = {{{Towards a Scalable and Flexible Simulation and Testing Environment Toolbox for Intelligent Microgrid Control}}}, year = {{2020}}, } @article{16297, abstract = {{In real-world problems, uncertainties (e.g., errors in the measurement, precision errors) often lead to poor performance of numerical algorithms when not explicitly taken into account. This is also the case for control problems, where optimal solutions can degrade in quality or even become infeasible. Thus, there is the need to design methods that can handle uncertainty. In this work, we consider nonlinear multi-objective optimal control problems with uncertainty on the initial conditions, and in particular their incorporation into a feedback loop via model predictive control (MPC). In multi-objective optimal control, an optimal compromise between multiple conflicting criteria has to be found. For such problems, not much has been reported in terms of uncertainties. To address this problem class, we design an offline/online framework to compute an approximation of efficient control strategies. This approach is closely related to explicit MPC for nonlinear systems, where the potentially expensive optimization problem is solved in an offline phase in order to enable fast solutions in the online phase. In order to reduce the numerical cost of the offline phase, we exploit symmetries in the control problems. Furthermore, in order to ensure optimality of the solutions, we include an additional online optimization step, which is considerably cheaper than the original multi-objective optimization problem. We test our framework on a car maneuvering problem where safety and speed are the objectives. The multi-objective framework allows for online adaptations of the desired objective. Alternatively, an automatic scalarizing procedure yields very efficient feedback controls. Our results show that the method is capable of designing driving strategies that deal better with uncertainties in the initial conditions, which translates into potentially safer and faster driving strategies.}}, author = {{Hernández Castellanos, Carlos Ignacio and Ober-Blöbaum, Sina and Peitz, Sebastian}}, journal = {{International Journal of Robust and Nonlinear Control}}, pages = {{7593--7618}}, title = {{{Explicit Multi-objective Model Predictive Control for Nonlinear Systems Under Uncertainty}}}, doi = {{10.1002/rnc.5197}}, volume = {{30(17)}}, year = {{2020}}, } @article{27022, abstract = {{Abstract Purpose While observational studies revealed inverse associations between serum vitamin D levels [25(OH)D] and depression, randomized controlled trials (RCT) in children and adolescents are lacking. This RCT examined the effect of an untreated vitamin D deficiency compared to an immediate vitamin D3 supplementation on depression scores in children and adolescents during standard day and in-patient psychiatric treatment. Methods Patients with vitamin D deficiency [25(OH)D ≤ 30 nmol/l] and at least mild depression [Beck Depression Inventory II (BDI-II) > 13] (n = 113) were 1:1 randomized into verum (VG; 2640 IU vitamin D3/d) or placebo group (PG) in a double-blind manner. During the intervention period of 28 days, both groups additionally received treatment as usual. BDI-II scores were assessed as primary outcome, DISYPS-II (Diagnostic System for Mental Disorders in Childhood and Adolescence, Self- and Parent Rating) and serum total 25(OH)D were secondary outcomes. Results At admission, 49.3% of the screened patients (n = 280) had vitamin D deficiency. Although the intervention led to a higher increase of 25(OH)D levels in the VG than in the PG (treatment difference: + 14 ng/ml; 95% CI 4.86–23.77; p = 0.003), the change in BDI-II scores did not differ (+ 1.3; 95% CI − 2.22 to 4.81; p = 0.466). In contrast, DISYPS parental ratings revealed pronounced improvements of depressive symptoms in the VG (− 0.68; 95% CI − 1.23 to − 0.13; p = 0.016). Conclusion Whereas this study failed to show a vitamin D supplementation effect on self-rated depression in adolescent in- or daycare patients, parents reported less depressive symptoms in VG at the end of our study. Future trials should consider clinician-rated depressive symptoms as primary outcome. Trial registration “German Clinical Trials Register” (https://www.drks.de), registration number: DRKS00009758 }}, author = {{Libuda, Lars and Timmesfeld, Nina and Antel, Jochen and Hirtz, Raphael and Bauer, Jens and Führer, Dagmar and Zwanziger, Denise and Öztürk, Dana and Langenbach, Gina and Hahn, Denise and Ring, Stefanie and Peters, Triinu and Hinney, Anke and Bühlmeier, Judith and Hebebrand, Johannes and Grasemann, Corinna and Föcker, Manuel}}, issn = {{1436-6207}}, journal = {{European Journal of Nutrition}}, pages = {{3415--3424}}, title = {{{Effect of vitamin D deficiency on depressive symptoms in child and adolescent psychiatric patients: results of a randomized controlled trial}}}, doi = {{10.1007/s00394-020-02176-6}}, year = {{2020}}, } @article{27018, abstract = {{Acute anorexia nervosa (AN) constitutes an extreme physiological state. We aimed to detect state related metabolic alterations during inpatient admission and upon short- and long-term weight regain. In addition, we tested the hypothesis that metabolite concentrations adapt to those of healthy controls (HC) after long-term weight regain. Thirty-five female adolescents with AN and 25 female HC were recruited. Based on a targeted approach 187 metabolite concentrations were detected at inpatient admission (T0), after short-term weight recovery (T1; half of target-weight) and close to target weight (T2). Pattern hunter and time course analysis were performed. The highest number of significant differences in metabolite concentrations (N = 32) were observed between HC and T1. According to the detected main pattern, metabolite concentrations at T2 became more similar to those of HC. The course of single metabolite concentrations (e.g., glutamic acid) revealed different metabolic subtypes within the study sample. Patients with AN after short-term weight regain are in a greater “metabolic imbalance” than at starvation. After long-term weight regain, patients reach a metabolite profile similar to HC. Our results might be confounded by different metabolic subtypes of patients with AN.}}, author = {{Föcker, Manuel and Cecil, Alexander and Prehn, Cornelia and Adamski, Jerzy and Albrecht, Muriel and Adams, Frederike and Hinney, Anke and Libuda, Lars and Bühlmeier, Judith and Hebebrand, Johannes and Peters, Triinu and Antel, Jochen}}, issn = {{2218-1989}}, journal = {{Metabolites}}, title = {{{Evaluation of Metabolic Profiles of Patients with Anorexia Nervosa at Inpatient Admission, Short- and Long-Term Weight Regain—Descriptive and Pattern Analysis}}}, doi = {{10.3390/metabo11010007}}, year = {{2020}}, } @article{27021, author = {{Jansen, Kathrin and Tempes, Jana and Drozdowska, Alina and Gutmann, Maike and Falkenstein, Michael and Buyken, Anette and Libuda, Lars and Rudolf, Henrik and Lücke, Thomas and Kersting, Mathilde}}, issn = {{0954-3007}}, journal = {{European Journal of Clinical Nutrition}}, pages = {{757--764}}, title = {{{Short-term effects of carbohydrates differing in glycemic index (GI) consumed at lunch on children’s cognitive function in a randomized crossover study}}}, doi = {{10.1038/s41430-020-0600-0}}, year = {{2020}}, } @inproceedings{36380, author = {{Lee, J.G. and Berger, Thomas and Trenn, S. and Shim, H.}}, booktitle = {{Proceedings of the European Control Confrence 2020}}, location = {{Saint Petersburg, Russia}}, pages = {{911--916}}, title = {{{Utility of Edge-wise Funnel Coupling for Asymptotically Solving Distributed Consensus Optimization}}}, year = {{2020}}, } @inbook{29413, author = {{Flaßkamp, K. and Ober-Blöbaum, Sina and Peitz, S. }}, booktitle = {{Advances in Dynamics, Optimization and Computation}}, editor = {{Junge, Oliver and Schütze, Oliver and Froyland, Gary and Ober-Blöbaum, Sina and Padberg-Gehle, Kathrin}}, pages = {{209--237}}, publisher = {{Springer International Publishing}}, title = {{{Symmetry in optimal control: A multiobjective model predictive control approach}}}, year = {{2020}}, } @inproceedings{46328, abstract = {{In this paper, we rely on previous work proposing a modularized version of CMA-ES, which captures several alterations to the conventional CMA-ES developed in recent years. Each alteration provides significant advantages under certain problem properties, e.g., multi-modality, high conditioning. These distinct advancements are implemented as modules which result in 4608 unique versions of CMA-ES. Previous findings illustrate the competitive advantage of enabling and disabling the aforementioned modules for different optimization problems. Yet, this modular CMA-ES is lacking a method to automatically determine when the activation of specific modules is auspicious and when it is not. We propose a well-performing instance-specific algorithm configuration model which selects an (almost) optimal configuration of modules for a given problem instance. In addition, the structure of this configuration model is able to capture inter-dependencies between modules, e.g., two (or more) modules might only be advantageous in unison for some problem types, making the orchestration of modules a crucial task. This is accomplished by chaining multiple random forest classifiers together into a so-called Classifier Chain based on a set of numerical features extracted by means of Exploratory Landscape Analysis (ELA) to describe the given problem instances.}}, author = {{Prager, Raphael Patrick and Trautmann, Heike and Wang, Hao and Bäck, Thomas H. W. and Kerschke, Pascal}}, booktitle = {{Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)}}, pages = {{996–1003}}, title = {{{Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis}}}, doi = {{10.1109/SSCI47803.2020.9308510}}, year = {{2020}}, } @inproceedings{46326, abstract = {{Machine learning has become one of the most important tools in data analysis. However, selecting the most appropriate machine learning algorithm and tuning its hyperparameters to their optimal values remains a difficult task. This is even more difficult for streaming applications where automated approaches are often not available to help during algorithm selection and configuration. This paper proposes the first approach for automated algorithm selection and configuration of stream clustering algorithms. We train an ensemble of different stream clustering algorithms and configurations in parallel and use the best performing configuration to obtain a clustering solution. By drawing new configurations from better performing ones, we are able to improve the ensemble performance over time. In large experiments on real and artificial data we show how our ensemble approach can improve upon default configurations and can also compete with a-posteriori algorithm configuration. Our approach is considerably faster than a-posteriori approaches and applicable in real-time. In addition, it is not limited to stream clustering and can be generalised to all streaming applications, including stream classification and regression.}}, author = {{Carnein, Matthias and Trautmann, Heike and Bifet, Albert and Pfahringer, Bernhard}}, booktitle = {{Proceedings of the 14$^th$ Learning and Intelligent Optimization Conference (LION 2020)}}, pages = {{80–95}}, title = {{{confStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms}}}, doi = {{10.1007/978-3-030-53552-0_10}}, year = {{2020}}, } @inproceedings{46332, abstract = {{Multimodality is one of the biggest difficulties for optimization as local optima are often preventing algorithms from making progress. This does not only challenge local strategies that can get stuck. It also hinders meta-heuristics like evolutionary algorithms in convergence to the global optimum. In this paper we present a new concept of gradient descent, which is able to escape local traps. It relies on multiobjectivization of the original problem and applies the recently proposed and here slightly modified multi-objective local search mechanism MOGSA. We use a sophisticated visualization technique for multi-objective problems to prove the working principle of our idea. As such, this work highlights the transfer of new insights from the multi-objective to the single-objective domain and provides first visual evidence that multiobjectivization can link single-objective local optima in multimodal landscapes.}}, author = {{Steinhoff, Vera and Kerschke, Pascal and Aspar, Pelin and Trautmann, Heike and Grimme, Christian}}, booktitle = {{Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI)}}, pages = {{2445–2452}}, title = {{{Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent}}}, doi = {{10.1109/SSCI47803.2020.9308259}}, year = {{2020}}, } @article{21369, abstract = {{Successful design of human-in-the-loop control sys- tems requires appropriate models for human decision makers. Whilst most paradigms adopted in the control systems literature hide the (limited) decision capability of humans, in behavioral economics individual decision making and optimization processes are well-known to be affected by perceptual and behavioral biases. Our goal is to enrich control engineering with some insights from behavioral economics research through exposing such biases in control-relevant settings. This paper addresses the following two key questions: 1) How do behavioral biases affect decision making? 2) What is the role played by feedback in human-in-the-loop control systems? Our experimental framework shows how individuals behave when faced with the task of piloting an UAV under risk and uncertainty, paralleling a real-world decision-making scenario. Our findings support the notion of humans in Cyberphysical Systems underlying behavioral biases regardless of – or even because of – receiving immediate outcome feedback. We observe substantial shares of drone controllers to act inefficiently through either flying excessively (overconfident) or overly conservatively (underconfident). Furthermore, we observe human-controllers to self-servingly misinterpret random sequences through being subject to a “hot hand fallacy”. We advise control engineers to mind the human component in order not to compromise technological accomplishments through human issues.}}, author = {{Protte, Marius and Fahr, René and Quevedo, Daniel E.}}, journal = {{IEEE Control Systems Magazine}}, number = {{6}}, pages = {{57 -- 76}}, publisher = {{IEEE}}, title = {{{Behavioral Economics for Human-in-the-loop Control Systems Design: Overconfidence and the hot hand fallacy}}}, doi = {{10.1109/MCS.2020.3019723}}, volume = {{40}}, year = {{2020}}, }