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Schön, Oliver, et al. “Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems.”https://doi.org/10.1016/j.ifacol.2022.07.282__14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022), vol. 55, no. 12, 2022, pp. 19–24, doi:https://doi.org/10.1016/j.ifacol.2022.07.282.
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Philipo, Godiana Hagile, et al. “Neural Network-Based Demand-Side Management in a Stand-Alone Solar PV-Battery Microgrid Using Load-Shifting and Peak-Clipping.”10.3390/en15145215__Energies, vol. 15, no. 14, 5215, MDPI AG, 2022, doi:10.3390/en15145215.
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Grimme, Christian, et al. “Peeking beyond Peaks: Challenges and Research Potentials of Continuous Multimodal Multi-Objective Optimization.”https://doi.org/10.1016/j.cor.2021.105489__Computers &; Operations Research, vol. 136, 2021, p. 105489, doi:https://doi.org/10.1016/j.cor.2021.105489.
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Bossek, Jakob, et al. “Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms.”10.1145/3449639.3459364__Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, 2021, pp. 556–564, doi:10.1145/3449639.3459364.
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Bossek, Jakob, and Frank Neumann. “Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem.”10.1145/3449639.3459363__Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, 2021, pp. 198–206, doi:10.1145/3449639.3459363.
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Bossek, Jakob, et al. “Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem.”10.1007/s00453-021-00838-3__Algorithmica, vol. 83, no. 10, 2021, pp. 3148–3179, doi:10.1007/s00453-021-00838-3.
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Schneider, Stefan Balthasar, et al. “Self-Driving Network and Service Coordination Using Deep Reinforcement Learning.” IEEE International Conference on Network and Service Management (CNSM), IEEE, 2020.
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Castenow, Jannik, et al. “The Online Multi-Commodity Facility Location Problem.”10.1145/3350755.3400281__Proceedings of the 32nd ACM Symposium on Parallelism in Algorithms and Architectures, 2020, doi:10.1145/3350755.3400281.
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Otroshi, Mortaza, et al. “Stress State Dependent Damage Modeling of Self-Pierce Riveting Process Simulation Using GISSMO Damage Model.”10.1016/j.jajp.2020.100015__Journal of Advanced Joining Processes, vol. 1, Elsevier, 2020, doi:10.1016/j.jajp.2020.100015.
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Caruso, Carina, et al. “Motivierende Lernzugänge als Ausgangspunkt der Professionalisierung angehender Lehrer_innen.”10.4119/hlz-2540__Herausforderung Lehrer*innenbildung - Zeitschrift Zur Konzeption, Gestaltung Und Diskussion, vol. 3, no. 1, 2020, pp. 18–33, doi:10.4119/hlz-2540.
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Bonnard, Bernard, et al. “A Zermelo Navigation Problem with a Vortex Singularity.”10.1051/cocv/2020058__ESAIM: Control, Optimisation and Calculus of Variations, vol. 27, , EDP Sciences, 2020, doi:10.1051/cocv/2020058.
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Bossek, Jakob, et al. “A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms.”https://doi.org/10.1016/j.asoc.2019.105901__Applied Soft Computing, vol. 88, 2020, p. 105901, doi:https://doi.org/10.1016/j.asoc.2019.105901.
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Bossek, Jakob, et al. “More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization.”10.1145/3377930.3390174__Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, 2020, pp. 1277–1285, doi:10.1145/3377930.3390174.
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Bossek, Jakob, et al. “Evolving Sampling Strategies for One-Shot Optimization Tasks.”10.1007/978-3-030-58112-1_8__Parallel Problem Solving from Nature (PPSN XVI), Springer-Verlag, 2020, pp. 111–124, doi:10.1007/978-3-030-58112-1_8.
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Bossek, Jakob, et al. “The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics.”10.1145/3377930.3390243__Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, 2020, pp. 1286–1294, doi:10.1145/3377930.3390243.
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Bossek, Jakob, et al. “Dynamic Bi-Objective Routing of Multiple Vehicles.”10.1145/3377930.3390146__Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, 2020, pp. 166–174, doi:10.1145/3377930.3390146.
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Bossek, Jakob, et al. “Initial Design Strategies and Their Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB.”10.1145/3377930.3390155__Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, 2020, pp. 778–786, doi:10.1145/3377930.3390155.
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Bossek, Jakob, et al. “A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms.”10.1016/j.asoc.2019.105901__Applied Soft Computing, vol. 88, no. C, 2020, doi:10.1016/j.asoc.2019.105901.
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Hippert, Benjamin, et al. “Portfolio Benefits of Adding Corporate Credit Default Swap Indices: Evidence from North America and Europe.”https://doi.org/10.1007/s11147-018-9148-8__Review of Derivatives Research , vol. 22, no. 2, 2019, pp. 203–59, doi:https://doi.org/10.1007/s11147-018-9148-8.
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Bornmann, Peter. Modellierung Und Experimentelle Charakterisierung Der Wechselwirkung Zwischen Ultraschallwandler Und Flüssigkeit in Kavitationsbasierten Prozessen. Shaker, 2019.
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Bossek, Jakob, et al. “Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm.”10.1007/978-3-030-12598-1_41__Evolutionary Multi-Criterion Optimization (EMO), edited by Kalyanmoy Deb et al., Springer International Publishing, 2019, pp. 516–528, doi:10.1007/978-3-030-12598-1_41.
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Bossek, Jakob, et al. “Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators.”10.1145/3299904.3340307__Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, Association for Computing Machinery, 2019, pp. 58–71, doi:10.1145/3299904.3340307.
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Bossek, Jakob, et al. “Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring.”10.1145/3321707.3321792__Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, 2019, pp. 1443–1451, doi:10.1145/3321707.3321792.
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Bossek, Jakob, et al. “On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem.”10.1145/3321707.3321818__Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, 2019, pp. 516–523, doi:10.1145/3321707.3321818.
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Feldmann, Michael, et al. “Self-Stabilizing Supervised Publish-Subscribe Systems.”10.1109/IPDPS.2018.00114__Proceedings of the 32nd IEEE International Parallel &; Distributed Processing Symposium (IPDPS), IEEE, 2018, doi:10.1109/IPDPS.2018.00114.
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Rauchecker, Gerhard, and Guido Schryen. “Decision Support for the Optimal Coordination of Spontaneous Volunteers in Disaster Relief.” Proceedings of the 15th International Conference on Information Systems for Crisis Response and Management, 2018.
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Knollmann, Till, and Christian Scheideler. “A Self-Stabilizing Hashed Patricia Trie.”10.1007/978-3-030-03232-6_1__Proceedings of the 20th International Symposium on Stabilization, Safety, and Security of Distributed Systems (SSS), edited by Taisuke Izumi and Petr Kuznetsov, vol. 11201, Springer, Cham, 2018, doi:10.1007/978-3-030-03232-6_1.
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Unger, Andreas, et al. “Intelligent Production of Wire Bonds Using Multi-Objective Optimization – Insights, Opportunities and Challenges.”10.4071/2380-4505-2018.1.000572__In Proceedings of IMAPS 2018 – 51st Symposium on Microelectronics, Pasadena, CA, 2018, vol. Vol. 2018, No. 1, pp. 000572-000577., 2018, doi:10.4071/2380-4505-2018.1.000572.
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Bertau, Marie-Cécile, and Andrea Karsten. “Reconsidering Interiorization: Self Moving across Language Spacetimes.”10.1016/j.newideapsych.2017.12.001__New Ideas in Psychology, vol. 49, Elsevier BV, 2018, pp. 7–17, doi:10.1016/j.newideapsych.2017.12.001.
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Meyer, Tobias. Optimization-Based Reliability Control of Mechatronic Systems. Shaker, 2018.
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Bossek, Jakob, et al. “Local Search Effects in Bi-Objective Orienteering.”10.1145/3205455.3205548__Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, 2018, pp. 585–592, doi:10.1145/3205455.3205548.
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Bossek, Jakob. “Performance Assessment of Multi-Objective Evolutionary Algorithms with the R Package Ecr.”10.1145/3205651.3208312__Proceedings of the Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, 2018, pp. 1350–1356, doi:10.1145/3205651.3208312.
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Kerschke, Pascal, et al. “Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers.”10.1145/3205651.3208233__Proceedings of the Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, 2018, pp. 1737–1744, doi:10.1145/3205651.3208233.
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Mahnken, Rolf. “‘A Variational Formulation for Fuzzy Analysis in Continuum Mechanics.’” Mathematics and Mechanics of Complex Systems, vol. 5, no. 3–4, 2017.
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Ho, Nam, et al. “Evolvable Caches: Optimization of Reconfigurable Cache Mappings for a LEON3/Linux-Based Multi-Core Processor.”10.1109/FPT.2017.8280144__2017 International Conference on Field Programmable Technology (ICFPT), 2017, pp. 215–18, doi:10.1109/FPT.2017.8280144.
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Bossek, Jakob. “Ecr 2.0: A Modular Framework for Evolutionary Computation in R.”10.1145/3067695.3082470__Proceedings of the Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, 2017, pp. 1187–1193, doi:10.1145/3067695.3082470.
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Bossek, Jakob, and Christian Grimme. “A Pareto-Beneficial Sub-Tree Mutation for the Multi-Criteria Minimum Spanning Tree Problem.”10.1109/SSCI.2017.8285183__2017 IEEE Symposium Series on Computational Intelligence (SSCI), 2017, pp. 1–8, doi:10.1109/SSCI.2017.8285183.
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Meyer , Tobias, et al. “Reliable Manufacturing of Heavy Copper Wire Bonds Using Online Parameter Adaptation.”10.1109/ECTC.2016.215__IEEE 66th Electronic Components and Technology Conference, 2016, pp. 622–28, doi:10.1109/ECTC.2016.215.
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Bossek, Jakob, and Heike Trautmann. “Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference.”10.1007/978-3-319-49130-1_1__Proceedings of the XV International Conference of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037, Springer-Verlag, 2016, pp. 3–12, doi:10.1007/978-3-319-49130-1_1.
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Meyer, Tobias, et al. “Advantages of Reliability-Adaptive System Operation for Maintenance Planning.”10.1016/j.ifacol.2015.09.647__Proceedings of the 9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, 2015, pp. 940–45, doi:10.1016/j.ifacol.2015.09.647.
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Ho, Nam, et al. “Microarchitectural Optimization by Means of Reconfigurable and Evolvable Cache Mappings.”10.1109/AHS.2015.7231178__Proc. NASA/ESA Conf. Adaptive Hardware and Systems (AHS), 2015, pp. 1–7, doi:10.1109/AHS.2015.7231178.
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Bossek, Jakob, et al. “Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement.”10.1145/2739480.2754673__Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, 2015, pp. 1319–1326, doi:10.1145/2739480.2754673.
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Meisel, Stephan, et al. “Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle.”10.1145/2739480.2754705__Proceedings of the Genetic and Evolutionary Computation Conference , Association for Computing Machinery, 2015, pp. 425–432, doi:10.1145/2739480.2754705.
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Uribe, David Oliva, et al. “Development of a Biomedical Tissue Differentiation System Using Piezoelectric Actuators.”10.1109/FREQ.2008.4622963__Frequency Control Symposium, 2008 IEEE International, 2008, pp. 91–94, doi:10.1109/FREQ.2008.4622963.
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