[{"type":"conference","publication":"2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)","abstract":[{"text":"This paper deals with the development and results of a prediction framework for traffic light control systems as well as the usage and benefits of such predictions in green light optimal speed advisory (GLOSA) scenarios.\r\nVarious machine learning methods like support vector machines, neural networks or reinforcement learning were evaluated for their applicability in the prediction context and compared based on their efficiency and most importantly accuracy. The resulting prediction framework uses decision tree ensemble models combined with certain model knowledge to forecast different control strategies. This method was chosen due to its best performance in various test scenarios. Very high accuracy and fidelity were achieved for standard control methods like fixed-time, time-of-day-based and 'ordinary' traffic-based programs. Only for the more sophisticated model predictive control which was tested lower accuracies were achieved.\r\nFor the upcoming GLOSA application the penetration of equipped vehicles was varied for different traffic scenarios and control strategies. Results showcase high potentials for enhancing urban mobility and reducing environmental impact by lower emissions and waiting times. However, it is also clear from the studies presented in this contribution that the coordination of the control strategy with the GLOSA vehicles is of enormous importance.","lang":"eng"}],"status":"public","_id":"61492","user_id":"36303","department":[{"_id":"153"}],"keyword":["ML","Prediction","Tree Ensembles","GLOSA"],"language":[{"iso":"eng"}],"publication_status":"accepted","quality_controlled":"1","year":"2026","citation":{"apa":"Malena, K., Link, C., Gausemeier, S., &#38; Trächtler, A. (n.d.). ML-based Prediction Framework for varying Traffic Signal Control Strategies and its GLOSA-application. <i>2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)</i>, <i>28</i>.","mla":"Malena, Kevin, et al. “ML-Based Prediction Framework for Varying Traffic Signal Control Strategies and Its GLOSA-Application.” <i>2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)</i>, vol. 28, IEEE.","short":"K. Malena, C. Link, S. Gausemeier, A. Trächtler, in: 2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), IEEE, n.d.","bibtex":"@inproceedings{Malena_Link_Gausemeier_Trächtler, title={ML-based Prediction Framework for varying Traffic Signal Control Strategies and its GLOSA-application}, volume={28}, booktitle={2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)}, publisher={IEEE}, author={Malena, Kevin and Link, Christopher and Gausemeier, Sandra and Trächtler, Ansgar} }","chicago":"Malena, Kevin, Christopher Link, Sandra Gausemeier, and Ansgar Trächtler. “ML-Based Prediction Framework for Varying Traffic Signal Control Strategies and Its GLOSA-Application.” In <i>2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)</i>, Vol. 28. IEEE, n.d.","ieee":"K. Malena, C. Link, S. Gausemeier, and A. Trächtler, “ML-based Prediction Framework for varying Traffic Signal Control Strategies and its GLOSA-application,” in <i>2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)</i>, Gold Coast (Australia), vol. 28.","ama":"Malena K, Link C, Gausemeier S, Trächtler A. ML-based Prediction Framework for varying Traffic Signal Control Strategies and its GLOSA-application. In: <i>2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)</i>. Vol 28. IEEE."},"intvolume":"        28","date_updated":"2026-01-26T08:50:37Z","publisher":"IEEE","date_created":"2025-10-01T11:20:34Z","author":[{"first_name":"Kevin","id":"36303","full_name":"Malena, Kevin","last_name":"Malena","orcid":"0000-0003-1183-4679"},{"last_name":"Link","full_name":"Link, Christopher","id":"38249","first_name":"Christopher"},{"first_name":"Sandra","full_name":"Gausemeier, Sandra","id":"17793","last_name":"Gausemeier"},{"first_name":"Ansgar","last_name":"Trächtler","id":"552","full_name":"Trächtler, Ansgar"}],"volume":28,"title":"ML-based Prediction Framework for varying Traffic Signal Control Strategies and its GLOSA-application","conference":{"end_date":"2025-11-21","location":"Gold Coast (Australia)","name":"28th International Conference on Intelligent Transportation Systems (ITSC)","start_date":"2025-11-18"}},{"year":"2025","title":"Tree-Based OWL Class Expression Learner over Large Graphs","date_created":"2025-11-28T14:09:17Z","publisher":"Springer Nature Switzerland","abstract":[{"lang":"eng","text":"Learning  continuous  vector  representations  for  knowledge graphs has significantly improved state-of-the-art performances in many challenging tasks. Yet, deep-learning-based models are only post-hoc and locally explainable. In contrast, learning Web Ontology Language (OWL) class  expressions  in  Description  Logics  (DLs)  is  ante-hoc  and  globally explainable. However, state-of-the-art learners have two well-known lim-itations:  scaling  to  large  knowledge  graphs  and  handling  missing  infor-mation.  Here,  we  present  a  decision-tree-based  learner  (tDL)  to  learn Web  Ontology  Languages  (OWLs)  class  expressions  over  large  knowl-edge graphs, while imputing missing triples. Given positive and negative example individuals, tDL  firstly constructs unique OWL expressions in .SHOIN from  concise  bounded  descriptions  of  individuals.  Each  OWL class expression is used as a feature in a binary classification problem to represent input individuals. Thereafter, tDL  fits a CART decision tree to learn Boolean decision rules distinguishing positive examples from nega-tive examples. A final OWL expression in.SHOIN is built by traversing the  built  CART  decision  tree  from  the  root  node  to  leaf  nodes  for  each positive example. By this, tDL  can learn OWL class expressions without exploration, i.e., the number of queries to a knowledge graph is bounded by the number of input individuals. Our empirical results show that tDL outperforms  the  current state-of-the-art  models  across datasets. Impor-tantly, our experiments over a large knowledge graph (DBpedia with 1.1 billion triples) show that tDL  can effectively learn accurate OWL class expressions,  while  the  state-of-the-art  models  fail  to  return  any  results. Finally,  expressions  learned  by  tDL  can  be  seamlessly  translated  into natural language explanations using a pre-trained large language model and a DL verbalizer."}],"publication":"Lecture Notes in Computer Science","language":[{"iso":"eng"}],"keyword":["Decision Tree","OWL Class Expression Learning","Description Logic","Knowledge Graph","Large Language Model","Verbalizer"],"citation":{"ieee":"C. Demir, M. Yekini, M. Röder, Y. Mahmood, and A.-C. Ngonga Ngomo, “Tree-Based OWL Class Expression Learner over Large Graphs,” in <i>Lecture Notes in Computer Science</i>, Cham: Springer Nature Switzerland, 2025.","chicago":"Demir, Caglar, Moshood Yekini, Michael Röder, Yasir Mahmood, and Axel-Cyrille Ngonga Ngomo. “Tree-Based OWL Class Expression Learner over Large Graphs.” In <i>Lecture Notes in Computer Science</i>. Cham: Springer Nature Switzerland, 2025. <a href=\"https://doi.org/10.1007/978-3-032-06066-2_29\">https://doi.org/10.1007/978-3-032-06066-2_29</a>.","ama":"Demir C, Yekini M, Röder M, Mahmood Y, Ngonga Ngomo A-C. Tree-Based OWL Class Expression Learner over Large Graphs. In: <i>Lecture Notes in Computer Science</i>. Springer Nature Switzerland; 2025. doi:<a href=\"https://doi.org/10.1007/978-3-032-06066-2_29\">10.1007/978-3-032-06066-2_29</a>","mla":"Demir, Caglar, et al. “Tree-Based OWL Class Expression Learner over Large Graphs.” <i>Lecture Notes in Computer Science</i>, Springer Nature Switzerland, 2025, doi:<a href=\"https://doi.org/10.1007/978-3-032-06066-2_29\">10.1007/978-3-032-06066-2_29</a>.","bibtex":"@inbook{Demir_Yekini_Röder_Mahmood_Ngonga Ngomo_2025, place={Cham}, title={Tree-Based OWL Class Expression Learner over Large Graphs}, DOI={<a href=\"https://doi.org/10.1007/978-3-032-06066-2_29\">10.1007/978-3-032-06066-2_29</a>}, booktitle={Lecture Notes in Computer Science}, publisher={Springer Nature Switzerland}, author={Demir, Caglar and Yekini, Moshood and Röder, Michael and Mahmood, Yasir and Ngonga Ngomo, Axel-Cyrille}, year={2025} }","short":"C. Demir, M. Yekini, M. Röder, Y. Mahmood, A.-C. Ngonga Ngomo, in: Lecture Notes in Computer Science, Springer Nature Switzerland, Cham, 2025.","apa":"Demir, C., Yekini, M., Röder, M., Mahmood, Y., &#38; Ngonga Ngomo, A.-C. (2025). Tree-Based OWL Class Expression Learner over Large Graphs. In <i>Lecture Notes in Computer Science</i>. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECML PKDD, Porto, Portugal. Springer Nature Switzerland. <a href=\"https://doi.org/10.1007/978-3-032-06066-2_29\">https://doi.org/10.1007/978-3-032-06066-2_29</a>"},"place":"Cham","publication_status":"published","publication_identifier":{"isbn":["9783032060655","9783032060662"],"issn":["0302-9743","1611-3349"]},"doi":"10.1007/978-3-032-06066-2_29","conference":{"start_date":"2025-09-15","name":"European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECML PKDD","location":"Porto, Portugal","end_date":"2025-09-19"},"author":[{"first_name":"Caglar","full_name":"Demir, Caglar","last_name":"Demir"},{"full_name":"Yekini, Moshood","last_name":"Yekini","first_name":"Moshood"},{"last_name":"Röder","full_name":"Röder, Michael","first_name":"Michael"},{"first_name":"Yasir","last_name":"Mahmood","full_name":"Mahmood, Yasir"},{"last_name":"Ngonga Ngomo","full_name":"Ngonga Ngomo, Axel-Cyrille","first_name":"Axel-Cyrille"}],"date_updated":"2025-11-28T14:57:39Z","status":"public","type":"book_chapter","user_id":"114533","department":[{"_id":"34"},{"_id":"574"}],"project":[{"_id":"285","name":"SAIL - Nachhaltiger Lebenszyklus von intelligenten soziotechnischen Systemen"}],"_id":"62701"},{"conference":{"start_date":"2022-04-25","name":"IEEE/IFIP Network Operations and Management Symposium (NOMS)","location":"Budapest","end_date":"2022-04-29"},"author":[{"first_name":"Stefan","full_name":"Werner, Stefan","last_name":"Werner"},{"full_name":"Schneider, Stefan Balthasar","id":"35343","orcid":"0000-0001-8210-4011","last_name":"Schneider","first_name":"Stefan Balthasar"},{"first_name":"Holger","id":"126","full_name":"Karl, Holger","last_name":"Karl"}],"date_updated":"2022-01-11T08:44:04Z","oa":"1","citation":{"ama":"Werner S, Schneider SB, Karl H. Use What You Know: Network and Service Coordination Beyond Certainty. In: <i>IEEE/IFIP Network Operations and Management Symposium (NOMS)</i>. IEEE; 2022.","chicago":"Werner, Stefan, Stefan Balthasar Schneider, and Holger Karl. “Use What You Know: Network and Service Coordination Beyond Certainty.” In <i>IEEE/IFIP Network Operations and Management Symposium (NOMS)</i>. IEEE, 2022.","ieee":"S. Werner, S. B. Schneider, and H. Karl, “Use What You Know: Network and Service Coordination Beyond Certainty,” presented at the IEEE/IFIP Network Operations and Management Symposium (NOMS), Budapest, 2022.","bibtex":"@inproceedings{Werner_Schneider_Karl_2022, title={Use What You Know: Network and Service Coordination Beyond Certainty}, booktitle={IEEE/IFIP Network Operations and Management Symposium (NOMS)}, publisher={IEEE}, author={Werner, Stefan and Schneider, Stefan Balthasar and Karl, Holger}, year={2022} }","short":"S. Werner, S.B. Schneider, H. Karl, in: IEEE/IFIP Network Operations and Management Symposium (NOMS), IEEE, 2022.","mla":"Werner, Stefan, et al. “Use What You Know: Network and Service Coordination Beyond Certainty.” <i>IEEE/IFIP Network Operations and Management Symposium (NOMS)</i>, IEEE, 2022.","apa":"Werner, S., Schneider, S. B., &#38; Karl, H. (2022). Use What You Know: Network and Service Coordination Beyond Certainty. <i>IEEE/IFIP Network Operations and Management Symposium (NOMS)</i>. IEEE/IFIP Network Operations and Management Symposium (NOMS), Budapest."},"has_accepted_license":"1","file_date_updated":"2022-01-11T08:39:57Z","user_id":"35343","department":[{"_id":"75"}],"project":[{"_id":"1","name":"SFB 901: SFB 901"},{"name":"SFB 901 - C: SFB 901 - Project Area C","_id":"4"},{"name":"SFB 901 - C4: SFB 901 - Subproject C4","_id":"16"}],"_id":"29220","status":"public","type":"conference","title":"Use What You Know: Network and Service Coordination Beyond Certainty","date_created":"2022-01-11T08:43:26Z","publisher":"IEEE","year":"2022","quality_controlled":"1","language":[{"iso":"eng"}],"ddc":["004"],"keyword":["network management","service management","AI","Monte Carlo Tree Search","model-based","QoS"],"file":[{"relation":"main_file","content_type":"application/pdf","access_level":"open_access","file_id":"29222","file_name":"author_version.pdf","file_size":528653,"date_created":"2022-01-11T08:39:57Z","creator":"stschn","date_updated":"2022-01-11T08:39:57Z"}],"abstract":[{"text":"Modern services often comprise several components, such as chained virtual network functions, microservices, or\r\nmachine learning functions. Providing such services requires to decide how often to instantiate each component, where to place these instances in the network, how to chain them and route traffic through them. \r\nTo overcome limitations of conventional, hardwired heuristics, deep reinforcement learning (DRL) approaches for self-learning network and service management have emerged recently. These model-free DRL approaches are more flexible but typically learn tabula rasa, i.e., disregard existing understanding of networks, services, and their coordination. \r\n\r\nInstead, we propose FutureCoord, a novel model-based AI approach that leverages existing understanding of networks and services for more efficient and effective coordination without time-intensive training. FutureCoord combines Monte Carlo Tree Search with a stochastic traffic model. This allows FutureCoord to estimate the impact of future incoming traffic and effectively optimize long-term effects, taking fluctuating demand and Quality of Service (QoS) requirements into account. Our extensive evaluation based on real-world network topologies, services, and traffic traces indicates that FutureCoord clearly outperforms state-of-the-art model-free and model-based approaches with up to 51% higher flow success ratios.","lang":"eng"}],"publication":"IEEE/IFIP Network Operations and Management Symposium (NOMS)"},{"title":"Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem","doi":"10.1145/3449639.3459363","publisher":"Association for Computing Machinery","date_updated":"2023-12-13T10:45:37Z","date_created":"2023-11-14T15:58:55Z","author":[{"first_name":"Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","full_name":"Bossek, Jakob","id":"102979"},{"first_name":"Frank","last_name":"Neumann","full_name":"Neumann, Frank"}],"place":"New York, NY, USA","year":"2021","page":"198–206","citation":{"chicago":"Bossek, Jakob, and Frank Neumann. “Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 198–206. GECCO ’21. New York, NY, USA: Association for Computing Machinery, 2021. <a href=\"https://doi.org/10.1145/3449639.3459363\">https://doi.org/10.1145/3449639.3459363</a>.","ieee":"J. Bossek and F. Neumann, “Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2021, pp. 198–206, doi: <a href=\"https://doi.org/10.1145/3449639.3459363\">10.1145/3449639.3459363</a>.","ama":"Bossek J, Neumann F. Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO ’21. Association for Computing Machinery; 2021:198–206. doi:<a href=\"https://doi.org/10.1145/3449639.3459363\">10.1145/3449639.3459363</a>","apa":"Bossek, J., &#38; Neumann, F. (2021). Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 198–206. <a href=\"https://doi.org/10.1145/3449639.3459363\">https://doi.org/10.1145/3449639.3459363</a>","bibtex":"@inproceedings{Bossek_Neumann_2021, place={New York, NY, USA}, series={GECCO ’21}, title={Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem}, DOI={<a href=\"https://doi.org/10.1145/3449639.3459363\">10.1145/3449639.3459363</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Neumann, Frank}, year={2021}, pages={198–206}, collection={GECCO ’21} }","short":"J. Bossek, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2021, pp. 198–206.","mla":"Bossek, Jakob, and Frank Neumann. “Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2021, pp. 198–206, doi:<a href=\"https://doi.org/10.1145/3449639.3459363\">10.1145/3449639.3459363</a>."},"publication_identifier":{"isbn":["978-1-4503-8350-9"]},"publication_status":"published","keyword":["evolutionary algorithms","evolutionary diversity optimization","minimum spanning tree","runtime analysis"],"language":[{"iso":"eng"}],"extern":"1","_id":"48860","department":[{"_id":"819"}],"series_title":"GECCO ’21","user_id":"102979","abstract":[{"lang":"eng","text":"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."}],"status":"public","publication":"Proceedings of the Genetic and Evolutionary Computation Conference","type":"conference"},{"extern":"1","language":[{"iso":"eng"}],"keyword":["biased mutation","evolutionary algorithms","minimum spanning tree problem","runtime analysis"],"user_id":"102979","series_title":"{GECCO} ’20","department":[{"_id":"819"}],"_id":"48895","status":"public","abstract":[{"lang":"eng","text":"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."}],"type":"conference","publication":"Proceedings of the 2020 Genetic and Evolutionary Computation Conference","doi":"10.1145/3377930.3390168","title":"Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem","author":[{"full_name":"Roostapour, Vahid","last_name":"Roostapour","first_name":"Vahid"},{"first_name":"Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","full_name":"Bossek, Jakob","id":"102979"},{"full_name":"Neumann, Frank","last_name":"Neumann","first_name":"Frank"}],"date_created":"2023-11-14T15:59:00Z","publisher":"Association for Computing Machinery","date_updated":"2023-12-13T10:49:38Z","citation":{"ama":"Roostapour V, Bossek J, Neumann F. Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem. In: <i>Proceedings of the 2020 Genetic and Evolutionary Computation Conference</i>. {GECCO} ’20. Association for Computing Machinery; 2020:551–559. doi:<a href=\"https://doi.org/10.1145/3377930.3390168\">10.1145/3377930.3390168</a>","chicago":"Roostapour, Vahid, Jakob Bossek, and Frank Neumann. “Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem.” In <i>Proceedings of the 2020 Genetic and Evolutionary Computation Conference</i>, 551–559. {GECCO} ’20. New York, NY, USA: Association for Computing Machinery, 2020. <a href=\"https://doi.org/10.1145/3377930.3390168\">https://doi.org/10.1145/3377930.3390168</a>.","ieee":"V. Roostapour, J. Bossek, and F. Neumann, “Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem,” in <i>Proceedings of the 2020 Genetic and Evolutionary Computation Conference</i>, 2020, pp. 551–559, doi: <a href=\"https://doi.org/10.1145/3377930.3390168\">10.1145/3377930.3390168</a>.","bibtex":"@inproceedings{Roostapour_Bossek_Neumann_2020, place={New York, NY, USA}, series={{GECCO} ’20}, title={Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem}, DOI={<a href=\"https://doi.org/10.1145/3377930.3390168\">10.1145/3377930.3390168</a>}, booktitle={Proceedings of the 2020 Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Roostapour, Vahid and Bossek, Jakob and Neumann, Frank}, year={2020}, pages={551–559}, collection={{GECCO} ’20} }","short":"V. Roostapour, J. Bossek, F. Neumann, in: Proceedings of the 2020 Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2020, pp. 551–559.","mla":"Roostapour, Vahid, et al. “Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem.” <i>Proceedings of the 2020 Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2020, pp. 551–559, doi:<a href=\"https://doi.org/10.1145/3377930.3390168\">10.1145/3377930.3390168</a>.","apa":"Roostapour, V., Bossek, J., &#38; Neumann, F. (2020). Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem. <i>Proceedings of the 2020 Genetic and Evolutionary Computation Conference</i>, 551–559. <a href=\"https://doi.org/10.1145/3377930.3390168\">https://doi.org/10.1145/3377930.3390168</a>"},"page":"551–559","place":"New York, NY, USA","year":"2020","publication_identifier":{"isbn":["978-1-4503-7128-5"]}},{"year":"2019","title":"On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem","date_created":"2023-11-14T15:58:52Z","publisher":"Association for Computing Machinery","abstract":[{"lang":"eng","text":"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."}],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference","language":[{"iso":"eng"}],"keyword":["biased mutation","combinatorial optimization","minimum spanning tree","multi-objective optimization"],"page":"516–523","citation":{"chicago":"Bossek, Jakob, Christian Grimme, and Frank Neumann. “On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 516–523. GECCO ’19. New York, NY, USA: Association for Computing Machinery, 2019. <a href=\"https://doi.org/10.1145/3321707.3321818\">https://doi.org/10.1145/3321707.3321818</a>.","ieee":"J. Bossek, C. Grimme, and F. Neumann, “On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2019, pp. 516–523, doi: <a href=\"https://doi.org/10.1145/3321707.3321818\">10.1145/3321707.3321818</a>.","ama":"Bossek J, Grimme C, Neumann F. On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO ’19. Association for Computing Machinery; 2019:516–523. doi:<a href=\"https://doi.org/10.1145/3321707.3321818\">10.1145/3321707.3321818</a>","apa":"Bossek, J., Grimme, C., &#38; Neumann, F. (2019). On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 516–523. <a href=\"https://doi.org/10.1145/3321707.3321818\">https://doi.org/10.1145/3321707.3321818</a>","bibtex":"@inproceedings{Bossek_Grimme_Neumann_2019, place={New York, NY, USA}, series={GECCO ’19}, title={On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem}, DOI={<a href=\"https://doi.org/10.1145/3321707.3321818\">10.1145/3321707.3321818</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Grimme, Christian and Neumann, Frank}, year={2019}, pages={516–523}, collection={GECCO ’19} }","short":"J. Bossek, C. Grimme, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2019, pp. 516–523.","mla":"Bossek, Jakob, et al. “On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2019, pp. 516–523, doi:<a href=\"https://doi.org/10.1145/3321707.3321818\">10.1145/3321707.3321818</a>."},"place":"New York, NY, USA","publication_identifier":{"isbn":["978-1-4503-6111-8"]},"publication_status":"published","doi":"10.1145/3321707.3321818","author":[{"orcid":"0000-0002-4121-4668","last_name":"Bossek","id":"102979","full_name":"Bossek, Jakob","first_name":"Jakob"},{"last_name":"Grimme","full_name":"Grimme, Christian","first_name":"Christian"},{"first_name":"Frank","full_name":"Neumann, Frank","last_name":"Neumann"}],"date_updated":"2023-12-13T10:42:24Z","status":"public","type":"conference","extern":"1","department":[{"_id":"819"}],"series_title":"GECCO ’19","user_id":"102979","_id":"48840"},{"user_id":"83983","department":[{"_id":"63"},{"_id":"541"}],"_id":"17651","extern":"1","language":[{"iso":"eng"}],"keyword":["flow","Red-Blue Set Cover","Positive-Negative Partial Set Cover","approximation","tree","MAX SNP-hard","root","leaf","dynamic programming","FPT"],"type":"conference","publication":"Combinatorial Optimization and Applications","status":"public","abstract":[{"lang":"eng","text":"Consider mitigating the effects of denial of service or of malicious traffic in networks by deleting edges. Edge deletion reduces the DoS or the number of the malicious flows, but it also inadvertently removes some of the desired flows. To model this important problem, we formulate two problems: (1) remove all the undesirable flows while minimizing the damage to the desirable ones and (2) balance removing the undesirable flows and not removing too many of the desirable flows. We prove these problems are equivalent to important theoretical problems, thereby being important not only practically but also theoretically, and very hard to approximate in a general network. We employ reductions to nonetheless approximate the problem and also provide a greedy approximation. When the network is a tree, the problems are still MAX SNP-hard, but we provide a greedy-based 2l-approximation algorithm, where l is the longest desirable flow. We also provide an algorithm, approximating the first and the second problem within {\\$}{\\$}2 {\\backslash}sqrt{\\{} 2{\\backslash}left| E {\\backslash}right| {\\}}{\\$}{\\$}and {\\$}{\\$}2 {\\backslash}sqrt{\\{}2 ({\\backslash}left| E {\\backslash}right| + {\\backslash}left| {\\backslash}text {\\{}undesirable flows{\\}} {\\backslash}right| ){\\}}{\\$}{\\$}, respectively, where E is the set of the edges of the network. We also provide a fixed-parameter tractable (FPT) algorithm. Finally, if the tree has a root such that every flow in the tree flows on the path from the root to a leaf, we solve the problem exactly using dynamic programming."}],"editor":[{"first_name":"Donghyun","last_name":"Kim","full_name":"Kim, Donghyun"},{"first_name":"R. N.","full_name":"Uma, R. N.","last_name":"Uma"},{"last_name":"Zelikovsky","full_name":"Zelikovsky, Alexander","first_name":"Alexander"}],"date_created":"2020-08-06T15:19:36Z","author":[{"first_name":"Gleb","last_name":"Polevoy","id":"83983","full_name":"Polevoy, Gleb"},{"full_name":"Trajanovski, Stojan","last_name":"Trajanovski","first_name":"Stojan"},{"last_name":"Grosso","full_name":"Grosso, Paola","first_name":"Paola"},{"full_name":"de Laat, Cees","last_name":"de Laat","first_name":"Cees"}],"date_updated":"2022-01-06T06:53:16Z","publisher":"Springer International Publishing","title":"Removing Undesirable Flows by Edge Deletion","publication_identifier":{"isbn":["978-3-030-04651-4"]},"citation":{"ama":"Polevoy G, Trajanovski S, Grosso P, de Laat C. Removing Undesirable Flows by Edge Deletion. In: Kim D, Uma RN, Zelikovsky A, eds. <i>Combinatorial Optimization and Applications</i>. Cham: Springer International Publishing; 2018:217-232.","ieee":"G. Polevoy, S. Trajanovski, P. Grosso, and C. de Laat, “Removing Undesirable Flows by Edge Deletion,” in <i>Combinatorial Optimization and Applications</i>, 2018, pp. 217–232.","chicago":"Polevoy, Gleb, Stojan Trajanovski, Paola Grosso, and Cees de Laat. “Removing Undesirable Flows by Edge Deletion.” In <i>Combinatorial Optimization and Applications</i>, edited by Donghyun Kim, R. N. Uma, and Alexander Zelikovsky, 217–32. Cham: Springer International Publishing, 2018.","mla":"Polevoy, Gleb, et al. “Removing Undesirable Flows by Edge Deletion.” <i>Combinatorial Optimization and Applications</i>, edited by Donghyun Kim et al., Springer International Publishing, 2018, pp. 217–32.","short":"G. Polevoy, S. Trajanovski, P. Grosso, C. de Laat, in: D. Kim, R.N. Uma, A. Zelikovsky (Eds.), Combinatorial Optimization and Applications, Springer International Publishing, Cham, 2018, pp. 217–232.","bibtex":"@inproceedings{Polevoy_Trajanovski_Grosso_de Laat_2018, place={Cham}, title={Removing Undesirable Flows by Edge Deletion}, booktitle={Combinatorial Optimization and Applications}, publisher={Springer International Publishing}, author={Polevoy, Gleb and Trajanovski, Stojan and Grosso, Paola and de Laat, Cees}, editor={Kim, Donghyun and Uma, R. N. and Zelikovsky, AlexanderEditors}, year={2018}, pages={217–232} }","apa":"Polevoy, G., Trajanovski, S., Grosso, P., &#38; de Laat, C. (2018). Removing Undesirable Flows by Edge Deletion. In D. Kim, R. N. Uma, &#38; A. Zelikovsky (Eds.), <i>Combinatorial Optimization and Applications</i> (pp. 217–232). Cham: Springer International Publishing."},"page":"217-232","place":"Cham","year":"2018"},{"abstract":[{"text":"Approximate computing has become a very popular design\r\nstrategy that exploits error resilient computations to achieve higher\r\nperformance and energy efficiency. Automated synthesis of approximate\r\ncircuits is performed via functional approximation, in which various\r\nparts of the target circuit are extensively examined with a library\r\nof approximate components/transformations to trade off the functional\r\naccuracy and computational budget (i.e., power). However, as the number\r\nof possible approximate transformations increases, traditional search\r\ntechniques suffer from a combinatorial explosion due to the large\r\nbranching factor. In this work, we present a comprehensive framework\r\nfor automated synthesis of approximate circuits from either structural\r\nor behavioral descriptions. We adapt the Monte Carlo Tree Search\r\n(MCTS), as a stochastic search technique, to deal with the large design\r\nspace exploration, which enables a broader range of potential possible\r\napproximations through lightweight random simulations. The proposed\r\nframework is able to recognize the design Pareto set even with low\r\ncomputational budgets. Experimental results highlight the capabilities of\r\nthe proposed synthesis framework by resulting in up to 61.69% energy\r\nsaving while maintaining the predefined quality constraints.","lang":"eng"}],"status":"public","type":"conference","publication":"26th IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC)","keyword":["Approximate computing","High-level synthesis","Accuracy","Monte-Carlo tree search","Circuit simulation"],"language":[{"iso":"eng"}],"_id":"10598","user_id":"64665","department":[{"_id":"78"}],"year":"2018","citation":{"ama":"Awais M, Ghasemzadeh Mohammadi H, Platzner M. An MCTS-based Framework for Synthesis of Approximate Circuits. In: <i>26th IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC)</i>. ; 2018:219-224. doi:<a href=\"https://doi.org/10.1109/VLSI-SoC.2018.8645026\">10.1109/VLSI-SoC.2018.8645026</a>","ieee":"M. Awais, H. Ghasemzadeh Mohammadi, and M. Platzner, “An MCTS-based Framework for Synthesis of Approximate Circuits,” in <i>26th IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC)</i>, 2018, pp. 219–224.","chicago":"Awais, Muhammad, Hassan Ghasemzadeh Mohammadi, and Marco Platzner. “An MCTS-Based Framework for Synthesis of Approximate Circuits.” In <i>26th IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC)</i>, 219–24, 2018. <a href=\"https://doi.org/10.1109/VLSI-SoC.2018.8645026\">https://doi.org/10.1109/VLSI-SoC.2018.8645026</a>.","apa":"Awais, M., Ghasemzadeh Mohammadi, H., &#38; Platzner, M. (2018). An MCTS-based Framework for Synthesis of Approximate Circuits. In <i>26th IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC)</i> (pp. 219–224). <a href=\"https://doi.org/10.1109/VLSI-SoC.2018.8645026\">https://doi.org/10.1109/VLSI-SoC.2018.8645026</a>","bibtex":"@inproceedings{Awais_Ghasemzadeh Mohammadi_Platzner_2018, title={An MCTS-based Framework for Synthesis of Approximate Circuits}, DOI={<a href=\"https://doi.org/10.1109/VLSI-SoC.2018.8645026\">10.1109/VLSI-SoC.2018.8645026</a>}, booktitle={26th IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC)}, author={Awais, Muhammad and Ghasemzadeh Mohammadi, Hassan and Platzner, Marco}, year={2018}, pages={219–224} }","short":"M. Awais, H. Ghasemzadeh Mohammadi, M. Platzner, in: 26th IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC), 2018, pp. 219–224.","mla":"Awais, Muhammad, et al. “An MCTS-Based Framework for Synthesis of Approximate Circuits.” <i>26th IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC)</i>, 2018, pp. 219–24, doi:<a href=\"https://doi.org/10.1109/VLSI-SoC.2018.8645026\">10.1109/VLSI-SoC.2018.8645026</a>."},"page":"219-224","title":"An MCTS-based Framework for Synthesis of Approximate Circuits","doi":"10.1109/VLSI-SoC.2018.8645026","date_updated":"2022-01-06T06:50:46Z","date_created":"2019-07-10T09:21:38Z","author":[{"last_name":"Awais","orcid":"https://orcid.org/0000-0003-4148-2969","full_name":"Awais, Muhammad","id":"64665","first_name":"Muhammad"},{"last_name":"Ghasemzadeh Mohammadi","full_name":"Ghasemzadeh Mohammadi, Hassan","id":"61186","first_name":"Hassan"},{"last_name":"Platzner","full_name":"Platzner, Marco","id":"398","first_name":"Marco"}]},{"title":"A Systematic Approach to Combined HW/SW System Test","conference":{"location":"Dresden","name":"Design, Automation & Test in Europe Conference & Exhibition (DATE 2010)"},"doi":"10.1109/DATE.2010.5457186","date_updated":"2023-01-17T10:41:25Z","publisher":"IEEE","date_created":"2023-01-17T10:41:15Z","author":[{"first_name":"Alexander","last_name":"Krupp","full_name":"Krupp, Alexander"},{"first_name":"Wolfgang","last_name":"Müller","id":"16243","full_name":"Müller, Wolfgang"}],"place":"Dresden","year":"2010","citation":{"apa":"Krupp, A., &#38; Müller, W. (2010). A Systematic Approach to Combined HW/SW System Test. <i>Proceedings of DATE’10</i>. Design, Automation &#38; Test in Europe Conference &#38; Exhibition (DATE 2010), Dresden. <a href=\"https://doi.org/10.1109/DATE.2010.5457186\">https://doi.org/10.1109/DATE.2010.5457186</a>","mla":"Krupp, Alexander, and Wolfgang Müller. “A Systematic Approach to Combined HW/SW System Test.” <i>Proceedings of DATE’10</i>, IEEE, 2010, doi:<a href=\"https://doi.org/10.1109/DATE.2010.5457186\">10.1109/DATE.2010.5457186</a>.","short":"A. Krupp, W. Müller, in: Proceedings of DATE’10, IEEE, Dresden, 2010.","bibtex":"@inproceedings{Krupp_Müller_2010, place={Dresden}, title={A Systematic Approach to Combined HW/SW System Test}, DOI={<a href=\"https://doi.org/10.1109/DATE.2010.5457186\">10.1109/DATE.2010.5457186</a>}, booktitle={Proceedings of DATE’10}, publisher={IEEE}, author={Krupp, Alexander and Müller, Wolfgang}, year={2010} }","ama":"Krupp A, Müller W. A Systematic Approach to Combined HW/SW System Test. In: <i>Proceedings of DATE’10</i>. IEEE; 2010. doi:<a href=\"https://doi.org/10.1109/DATE.2010.5457186\">10.1109/DATE.2010.5457186</a>","chicago":"Krupp, Alexander, and Wolfgang Müller. “A Systematic Approach to Combined HW/SW System Test.” In <i>Proceedings of DATE’10</i>. Dresden: IEEE, 2010. <a href=\"https://doi.org/10.1109/DATE.2010.5457186\">https://doi.org/10.1109/DATE.2010.5457186</a>.","ieee":"A. Krupp and W. Müller, “A Systematic Approach to Combined HW/SW System Test,” presented at the Design, Automation &#38; Test in Europe Conference &#38; Exhibition (DATE 2010), Dresden, 2010, doi: <a href=\"https://doi.org/10.1109/DATE.2010.5457186\">10.1109/DATE.2010.5457186</a>."},"keyword":["System testing","Automatic testing","Object oriented modeling","Classification tree analysis","Automotive engineering","Mathematical model","Embedded system","Control systems","Electronic equipment testing","Software testing"],"language":[{"iso":"eng"}],"_id":"37037","department":[{"_id":"672"}],"user_id":"5786","abstract":[{"text":"Today we can identify a big gap between requirement specification and the generation of test environments. This article extends the Classification Tree Method for Embedded Systems (CTM/ES) to fill this gap by new concepts for the precise specification of stimuli for operational ranges of continuous control systems. It introduces novel means for continuous acceptance criteria definition and for functional coverage definition.","lang":"eng"}],"status":"public","publication":"Proceedings of DATE’10","type":"conference"},{"language":[{"iso":"eng"}],"keyword":["Classification tree analysis","System testing","Embedded system","Safety","Automatic testing","Automation"],"department":[{"_id":"672"}],"user_id":"5786","_id":"38784","status":"public","abstract":[{"lang":"eng","text":"This article presents the classification tree method for functional verification to close the gap from the specification of a test plan to SystemVerilog (Chandra and Chakrabarty, 2001) test bench generation. Our method supports the systematic development of test configurations and is based on the classification tree method for embedded systems (CTM/ES) (Chakrabarty et al., 2000) extending CTM/ES for random test generation as well as for functional coverage and property specification"}],"publication":"Proceedings of the Design Automation & Test in Europe Conference","type":"conference","doi":"10.1109/DATE.2006.243902","title":"Classification Trees for Functional Coverage and Random Test Generation","author":[{"last_name":"Krupp","full_name":"Krupp, Alexander","first_name":"Alexander"},{"id":"16243","full_name":"Müller, Wolfgang","last_name":"Müller","first_name":"Wolfgang"}],"date_created":"2023-01-24T08:06:09Z","date_updated":"2023-01-24T08:06:14Z","publisher":"IEEE","citation":{"apa":"Krupp, A., &#38; Müller, W. (2006). Classification Trees for Functional Coverage and Random Test Generation. <i>Proceedings of the Design Automation &#38; Test in Europe Conference</i>. <a href=\"https://doi.org/10.1109/DATE.2006.243902\">https://doi.org/10.1109/DATE.2006.243902</a>","bibtex":"@inproceedings{Krupp_Müller_2006, place={Munich, Germany}, title={Classification Trees for Functional Coverage and Random Test Generation}, DOI={<a href=\"https://doi.org/10.1109/DATE.2006.243902\">10.1109/DATE.2006.243902</a>}, booktitle={Proceedings of the Design Automation &#38; Test in Europe Conference}, publisher={IEEE}, author={Krupp, Alexander and Müller, Wolfgang}, year={2006} }","short":"A. Krupp, W. Müller, in: Proceedings of the Design Automation &#38; Test in Europe Conference, IEEE, Munich, Germany, 2006.","mla":"Krupp, Alexander, and Wolfgang Müller. “Classification Trees for Functional Coverage and Random Test Generation.” <i>Proceedings of the Design Automation &#38; Test in Europe Conference</i>, IEEE, 2006, doi:<a href=\"https://doi.org/10.1109/DATE.2006.243902\">10.1109/DATE.2006.243902</a>.","chicago":"Krupp, Alexander, and Wolfgang Müller. “Classification Trees for Functional Coverage and Random Test Generation.” In <i>Proceedings of the Design Automation &#38; Test in Europe Conference</i>. Munich, Germany: IEEE, 2006. <a href=\"https://doi.org/10.1109/DATE.2006.243902\">https://doi.org/10.1109/DATE.2006.243902</a>.","ieee":"A. Krupp and W. Müller, “Classification Trees for Functional Coverage and Random Test Generation,” 2006, doi: <a href=\"https://doi.org/10.1109/DATE.2006.243902\">10.1109/DATE.2006.243902</a>.","ama":"Krupp A, Müller W. Classification Trees for Functional Coverage and Random Test Generation. In: <i>Proceedings of the Design Automation &#38; Test in Europe Conference</i>. IEEE; 2006. doi:<a href=\"https://doi.org/10.1109/DATE.2006.243902\">10.1109/DATE.2006.243902</a>"},"place":"Munich, Germany","year":"2006","publication_identifier":{"isbn":["3-9810801-1-4"]}}]