[{"citation":{"mla":"Bossek, Jakob, et al. “Dynamic Bi-Objective Routing of Multiple Vehicles.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2020, pp. 166–174, doi:<a href=\"https://doi.org/10.1145/3377930.3390146\">10.1145/3377930.3390146</a>.","bibtex":"@inproceedings{Bossek_Grimme_Trautmann_2020, place={New York, NY, USA}, series={GECCO ’20}, title={Dynamic Bi-Objective Routing of Multiple Vehicles}, DOI={<a href=\"https://doi.org/10.1145/3377930.3390146\">10.1145/3377930.3390146</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Grimme, Christian and Trautmann, Heike}, year={2020}, pages={166–174}, collection={GECCO ’20} }","ama":"Bossek J, Grimme C, Trautmann H. Dynamic Bi-Objective Routing of Multiple Vehicles. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO ’20. Association for Computing Machinery; 2020:166–174. doi:<a href=\"https://doi.org/10.1145/3377930.3390146\">10.1145/3377930.3390146</a>","ieee":"J. Bossek, C. Grimme, and H. Trautmann, “Dynamic Bi-Objective Routing of Multiple Vehicles,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2020, pp. 166–174, doi: <a href=\"https://doi.org/10.1145/3377930.3390146\">10.1145/3377930.3390146</a>.","apa":"Bossek, J., Grimme, C., &#38; Trautmann, H. (2020). Dynamic Bi-Objective Routing of Multiple Vehicles. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 166–174. <a href=\"https://doi.org/10.1145/3377930.3390146\">https://doi.org/10.1145/3377930.3390146</a>","short":"J. Bossek, C. Grimme, H. Trautmann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2020, pp. 166–174.","chicago":"Bossek, Jakob, Christian Grimme, and Heike Trautmann. “Dynamic Bi-Objective Routing of Multiple Vehicles.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 166–174. GECCO ’20. New York, NY, USA: Association for Computing Machinery, 2020. <a href=\"https://doi.org/10.1145/3377930.3390146\">https://doi.org/10.1145/3377930.3390146</a>."},"place":"New York, NY, USA","status":"public","publisher":"Association for Computing Machinery","_id":"48845","page":"166–174","user_id":"102979","publication":"Proceedings of the Genetic and Evolutionary Computation Conference","extern":"1","abstract":[{"text":"In practice, e.g. in delivery and service scenarios, Vehicle-Routing-Problems (VRPs) often imply repeated decision making on dynamic customer requests. As in classical VRPs, tours have to be planned short while the number of serviced customers has to be maximized at the same time resulting in a multi-objective problem. Beyond that, however, dynamic requests lead to the need for re-planning of not yet realized tour parts, while already realized tour parts are irreversible. In this paper we study this type of bi-objective dynamic VRP including sequential decision making and concurrent realization of decisions. We adopt a recently proposed Dynamic Evolutionary Multi-Objective Algorithm (DEMOA) for a related VRP problem and extend it to the more realistic (here considered) scenario of multiple vehicles. We empirically show that our DEMOA is competitive with a multi-vehicle offline and clairvoyant variant of the proposed DEMOA as well as with the dynamic single-vehicle approach proposed earlier.","lang":"eng"}],"date_created":"2023-11-14T15:58:52Z","department":[{"_id":"819"}],"type":"conference","keyword":["decision making","dynamic optimization","evolutionary algorithms","multi-objective optimization","vehicle routing"],"author":[{"id":"102979","last_name":"Bossek","orcid":"0000-0002-4121-4668","first_name":"Jakob","full_name":"Bossek, Jakob"},{"full_name":"Grimme, Christian","first_name":"Christian","last_name":"Grimme"},{"full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike"}],"publication_identifier":{"isbn":["978-1-4503-7128-5"]},"year":"2020","title":"Dynamic Bi-Objective Routing of Multiple Vehicles","publication_status":"published","date_updated":"2023-12-13T10:43:24Z","language":[{"iso":"eng"}],"series_title":"GECCO ’20","doi":"10.1145/3377930.3390146"},{"status":"public","title":"Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection","year":"2020","author":[{"id":"102979","last_name":"Bossek","first_name":"Jakob","orcid":"0000-0002-4121-4668","full_name":"Bossek, Jakob"},{"last_name":"Kerschke","first_name":"Pascal","full_name":"Kerschke, Pascal"},{"full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike"}],"publication_status":"published","date_updated":"2023-12-13T10:43:16Z","page":"1–8","_id":"48844","publisher":"IEEE Press","language":[{"iso":"eng"}],"user_id":"102979","doi":"10.1109/CEC48606.2020.9185613","publication":"2020 IEEE Congress on Evolutionary Computation (CEC)","citation":{"mla":"Bossek, Jakob, et al. “Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection.” <i>2020 IEEE Congress on Evolutionary Computation (CEC)</i>, IEEE Press, 2020, pp. 1–8, doi:<a href=\"https://doi.org/10.1109/CEC48606.2020.9185613\">10.1109/CEC48606.2020.9185613</a>.","bibtex":"@inproceedings{Bossek_Kerschke_Trautmann_2020, place={Glasgow, United Kingdom}, title={Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection}, DOI={<a href=\"https://doi.org/10.1109/CEC48606.2020.9185613\">10.1109/CEC48606.2020.9185613</a>}, booktitle={2020 IEEE Congress on Evolutionary Computation (CEC)}, publisher={IEEE Press}, author={Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}, year={2020}, pages={1–8} }","ama":"Bossek J, Kerschke P, Trautmann H. Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection. In: <i>2020 IEEE Congress on Evolutionary Computation (CEC)</i>. IEEE Press; 2020:1–8. doi:<a href=\"https://doi.org/10.1109/CEC48606.2020.9185613\">10.1109/CEC48606.2020.9185613</a>","ieee":"J. Bossek, P. Kerschke, and H. Trautmann, “Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection,” in <i>2020 IEEE Congress on Evolutionary Computation (CEC)</i>, 2020, pp. 1–8, doi: <a href=\"https://doi.org/10.1109/CEC48606.2020.9185613\">10.1109/CEC48606.2020.9185613</a>.","apa":"Bossek, J., Kerschke, P., &#38; Trautmann, H. (2020). Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection. <i>2020 IEEE Congress on Evolutionary Computation (CEC)</i>, 1–8. <a href=\"https://doi.org/10.1109/CEC48606.2020.9185613\">https://doi.org/10.1109/CEC48606.2020.9185613</a>","short":"J. Bossek, P. Kerschke, H. Trautmann, in: 2020 IEEE Congress on Evolutionary Computation (CEC), IEEE Press, Glasgow, United Kingdom, 2020, pp. 1–8.","chicago":"Bossek, Jakob, Pascal Kerschke, and Heike Trautmann. “Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection.” In <i>2020 IEEE Congress on Evolutionary Computation (CEC)</i>, 1–8. Glasgow, United Kingdom: IEEE Press, 2020. <a href=\"https://doi.org/10.1109/CEC48606.2020.9185613\">https://doi.org/10.1109/CEC48606.2020.9185613</a>."},"extern":"1","abstract":[{"text":"The Traveling-Salesperson-Problem (TSP) is arguably one of the best-known NP-hard combinatorial optimization problems. The two sophisticated heuristic solvers LKH and EAX and respective (restart) variants manage to calculate close-to optimal or even optimal solutions, also for large instances with several thousand nodes in reasonable time. In this work we extend existing benchmarking studies by addressing anytime behaviour of inexact TSP solvers based on empirical runtime distributions leading to an increased understanding of solver behaviour and the respective relation to problem hardness. It turns out that performance ranking of solvers is highly dependent on the focused approximation quality. Insights on intersection points of performances offer huge potential for the construction of hybridized solvers depending on instance features. Moreover, instance features tailored to anytime performance and corresponding performance indicators will highly improve automated algorithm selection models by including comprehensive information on solver quality.","lang":"eng"}],"date_created":"2023-11-14T15:58:52Z","place":"Glasgow, United Kingdom","type":"conference","department":[{"_id":"819"}]},{"status":"public","page":"778–786","publisher":"Association for Computing Machinery","_id":"48850","user_id":"102979","citation":{"chicago":"Bossek, Jakob, Carola Doerr, and Pascal Kerschke. “Initial Design Strategies and Their Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 778–786. GECCO ’20. New York, NY, USA: Association for Computing Machinery, 2020. <a href=\"https://doi.org/10.1145/3377930.3390155\">https://doi.org/10.1145/3377930.3390155</a>.","short":"J. Bossek, C. Doerr, P. Kerschke, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2020, pp. 778–786.","ieee":"J. Bossek, C. Doerr, and P. Kerschke, “Initial Design Strategies and Their Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2020, pp. 778–786, doi: <a href=\"https://doi.org/10.1145/3377930.3390155\">10.1145/3377930.3390155</a>.","apa":"Bossek, J., Doerr, C., &#38; Kerschke, P. (2020). Initial Design Strategies and Their Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 778–786. <a href=\"https://doi.org/10.1145/3377930.3390155\">https://doi.org/10.1145/3377930.3390155</a>","bibtex":"@inproceedings{Bossek_Doerr_Kerschke_2020, place={New York, NY, USA}, series={GECCO ’20}, title={Initial Design Strategies and Their Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB}, DOI={<a href=\"https://doi.org/10.1145/3377930.3390155\">10.1145/3377930.3390155</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Doerr, Carola and Kerschke, Pascal}, year={2020}, pages={778–786}, collection={GECCO ’20} }","ama":"Bossek J, Doerr C, Kerschke P. Initial Design Strategies and Their Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO ’20. Association for Computing Machinery; 2020:778–786. doi:<a href=\"https://doi.org/10.1145/3377930.3390155\">10.1145/3377930.3390155</a>","mla":"Bossek, Jakob, et al. “Initial Design Strategies and Their Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2020, pp. 778–786, doi:<a href=\"https://doi.org/10.1145/3377930.3390155\">10.1145/3377930.3390155</a>."},"place":"New York, NY, USA","year":"2020","title":"Initial Design Strategies and Their Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB","publication_identifier":{"isbn":["978-1-4503-7128-5"]},"author":[{"id":"102979","orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob","full_name":"Bossek, Jakob"},{"full_name":"Doerr, Carola","first_name":"Carola","last_name":"Doerr"},{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"}],"publication_status":"published","date_updated":"2023-12-13T10:44:01Z","series_title":"GECCO ’20","language":[{"iso":"eng"}],"doi":"10.1145/3377930.3390155","publication":"Proceedings of the Genetic and Evolutionary Computation Conference","extern":"1","abstract":[{"lang":"eng","text":"Sequential model-based optimization (SMBO) approaches are algorithms for solving problems that require computationally or otherwise expensive function evaluations. The key design principle of SMBO is a substitution of the true objective function by a surrogate, which is used to propose the point(s) to be evaluated next. SMBO algorithms are intrinsically modular, leaving the user with many important design choices. Significant research efforts go into understanding which settings perform best for which type of problems. Most works, however, focus on the choice of the model, the acquisition function, and the strategy used to optimize the latter. The choice of the initial sampling strategy, however, receives much less attention. Not surprisingly, quite diverging recommendations can be found in the literature. We analyze in this work how the size and the distribution of the initial sample influences the overall quality of the efficient global optimization (EGO) algorithm, a well-known SMBO approach. While, overall, small initial budgets using Halton sampling seem preferable, we also observe that the performance landscape is rather unstructured. We furthermore identify several situations in which EGO performs unfavorably against random sampling. Both observations indicate that an adaptive SMBO design could be beneficial, making SMBO an interesting test-bed for automated algorithm design."}],"date_created":"2023-11-14T15:58:53Z","type":"conference","keyword":["continuous black-box optimization","design of experiments","initial design","sequential model-based optimization"],"department":[{"_id":"819"}]},{"place":"Berlin, Heidelberg","citation":{"bibtex":"@inproceedings{Bossek_Neumann_Neumann_2020, place={Berlin, Heidelberg}, title={Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-58112-1_24\">10.1007/978-3-030-58112-1_24</a>}, booktitle={Parallel Problem Solving from Nature (PPSN XVI)}, publisher={Springer-Verlag}, author={Bossek, Jakob and Neumann, Aneta and Neumann, Frank}, year={2020}, pages={346–359} }","ama":"Bossek J, Neumann A, Neumann F. Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions. In: <i>Parallel Problem Solving from Nature (PPSN XVI)</i>. Springer-Verlag; 2020:346–359. doi:<a href=\"https://doi.org/10.1007/978-3-030-58112-1_24\">10.1007/978-3-030-58112-1_24</a>","mla":"Bossek, Jakob, et al. “Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions.” <i>Parallel Problem Solving from Nature (PPSN XVI)</i>, Springer-Verlag, 2020, pp. 346–359, doi:<a href=\"https://doi.org/10.1007/978-3-030-58112-1_24\">10.1007/978-3-030-58112-1_24</a>.","short":"J. Bossek, A. Neumann, F. Neumann, in: Parallel Problem Solving from Nature (PPSN XVI), Springer-Verlag, Berlin, Heidelberg, 2020, pp. 346–359.","chicago":"Bossek, Jakob, Aneta Neumann, and Frank Neumann. “Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions.” In <i>Parallel Problem Solving from Nature (PPSN XVI)</i>, 346–359. Berlin, Heidelberg: Springer-Verlag, 2020. <a href=\"https://doi.org/10.1007/978-3-030-58112-1_24\">https://doi.org/10.1007/978-3-030-58112-1_24</a>.","ieee":"J. Bossek, A. Neumann, and F. Neumann, “Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions,” in <i>Parallel Problem Solving from Nature (PPSN XVI)</i>, 2020, pp. 346–359, doi: <a href=\"https://doi.org/10.1007/978-3-030-58112-1_24\">10.1007/978-3-030-58112-1_24</a>.","apa":"Bossek, J., Neumann, A., &#38; Neumann, F. (2020). Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions. <i>Parallel Problem Solving from Nature (PPSN XVI)</i>, 346–359. <a href=\"https://doi.org/10.1007/978-3-030-58112-1_24\">https://doi.org/10.1007/978-3-030-58112-1_24</a>"},"user_id":"102979","page":"346–359","publisher":"Springer-Verlag","_id":"48852","status":"public","keyword":["Evolutionary algorithms","Node weight dependent TSP","Traveling Thief Problem"],"type":"conference","department":[{"_id":"819"}],"date_created":"2023-11-14T15:58:54Z","abstract":[{"lang":"eng","text":"The Traveling Salesperson Problem (TSP) is one of the best-known combinatorial optimisation problems. However, many real-world problems are composed of several interacting components. The Traveling Thief Problem (TTP) addresses such interactions by combining two combinatorial optimisation problems, namely the TSP and the Knapsack Problem (KP). Recently, a new problem called the node weight dependent Traveling Salesperson Problem (W-TSP) has been introduced where nodes have weights that influence the cost of the tour. In this paper, we compare W-TSP and TTP. We investigate the structure of the optimised tours for W-TSP and TTP and the impact of using each others fitness function. Our experimental results suggest (1) that the W-TSP often can be solved better using the TTP fitness function and (2) final W-TSP and TTP solutions show different distributions when compared with optimal TSP or weighted greedy solutions."}],"extern":"1","publication":"Parallel Problem Solving from Nature (PPSN XVI)","doi":"10.1007/978-3-030-58112-1_24","language":[{"iso":"eng"}],"date_updated":"2023-12-13T10:44:54Z","publication_status":"published","year":"2020","title":"Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions","author":[{"full_name":"Bossek, Jakob","orcid":"0000-0002-4121-4668","first_name":"Jakob","last_name":"Bossek","id":"102979"},{"first_name":"Aneta","last_name":"Neumann","full_name":"Neumann, Aneta"},{"full_name":"Neumann, Frank","first_name":"Frank","last_name":"Neumann"}],"publication_identifier":{"isbn":["978-3-030-58111-4"]}},{"extern":"1","abstract":[{"text":"We consider a dynamic bi-objective vehicle routing problem, where a subset of customers ask for service over time. Therein, the distance traveled by a single vehicle and the number of unserved dynamic requests is minimized by a dynamic evolutionary multi-objective algorithm (DEMOA), which operates on discrete time windows (eras). A decision is made at each era by a decision-maker, thus any decision depends on irreversible decisions made in foregoing eras. To understand effects of sequences of decision-making and interactions/dependencies between decisions made, we conduct a series of experiments. More precisely, we fix a set of decision-maker preferences D and the number of eras n{$<$}inf{$>$}t{$<$}/inf{$>$} and analyze all $|D|\\^{n_t}$ combinations of decision-maker options. We find that for random uniform instances (a) the final selected solutions mainly depend on the final decision and not on the decision history, (b) solutions are quite robust with respect to the number of unvisited dynamic customers, and (c) solutions of the dynamic approach can even dominate solutions obtained by a clairvoyant EMOA. In contrast, for instances with clustered customers, we observe a strong dependency on decision-making history as well as more variance in solution diversity.","lang":"eng"}],"publication":"2020 IEEE Congress on Evolutionary Computation (CEC)","citation":{"apa":"Bossek, J., Grimme, C., Rudolph, G., &#38; Trautmann, H. (2020). Towards Decision Support in Dynamic Bi-Objective Vehicle Routing. <i>2020 IEEE Congress on Evolutionary Computation (CEC)</i>, 1–8. <a href=\"https://doi.org/10.1109/CEC48606.2020.9185778\">https://doi.org/10.1109/CEC48606.2020.9185778</a>","ieee":"J. Bossek, C. Grimme, G. Rudolph, and H. Trautmann, “Towards Decision Support in Dynamic Bi-Objective Vehicle Routing,” in <i>2020 IEEE Congress on Evolutionary Computation (CEC)</i>, 2020, pp. 1–8, doi: <a href=\"https://doi.org/10.1109/CEC48606.2020.9185778\">10.1109/CEC48606.2020.9185778</a>.","chicago":"Bossek, Jakob, Christian Grimme, Günter Rudolph, and Heike Trautmann. “Towards Decision Support in Dynamic Bi-Objective Vehicle Routing.” In <i>2020 IEEE Congress on Evolutionary Computation (CEC)</i>, 1–8. Glasgow, United Kingdom: IEEE Press, 2020. <a href=\"https://doi.org/10.1109/CEC48606.2020.9185778\">https://doi.org/10.1109/CEC48606.2020.9185778</a>.","short":"J. Bossek, C. Grimme, G. Rudolph, H. Trautmann, in: 2020 IEEE Congress on Evolutionary Computation (CEC), IEEE Press, Glasgow, United Kingdom, 2020, pp. 1–8.","mla":"Bossek, Jakob, et al. “Towards Decision Support in Dynamic Bi-Objective Vehicle Routing.” <i>2020 IEEE Congress on Evolutionary Computation (CEC)</i>, IEEE Press, 2020, pp. 1–8, doi:<a href=\"https://doi.org/10.1109/CEC48606.2020.9185778\">10.1109/CEC48606.2020.9185778</a>.","ama":"Bossek J, Grimme C, Rudolph G, Trautmann H. Towards Decision Support in Dynamic Bi-Objective Vehicle Routing. In: <i>2020 IEEE Congress on Evolutionary Computation (CEC)</i>. IEEE Press; 2020:1–8. doi:<a href=\"https://doi.org/10.1109/CEC48606.2020.9185778\">10.1109/CEC48606.2020.9185778</a>","bibtex":"@inproceedings{Bossek_Grimme_Rudolph_Trautmann_2020, place={Glasgow, United Kingdom}, title={Towards Decision Support in Dynamic Bi-Objective Vehicle Routing}, DOI={<a href=\"https://doi.org/10.1109/CEC48606.2020.9185778\">10.1109/CEC48606.2020.9185778</a>}, booktitle={2020 IEEE Congress on Evolutionary Computation (CEC)}, publisher={IEEE Press}, author={Bossek, Jakob and Grimme, Christian and Rudolph, Günter and Trautmann, Heike}, year={2020}, pages={1–8} }"},"type":"conference","department":[{"_id":"819"}],"date_created":"2023-11-14T15:58:53Z","place":"Glasgow, United Kingdom","publication_status":"published","date_updated":"2023-12-13T10:44:17Z","status":"public","year":"2020","title":"Towards Decision Support in Dynamic Bi-Objective Vehicle Routing","author":[{"id":"102979","full_name":"Bossek, Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob"},{"full_name":"Grimme, Christian","first_name":"Christian","last_name":"Grimme"},{"last_name":"Rudolph","first_name":"Günter","full_name":"Rudolph, Günter"},{"full_name":"Trautmann, Heike","first_name":"Heike","last_name":"Trautmann"}],"user_id":"102979","doi":"10.1109/CEC48606.2020.9185778","page":"1–8","language":[{"iso":"eng"}],"_id":"48846","publisher":"IEEE Press"},{"place":"New York, NY, USA","citation":{"short":"A.V. Do, J. Bossek, A. Neumann, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2020, pp. 681–689.","chicago":"Do, Anh Viet, Jakob Bossek, Aneta Neumann, and Frank Neumann. “Evolving Diverse Sets of Tours for the Travelling Salesperson Problem.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 681–689. GECCO’20. New York, NY, USA: Association for Computing Machinery, 2020. <a href=\"https://doi.org/10.1145/3377930.3389844\">https://doi.org/10.1145/3377930.3389844</a>.","ieee":"A. V. Do, J. Bossek, A. Neumann, and F. Neumann, “Evolving Diverse Sets of Tours for the Travelling Salesperson Problem,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2020, pp. 681–689, doi: <a href=\"https://doi.org/10.1145/3377930.3389844\">10.1145/3377930.3389844</a>.","apa":"Do, A. V., Bossek, J., Neumann, A., &#38; Neumann, F. (2020). Evolving Diverse Sets of Tours for the Travelling Salesperson Problem. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 681–689. <a href=\"https://doi.org/10.1145/3377930.3389844\">https://doi.org/10.1145/3377930.3389844</a>","bibtex":"@inproceedings{Do_Bossek_Neumann_Neumann_2020, place={New York, NY, USA}, series={GECCO’20}, title={Evolving Diverse Sets of Tours for the Travelling Salesperson Problem}, DOI={<a href=\"https://doi.org/10.1145/3377930.3389844\">10.1145/3377930.3389844</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Do, Anh Viet and Bossek, Jakob and Neumann, Aneta and Neumann, Frank}, year={2020}, pages={681–689}, collection={GECCO’20} }","ama":"Do AV, Bossek J, Neumann A, Neumann F. Evolving Diverse Sets of Tours for the Travelling Salesperson Problem. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO’20. Association for Computing Machinery; 2020:681–689. doi:<a href=\"https://doi.org/10.1145/3377930.3389844\">10.1145/3377930.3389844</a>","mla":"Do, Anh Viet, et al. “Evolving Diverse Sets of Tours for the Travelling Salesperson Problem.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2020, pp. 681–689, doi:<a href=\"https://doi.org/10.1145/3377930.3389844\">10.1145/3377930.3389844</a>."},"page":"681–689","_id":"48879","publisher":"Association for Computing Machinery","user_id":"102979","status":"public","date_created":"2023-11-14T15:58:58Z","type":"conference","keyword":["diversity maximisation","evolutionary algorithms","travelling salesperson problem"],"department":[{"_id":"819"}],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference","extern":"1","abstract":[{"text":"Evolving diverse sets of high quality solutions has gained increasing interest in the evolutionary computation literature in recent years. With this paper, we contribute to this area of research by examining evolutionary diversity optimisation approaches for the classical Traveling Salesperson Problem (TSP). We study the impact of using different diversity measures for a given set of tours and the ability of evolutionary algorithms to obtain a diverse set of high quality solutions when adopting these measures. Our studies show that a large variety of diverse high quality tours can be achieved by using our approaches. Furthermore, we compare our approaches in terms of theoretical properties and the final set of tours obtained by the evolutionary diversity optimisation algorithm.","lang":"eng"}],"language":[{"iso":"eng"}],"series_title":"GECCO’20","doi":"10.1145/3377930.3389844","year":"2020","title":"Evolving Diverse Sets of Tours for the Travelling Salesperson Problem","publication_identifier":{"isbn":["978-1-4503-7128-5"]},"author":[{"first_name":"Anh Viet","last_name":"Do","full_name":"Do, Anh Viet"},{"last_name":"Bossek","orcid":"0000-0002-4121-4668","first_name":"Jakob","full_name":"Bossek, Jakob","id":"102979"},{"last_name":"Neumann","first_name":"Aneta","full_name":"Neumann, Aneta"},{"first_name":"Frank","last_name":"Neumann","full_name":"Neumann, Frank"}],"date_updated":"2023-12-13T10:48:50Z"},{"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>","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} }","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>.","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>.","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.","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>","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>."},"place":"New York, NY, USA","status":"public","user_id":"102979","_id":"48895","publisher":"Association for Computing Machinery","page":"551–559","abstract":[{"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.","lang":"eng"}],"extern":"1","publication":"Proceedings of the 2020 Genetic and Evolutionary Computation Conference","department":[{"_id":"819"}],"keyword":["biased mutation","evolutionary algorithms","minimum spanning tree problem","runtime analysis"],"type":"conference","date_created":"2023-11-14T15:59:00Z","date_updated":"2023-12-13T10:49:38Z","author":[{"last_name":"Roostapour","first_name":"Vahid","full_name":"Roostapour, Vahid"},{"orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob","full_name":"Bossek, Jakob","id":"102979"},{"last_name":"Neumann","first_name":"Frank","full_name":"Neumann, Frank"}],"publication_identifier":{"isbn":["978-1-4503-7128-5"]},"title":"Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem","year":"2020","doi":"10.1145/3377930.3390168","language":[{"iso":"eng"}],"series_title":"{GECCO} ’20"},{"page":"48–64","publisher":"Springer-Verlag","_id":"48897","user_id":"102979","status":"public","place":"Berlin, Heidelberg","citation":{"chicago":"Seiler, Moritz, Janina Pohl, Jakob Bossek, Pascal Kerschke, and Heike Trautmann. “Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem.” In <i>Parallel Problem Solving from {Nature} (PPSN XVI)</i>, 48–64. Berlin, Heidelberg: Springer-Verlag, 2020. <a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">https://doi.org/10.1007/978-3-030-58112-1_4</a>.","short":"M. Seiler, J. Pohl, J. Bossek, P. Kerschke, H. Trautmann, in: Parallel Problem Solving from {Nature} (PPSN XVI), Springer-Verlag, Berlin, Heidelberg, 2020, pp. 48–64.","ama":"Seiler M, Pohl J, Bossek J, Kerschke P, Trautmann H. Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. In: <i>Parallel Problem Solving from {Nature} (PPSN XVI)</i>. Springer-Verlag; 2020:48–64. doi:<a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">10.1007/978-3-030-58112-1_4</a>","bibtex":"@inproceedings{Seiler_Pohl_Bossek_Kerschke_Trautmann_2020, place={Berlin, Heidelberg}, title={Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">10.1007/978-3-030-58112-1_4</a>}, booktitle={Parallel Problem Solving from {Nature} (PPSN XVI)}, publisher={Springer-Verlag}, author={Seiler, Moritz and Pohl, Janina and Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}, year={2020}, pages={48–64} }","apa":"Seiler, M., Pohl, J., Bossek, J., Kerschke, P., &#38; Trautmann, H. (2020). Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. <i>Parallel Problem Solving from {Nature} (PPSN XVI)</i>, 48–64. <a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">https://doi.org/10.1007/978-3-030-58112-1_4</a>","mla":"Seiler, Moritz, et al. “Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem.” <i>Parallel Problem Solving from {Nature} (PPSN XVI)</i>, Springer-Verlag, 2020, pp. 48–64, doi:<a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">10.1007/978-3-030-58112-1_4</a>.","ieee":"M. Seiler, J. Pohl, J. Bossek, P. Kerschke, and H. Trautmann, “Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem,” in <i>Parallel Problem Solving from {Nature} (PPSN XVI)</i>, 2020, pp. 48–64, doi: <a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">10.1007/978-3-030-58112-1_4</a>."},"language":[{"iso":"eng"}],"doi":"10.1007/978-3-030-58112-1_4","title":"Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem","year":"2020","author":[{"full_name":"Seiler, Moritz","last_name":"Seiler","first_name":"Moritz"},{"first_name":"Janina","last_name":"Pohl","full_name":"Pohl, Janina"},{"id":"102979","first_name":"Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","full_name":"Bossek, Jakob"},{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"},{"last_name":"Trautmann","first_name":"Heike","full_name":"Trautmann, Heike"}],"publication_identifier":{"isbn":["978-3-030-58111-4"]},"date_updated":"2023-12-13T10:49:45Z","date_created":"2023-11-14T15:59:00Z","type":"conference","keyword":["Automated algorithm selection","Deep learning","Feature-based approaches","Traveling Salesperson Problem"],"department":[{"_id":"819"}],"publication":"Parallel Problem Solving from {Nature} (PPSN XVI)","extern":"1","abstract":[{"lang":"eng","text":"In this work we focus on the well-known Euclidean Traveling Salesperson Problem (TSP) and two highly competitive inexact heuristic TSP solvers, EAX and LKH, in the context of per-instance algorithm selection (AS). We evolve instances with nodes where the solvers show strongly different performance profiles. These instances serve as a basis for an exploratory study on the identification of well-discriminating problem characteristics (features). Our results in a nutshell: we show that even though (1) promising features exist, (2) these are in line with previous results from the literature, and (3) models trained with these features are more accurate than models adopting sophisticated feature selection methods, the advantage is not close to the virtual best solver in terms of penalized average runtime and so is the performance gain over the single best solver. However, we show that a feature-free deep neural network based approach solely based on visual representation of the instances already matches classical AS model results and thus shows huge potential for future studies."}]},{"abstract":[{"text":"We build upon a recently proposed multi-objective view onto performance measurement of single-objective stochastic solvers. The trade-off between the fraction of failed runs and the mean runtime of successful runs \\textendash both to be minimized \\textendash is directly analyzed based on a study on algorithm selection of inexact state-of-the-art solvers for the famous Traveling Salesperson Problem (TSP). Moreover, we adopt the hypervolume indicator (HV) commonly used in multi-objective optimization for simultaneously assessing both conflicting objectives and investigate relations to commonly used performance indicators, both theoretically and empirically. Next to Penalized Average Runtime (PAR) and Penalized Quantile Runtime (PQR), the HV measure is used as a core concept within the construction of per-instance algorithm selection models offering interesting insights into complementary behavior of inexact TSP solvers. \\textbullet The multi-objective perspective is naturally generalizable to multiple objectives. \\textbullet Proof of relationship between HV and the PAR in the considered bi-objective space. \\textbullet New insights into complementary behavior of stochastic optimization algorithms.","lang":"eng"}],"citation":{"chicago":"Bossek, Jakob, Pascal Kerschke, and Heike Trautmann. “A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms.” <i>Applied Soft Computing</i> 88, no. C (2020). <a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>.","ama":"Bossek J, Kerschke P, Trautmann H. A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms. <i>Applied Soft Computing</i>. 2020;88(C). doi:<a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">10.1016/j.asoc.2019.105901</a>","short":"J. Bossek, P. Kerschke, H. Trautmann, Applied Soft Computing 88 (2020).","bibtex":"@article{Bossek_Kerschke_Trautmann_2020, title={A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms}, volume={88}, DOI={<a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">10.1016/j.asoc.2019.105901</a>}, number={C}, journal={Applied Soft Computing}, author={Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}, year={2020} }","apa":"Bossek, J., Kerschke, P., &#38; Trautmann, H. (2020). A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms. <i>Applied Soft Computing</i>, <i>88</i>(C). <a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>","mla":"Bossek, Jakob, et al. “A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms.” <i>Applied Soft Computing</i>, vol. 88, no. C, 2020, doi:<a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">10.1016/j.asoc.2019.105901</a>.","ieee":"J. Bossek, P. Kerschke, and H. Trautmann, “A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms,” <i>Applied Soft Computing</i>, vol. 88, no. C, 2020, doi: <a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">10.1016/j.asoc.2019.105901</a>."},"issue":"C","publication":"Applied Soft Computing","department":[{"_id":"819"}],"keyword":["Algorithm selection","Combinatorial optimization","Multi-objective optimization","Performance measurement","Traveling Salesperson Problem"],"type":"journal_article","date_created":"2023-11-14T15:58:53Z","intvolume":"        88","date_updated":"2023-12-13T10:52:17Z","author":[{"id":"102979","full_name":"Bossek, Jakob","first_name":"Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668"},{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"},{"full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike"}],"publication_identifier":{"issn":["1568-4946"]},"status":"public","title":"A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms","year":"2020","volume":88,"doi":"10.1016/j.asoc.2019.105901","user_id":"102979","_id":"48848","language":[{"iso":"eng"}]},{"date_updated":"2023-12-13T10:52:24Z","author":[{"full_name":"Bartz-Beielstein, Thomas","first_name":"Thomas","last_name":"Bartz-Beielstein"},{"full_name":"Doerr, Carola","first_name":"Carola","last_name":"Doerr"},{"full_name":"van den Berg, Daan","first_name":"Daan","last_name":"van den Berg"},{"id":"102979","first_name":"Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","full_name":"Bossek, Jakob"},{"full_name":"Chandrasekaran, Sowmya","last_name":"Chandrasekaran","first_name":"Sowmya"},{"full_name":"Eftimov, Tome","last_name":"Eftimov","first_name":"Tome"},{"first_name":"Andreas","last_name":"Fischbach","full_name":"Fischbach, Andreas"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"full_name":"Cava, William La","first_name":"William La","last_name":"Cava"},{"first_name":"Manuel","last_name":"Lopez-Ibanez","full_name":"Lopez-Ibanez, Manuel"},{"last_name":"Malan","first_name":"Katherine M.","full_name":"Malan, Katherine M."},{"first_name":"Jason H.","last_name":"Moore","full_name":"Moore, Jason H."},{"first_name":"Boris","last_name":"Naujoks","full_name":"Naujoks, Boris"},{"full_name":"Orzechowski, Patryk","last_name":"Orzechowski","first_name":"Patryk"},{"last_name":"Volz","first_name":"Vanessa","full_name":"Volz, Vanessa"},{"full_name":"Wagner, Markus","first_name":"Markus","last_name":"Wagner"},{"first_name":"Thomas","last_name":"Weise","full_name":"Weise, Thomas"}],"title":"Benchmarking in Optimization: Best Practice and Open Issues","status":"public","year":"2020","user_id":"102979","_id":"48836","language":[{"iso":"eng"}],"citation":{"mla":"Bartz-Beielstein, Thomas, et al. “Benchmarking in Optimization: Best Practice and Open Issues.” <i>Corr</i>, 2020.","bibtex":"@article{Bartz-Beielstein_Doerr_van den Berg_Bossek_Chandrasekaran_Eftimov_Fischbach_Kerschke_Cava_Lopez-Ibanez_et al._2020, title={Benchmarking in Optimization: Best Practice and Open Issues}, journal={Corr}, author={Bartz-Beielstein, Thomas and Doerr, Carola and van den Berg, Daan and Bossek, Jakob and Chandrasekaran, Sowmya and Eftimov, Tome and Fischbach, Andreas and Kerschke, Pascal and Cava, William La and Lopez-Ibanez, Manuel and et al.}, year={2020} }","ama":"Bartz-Beielstein T, Doerr C, van den Berg D, et al. Benchmarking in Optimization: Best Practice and Open Issues. <i>Corr</i>. Published online 2020.","ieee":"T. Bartz-Beielstein <i>et al.</i>, “Benchmarking in Optimization: Best Practice and Open Issues,” <i>Corr</i>, 2020.","apa":"Bartz-Beielstein, T., Doerr, C., van den Berg, D., Bossek, J., Chandrasekaran, S., Eftimov, T., Fischbach, A., Kerschke, P., Cava, W. L., Lopez-Ibanez, M., Malan, K. M., Moore, J. H., Naujoks, B., Orzechowski, P., Volz, V., Wagner, M., &#38; Weise, T. (2020). Benchmarking in Optimization: Best Practice and Open Issues. <i>Corr</i>.","chicago":"Bartz-Beielstein, Thomas, Carola Doerr, Daan van den Berg, Jakob Bossek, Sowmya Chandrasekaran, Tome Eftimov, Andreas Fischbach, et al. “Benchmarking in Optimization: Best Practice and Open Issues.” <i>Corr</i>, 2020.","short":"T. Bartz-Beielstein, C. Doerr, D. van den Berg, J. Bossek, S. Chandrasekaran, T. Eftimov, A. Fischbach, P. Kerschke, W.L. Cava, M. Lopez-Ibanez, K.M. Malan, J.H. Moore, B. Naujoks, P. Orzechowski, V. Volz, M. Wagner, T. Weise, Corr (2020)."},"publication":"Corr","department":[{"_id":"819"}],"type":"journal_article","date_created":"2023-11-14T15:58:51Z"},{"abstract":[{"lang":"eng","text":"Artificial neural networks in general and deep learning networks in particular established themselves as popular and powerful machine learning algorithms. While the often tremendous sizes of these networks are beneficial when solving complex tasks, the tremendous number of parameters also causes such networks to be vulnerable to malicious behavior such as adversarial perturbations. These perturbations can change a model's classification decision. Moreover, while single-step adversaries can easily be transferred from network to network, the transfer of more powerful multi-step adversaries has - usually - been rather difficult.In this work, we introduce a method for generating strong adversaries that can easily (and frequently) be transferred between different models. This method is then used to generate a large set of adversaries, based on which the effects of selected defense methods are experimentally assessed. At last, we introduce a novel, simple, yet effective approach to enhance the resilience of neural networks against adversaries and benchmark it against established defense methods. In contrast to the already existing methods, our proposed defense approach is much more efficient as it only requires a single additional forward-pass to achieve comparable performance results."}],"citation":{"short":"M. Seiler, H. Trautmann, P. Kerschke, in: Proceedings of the International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 2020, pp. 1–8.","chicago":"Seiler, Moritz, Heike Trautmann, and Pascal Kerschke. “Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries.” In <i>Proceedings of the International Joint Conference on Neural Networks (IJCNN)</i>, 1–8. Glasgow, UK, 2020. <a href=\"https://doi.org/10.1109/IJCNN48605.2020.9207338\">https://doi.org/10.1109/IJCNN48605.2020.9207338</a>.","apa":"Seiler, M., Trautmann, H., &#38; Kerschke, P. (2020). Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries. <i>Proceedings of the International Joint Conference on Neural Networks (IJCNN)</i>, 1–8. <a href=\"https://doi.org/10.1109/IJCNN48605.2020.9207338\">https://doi.org/10.1109/IJCNN48605.2020.9207338</a>","ieee":"M. Seiler, H. Trautmann, and P. Kerschke, “Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries,” in <i>Proceedings of the International Joint Conference on Neural Networks (IJCNN)</i>, 2020, pp. 1–8, doi: <a href=\"https://doi.org/10.1109/IJCNN48605.2020.9207338\">10.1109/IJCNN48605.2020.9207338</a>.","ama":"Seiler M, Trautmann H, Kerschke P. Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries. In: <i>Proceedings of the International Joint Conference on Neural Networks (IJCNN)</i>. ; 2020:1–8. doi:<a href=\"https://doi.org/10.1109/IJCNN48605.2020.9207338\">10.1109/IJCNN48605.2020.9207338</a>","bibtex":"@inproceedings{Seiler_Trautmann_Kerschke_2020, place={Glasgow, UK}, title={Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries}, DOI={<a href=\"https://doi.org/10.1109/IJCNN48605.2020.9207338\">10.1109/IJCNN48605.2020.9207338</a>}, booktitle={Proceedings of the International Joint Conference on Neural Networks (IJCNN)}, author={Seiler, Moritz and Trautmann, Heike and Kerschke, Pascal}, year={2020}, pages={1–8} }","mla":"Seiler, Moritz, et al. “Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries.” <i>Proceedings of the International Joint Conference on Neural Networks (IJCNN)</i>, 2020, pp. 1–8, doi:<a href=\"https://doi.org/10.1109/IJCNN48605.2020.9207338\">10.1109/IJCNN48605.2020.9207338</a>."},"publication":"Proceedings of the International Joint Conference on Neural Networks (IJCNN)","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","date_created":"2023-08-04T07:39:48Z","place":"Glasgow, UK","date_updated":"2024-06-07T07:11:53Z","author":[{"id":"105520","full_name":"Seiler, Moritz","last_name":"Seiler","first_name":"Moritz"},{"id":"100740","full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike","orcid":"0000-0002-9788-8282"},{"last_name":"Kerschke","first_name":"Pascal","full_name":"Kerschke, Pascal"}],"status":"public","year":"2020","title":"Enhancing Resilience of Deep Learning Networks By Means of Transferable Adversaries","user_id":"15504","doi":"10.1109/IJCNN48605.2020.9207338","language":[{"iso":"eng"}],"_id":"46331","page":"1–8"},{"department":[{"_id":"34"},{"_id":"819"}],"type":"conference","place":"Leiden, The Netherlands","date_created":"2023-08-04T07:39:05Z","abstract":[{"text":"In this work we focus on the well-known Euclidean Traveling Salesperson Problem (TSP) and two highly competitive inexact heuristic TSP solvers, EAX and LKH, in the context of per-instance algorithm selection (AS). We evolve instances with 1000 nodes where the solvers show strongly different performance profiles. These instances serve as a basis for an exploratory study on the identification of well-discriminating problem characteristics (features). Our results in a nutshell: we show that even though (1) promising features exist, (2) these are in line with previous results from the literature, and (3) models trained with these features are more accurate than models adopting sophisticated feature selection methods, the advantage is not close to the virtual best solver in terms of penalized average runtime and so is the performance gain over the single best solver. However, we show that a feature-free deep neural network based approach solely based on visual representation of the instances already matches classical AS model results and thus shows huge potential for future studies.","lang":"eng"}],"citation":{"apa":"Seiler, M., Pohl, J., Bossek, J., Kerschke, P., &#38; Trautmann, H. (2020). Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. In T. Bäck, M. Preuss, A. Deutz, H. Wang, C. Doerr, M. Emmerich, &#38; H. Trautmann (Eds.), <i>Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI)</i> (pp. 48–64). <a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">https://doi.org/10.1007/978-3-030-58112-1_4</a>","ieee":"M. Seiler, J. Pohl, J. Bossek, P. Kerschke, and H. Trautmann, “Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem,” in <i>Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI)</i>, 2020, pp. 48–64, doi: <a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">10.1007/978-3-030-58112-1_4</a>.","short":"M. Seiler, J. Pohl, J. Bossek, P. Kerschke, H. Trautmann, in: T. Bäck, M. Preuss, A. Deutz, H. Wang, C. Doerr, M. Emmerich, H. Trautmann (Eds.), Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI), Leiden, The Netherlands, 2020, pp. 48–64.","chicago":"Seiler, Moritz, Janina Pohl, Jakob Bossek, Pascal Kerschke, and Heike Trautmann. “Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem.” In <i>Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI)</i>, edited by Thomas Bäck, Mike Preuss, André Deutz, Hao Wang, Carola Doerr, Michael Emmerich, and Heike Trautmann, 48–64. Leiden, The Netherlands, 2020. <a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">https://doi.org/10.1007/978-3-030-58112-1_4</a>.","mla":"Seiler, Moritz, et al. “Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem.” <i>Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI)</i>, edited by Thomas Bäck et al., 2020, pp. 48–64, doi:<a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">10.1007/978-3-030-58112-1_4</a>.","ama":"Seiler M, Pohl J, Bossek J, Kerschke P, Trautmann H. Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. In: Bäck T, Preuss M, Deutz A, et al., eds. <i>Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI)</i>. ; 2020:48–64. doi:<a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">10.1007/978-3-030-58112-1_4</a>","bibtex":"@inproceedings{Seiler_Pohl_Bossek_Kerschke_Trautmann_2020, place={Leiden, The Netherlands}, title={Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">10.1007/978-3-030-58112-1_4</a>}, booktitle={Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI)}, author={Seiler, Moritz and Pohl, Janina and Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}, editor={Bäck, Thomas and Preuss, Mike and Deutz, André and Wang, Hao and Doerr, Carola and Emmerich, Michael and Trautmann, Heike}, year={2020}, pages={48–64} }"},"publication":"Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI)","editor":[{"full_name":"Bäck, Thomas","first_name":"Thomas","last_name":"Bäck"},{"first_name":"Mike","last_name":"Preuss","full_name":"Preuss, Mike"},{"full_name":"Deutz, André","first_name":"André","last_name":"Deutz"},{"full_name":"Wang, Hao","last_name":"Wang","first_name":"Hao"},{"full_name":"Doerr, Carola","first_name":"Carola","last_name":"Doerr"},{"last_name":"Emmerich","first_name":"Michael","full_name":"Emmerich, Michael"},{"last_name":"Trautmann","first_name":"Heike","full_name":"Trautmann, Heike"}],"doi":"10.1007/978-3-030-58112-1_4","user_id":"15504","language":[{"iso":"eng"}],"_id":"46330","page":"48–64","date_updated":"2024-06-10T11:57:13Z","author":[{"id":"105520","first_name":"Moritz","last_name":"Seiler","full_name":"Seiler, Moritz"},{"last_name":"Pohl","first_name":"Janina","full_name":"Pohl, Janina"},{"first_name":"Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","full_name":"Bossek, Jakob","id":"102979"},{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"},{"id":"100740","last_name":"Trautmann","orcid":"0000-0002-9788-8282","first_name":"Heike","full_name":"Trautmann, Heike"}],"title":"Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem","status":"public","year":"2020"},{"doi":"https://doi.org/10.1016/j.asoc.2019.105901","user_id":"15504","volume":88,"page":"105901","_id":"46334","language":[{"iso":"eng"}],"date_updated":"2024-06-10T12:00:46Z","intvolume":"        88","title":"A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms","status":"public","year":"2020","author":[{"full_name":"Bossek, Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","first_name":"Jakob","id":"102979"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"orcid":"0000-0002-9788-8282","first_name":"Heike","last_name":"Trautmann","full_name":"Trautmann, Heike","id":"100740"}],"publication_identifier":{"issn":["1568-4946"]},"keyword":["Algorithm selection","Multi-objective optimization","Performance measurement","Combinatorial optimization","Traveling Salesperson Problem"],"type":"journal_article","department":[{"_id":"34"},{"_id":"819"}],"date_created":"2023-08-04T07:42:26Z","abstract":[{"lang":"eng","text":"We build upon a recently proposed multi-objective view onto performance measurement of single-objective stochastic solvers. The trade-off between the fraction of failed runs and the mean runtime of successful runs – both to be minimized – is directly analyzed based on a study on algorithm selection of inexact state-of-the-art solvers for the famous Traveling Salesperson Problem (TSP). Moreover, we adopt the hypervolume indicator (HV) commonly used in multi-objective optimization for simultaneously assessing both conflicting objectives and investigate relations to commonly used performance indicators, both theoretically and empirically. Next to Penalized Average Runtime (PAR) and Penalized Quantile Runtime (PQR), the HV measure is used as a core concept within the construction of per-instance algorithm selection models offering interesting insights into complementary behavior of inexact TSP solvers."}],"publication":"Applied Soft Computing","citation":{"chicago":"Bossek, Jakob, Pascal Kerschke, and Heike Trautmann. “A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms.” <i>Applied Soft Computing</i> 88 (2020): 105901. <a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>.","short":"J. Bossek, P. Kerschke, H. Trautmann, Applied Soft Computing 88 (2020) 105901.","apa":"Bossek, J., Kerschke, P., &#38; Trautmann, H. (2020). A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms. <i>Applied Soft Computing</i>, <i>88</i>, 105901. <a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>","ieee":"J. Bossek, P. Kerschke, and H. Trautmann, “A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms,” <i>Applied Soft Computing</i>, vol. 88, p. 105901, 2020, doi: <a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>.","ama":"Bossek J, Kerschke P, Trautmann H. A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms. <i>Applied Soft Computing</i>. 2020;88:105901. doi:<a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>","bibtex":"@article{Bossek_Kerschke_Trautmann_2020, title={A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms}, volume={88}, DOI={<a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>}, journal={Applied Soft Computing}, author={Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}, year={2020}, pages={105901} }","mla":"Bossek, Jakob, et al. “A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms.” <i>Applied Soft Computing</i>, vol. 88, 2020, p. 105901, doi:<a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>."}},{"type":"conference","department":[{"_id":"34"},{"_id":"819"}],"date_created":"2023-08-04T07:32:36Z","place":"Glasgow, UK","abstract":[{"lang":"eng","text":"We consider a dynamic bi-objective vehicle routing problem, where a subset of customers ask for service over time. Therein, the distance traveled by a single vehicle and the number of unserved dynamic requests is minimized by a dynamic evolutionary multi-objective algorithm (DEMOA), which operates on discrete time windows (eras). A decision is made at each era by a decision-maker, thus any decision depends on irreversible decisions made in foregoing eras. To understand effects of sequences of decision-making and interactions/dependencies between decisions made, we conduct a series of experiments. More precisely, we fix a set of decision-maker preferences D and the number of eras n t and analyze all |D| nt combinations of decision-maker options. We find that for random uniform instances (a) the final selected solutions mainly depend on the final decision and not on the decision history, (b) solutions are quite robust with respect to the number of unvisited dynamic customers, and (c) solutions of the dynamic approach can even dominate solutions obtained by a clairvoyant EMOA. In contrast, for instances with clustered customers, we observe a strong dependency on decision-making history as well as more variance in solution diversity."}],"publication":"Proceedings of the IEEE Congress on Evolutionary Computation (CEC)","citation":{"short":"J. Bossek, C. Grimme, G. Rudolph, H. Trautmann, in: Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Glasgow, UK, 2020, pp. 1–8.","chicago":"Bossek, Jakob, Christian Grimme, Günter Rudolph, and Heike Trautmann. “Towards Decision Support in Dynamic Bi-Objective Vehicle Routing.” In <i>Proceedings of the IEEE Congress on Evolutionary Computation (CEC)</i>, 1–8. Glasgow, UK, 2020. <a href=\"https://doi.org/10.1109/CEC48606.2020.9185778\">https://doi.org/10.1109/CEC48606.2020.9185778</a>.","ieee":"J. Bossek, C. Grimme, G. Rudolph, and H. Trautmann, “Towards Decision Support in Dynamic Bi-Objective Vehicle Routing,” in <i>Proceedings of the IEEE Congress on Evolutionary Computation (CEC)</i>, 2020, pp. 1–8, doi: <a href=\"https://doi.org/10.1109/CEC48606.2020.9185778\">10.1109/CEC48606.2020.9185778</a>.","apa":"Bossek, J., Grimme, C., Rudolph, G., &#38; Trautmann, H. (2020). Towards Decision Support in Dynamic Bi-Objective Vehicle Routing. <i>Proceedings of the IEEE Congress on Evolutionary Computation (CEC)</i>, 1–8. <a href=\"https://doi.org/10.1109/CEC48606.2020.9185778\">https://doi.org/10.1109/CEC48606.2020.9185778</a>","bibtex":"@inproceedings{Bossek_Grimme_Rudolph_Trautmann_2020, place={Glasgow, UK}, title={Towards Decision Support in Dynamic Bi-Objective Vehicle Routing}, DOI={<a href=\"https://doi.org/10.1109/CEC48606.2020.9185778\">10.1109/CEC48606.2020.9185778</a>}, booktitle={Proceedings of the IEEE Congress on Evolutionary Computation (CEC)}, author={Bossek, Jakob and Grimme, Christian and Rudolph, Günter and Trautmann, Heike}, year={2020}, pages={1–8} }","ama":"Bossek J, Grimme C, Rudolph G, Trautmann H. Towards Decision Support in Dynamic Bi-Objective Vehicle Routing. In: <i>Proceedings of the IEEE Congress on Evolutionary Computation (CEC)</i>. ; 2020:1–8. doi:<a href=\"https://doi.org/10.1109/CEC48606.2020.9185778\">10.1109/CEC48606.2020.9185778</a>","mla":"Bossek, Jakob, et al. “Towards Decision Support in Dynamic Bi-Objective Vehicle Routing.” <i>Proceedings of the IEEE Congress on Evolutionary Computation (CEC)</i>, 2020, pp. 1–8, doi:<a href=\"https://doi.org/10.1109/CEC48606.2020.9185778\">10.1109/CEC48606.2020.9185778</a>."},"user_id":"15504","doi":"10.1109/CEC48606.2020.9185778","page":"1–8","_id":"46322","language":[{"iso":"eng"}],"date_updated":"2024-06-10T12:02:05Z","year":"2020","title":"Towards Decision Support in Dynamic Bi-Objective Vehicle Routing","status":"public","author":[{"id":"102979","full_name":"Bossek, Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","first_name":"Jakob"},{"full_name":"Grimme, Christian","last_name":"Grimme","first_name":"Christian"},{"full_name":"Rudolph, Günter","first_name":"Günter","last_name":"Rudolph"},{"full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike","orcid":"0000-0002-9788-8282","id":"100740"}]},{"department":[{"_id":"34"},{"_id":"819"}],"type":"conference","place":"Glasgow, UK","date_created":"2023-08-04T07:34:40Z","abstract":[{"lang":"eng","text":"The Traveling-Salesperson-Problem (TSP) is arguably one of the best-known NP-hard combinatorial optimization problems. The two sophisticated heuristic solvers LKH and EAX and respective (restart) variants manage to calculate close-to optimal or even optimal solutions, also for large instances with several thousand nodes in reasonable time. In this work we extend existing benchmarking studies by addressing anytime behaviour of inexact TSP solvers based on empirical runtime distributions leading to an increased understanding of solver behaviour and the respective relation to problem hardness. It turns out that performance ranking of solvers is highly dependent on the focused approximation quality. Insights on intersection points of performances offer huge potential for the construction of hybridized solvers depending on instance features. Moreover, instance features tailored to anytime performance and corresponding performance indicators will highly improve automated algorithm selection models by including comprehensive information on solver quality."}],"citation":{"mla":"Bossek, Jakob, et al. “Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection.” <i>Proceedings of the IEEE Congress on Evolutionary Computation (CEC)</i>, IEEE, 2020, pp. 1–8.","apa":"Bossek, J., Kerschke, P., &#38; Trautmann, H. (2020). Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection. <i>Proceedings of the IEEE Congress on Evolutionary Computation (CEC)</i>, 1–8.","ieee":"J. Bossek, P. Kerschke, and H. Trautmann, “Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection,” in <i>Proceedings of the IEEE Congress on Evolutionary Computation (CEC)</i>, 2020, pp. 1–8.","short":"J. Bossek, P. Kerschke, H. Trautmann, in: Proceedings of the IEEE Congress on Evolutionary Computation (CEC), IEEE, Glasgow, UK, 2020, pp. 1–8.","ama":"Bossek J, Kerschke P, Trautmann H. Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection. In: <i>Proceedings of the IEEE Congress on Evolutionary Computation (CEC)</i>. IEEE; 2020:1–8.","chicago":"Bossek, Jakob, Pascal Kerschke, and Heike Trautmann. “Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection.” In <i>Proceedings of the IEEE Congress on Evolutionary Computation (CEC)</i>, 1–8. Glasgow, UK: IEEE, 2020.","bibtex":"@inproceedings{Bossek_Kerschke_Trautmann_2020, place={Glasgow, UK}, title={Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection}, booktitle={Proceedings of the IEEE Congress on Evolutionary Computation (CEC)}, publisher={IEEE}, author={Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}, year={2020}, pages={1–8} }"},"publication":"Proceedings of the IEEE Congress on Evolutionary Computation (CEC)","user_id":"15504","language":[{"iso":"eng"}],"_id":"46324","publisher":"IEEE","page":"1–8","date_updated":"2024-06-10T12:01:46Z","author":[{"full_name":"Bossek, Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob","id":"102979"},{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"},{"id":"100740","orcid":"0000-0002-9788-8282","first_name":"Heike","last_name":"Trautmann","full_name":"Trautmann, Heike"}],"title":"Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection","status":"public","year":"2020"},{"user_id":"15504","_id":"46323","publisher":"ACM","language":[{"iso":"eng"}],"page":"166–174","date_updated":"2024-06-10T12:01:57Z","author":[{"orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob","full_name":"Bossek, Jakob","id":"102979"},{"full_name":"Grimme, Christian","last_name":"Grimme","first_name":"Christian"},{"last_name":"Trautmann","orcid":"0000-0002-9788-8282","first_name":"Heike","full_name":"Trautmann, Heike","id":"100740"}],"year":"2020","status":"public","title":"Dynamic Bi-Objective Routing of Multiple Vehicles","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","date_created":"2023-08-04T07:33:30Z","place":"Cancun, Mexico","abstract":[{"text":"In practice, e.g. in delivery and service scenarios, Vehicle-Routing-Problems (VRPs) often imply repeated decision making on dynamic customer requests. As in classical VRPs, tours have to be planned short while the number of serviced customers has to be maximized at the same time resulting in a multi-objective problem. Beyond that, however, dynamic requests lead to the need for re-planning of not yet realized tour parts, while already realized tour parts are irreversible. In this paper we study this type of bi-objective dynamic VRP including sequential decision making and concurrent realization of decisions. We adopt a recently proposed Dynamic Evolutionary Multi-Objective Algorithm (DEMOA) for a related VRP problem and extend it to the more realistic (here considered) scenario of multiple vehicles. We empirically show that our DEMOA is competitive with a multi-vehicle offline and clairvoyant variant of the proposed DEMOA as well as with the dynamic single-vehicle approach proposed earlier.","lang":"eng"}],"citation":{"bibtex":"@inproceedings{Bossek_Grimme_Trautmann_2020, place={Cancun, Mexico}, title={Dynamic Bi-Objective Routing of Multiple Vehicles}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20)}, publisher={ACM}, author={Bossek, Jakob and Grimme, Christian and Trautmann, Heike}, year={2020}, pages={166–174} }","ama":"Bossek J, Grimme C, Trautmann H. Dynamic Bi-Objective Routing of Multiple Vehicles. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20)</i>. ACM; 2020:166–174.","mla":"Bossek, Jakob, et al. “Dynamic Bi-Objective Routing of Multiple Vehicles.” <i>Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20)</i>, ACM, 2020, pp. 166–174.","chicago":"Bossek, Jakob, Christian Grimme, and Heike Trautmann. “Dynamic Bi-Objective Routing of Multiple Vehicles.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20)</i>, 166–174. Cancun, Mexico: ACM, 2020.","short":"J. Bossek, C. Grimme, H. Trautmann, in: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20), ACM, Cancun, Mexico, 2020, pp. 166–174.","ieee":"J. Bossek, C. Grimme, and H. Trautmann, “Dynamic Bi-Objective Routing of Multiple Vehicles,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20)</i>, 2020, pp. 166–174.","apa":"Bossek, J., Grimme, C., &#38; Trautmann, H. (2020). Dynamic Bi-Objective Routing of Multiple Vehicles. <i>Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20)</i>, 166–174."},"publication":"Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20)"},{"abstract":[{"lang":"eng","text":"This paper addresses multimodality of multi-objective (MO) optimization landscapes. Contrary to common perception of local optima, according to which they are hindering the progress of optimization algorithms, it will be shown that local efficient sets in a multi-objective setting can assist optimizers in finding global efficient sets. We use sophisticated visualization techniques, which rely on gradient field heatmaps, to highlight those insights into landscape characteristics. Finally, the MO local optimizer MOGSA is introduced, which exploits those observations by sliding down the multi-objective gradient hill and moving along the local efficient sets."}],"publication":"Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO)","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","date_created":"2023-08-04T07:49:08Z","intvolume":"     11411","date_updated":"2023-10-16T13:31:03Z","author":[{"last_name":"Grimme","first_name":"Christian","full_name":"Grimme, Christian"},{"last_name":"Kerschke","first_name":"Pascal","full_name":"Kerschke, Pascal"},{"last_name":"Trautmann","orcid":"0000-0002-9788-8282","first_name":"Heike","full_name":"Trautmann, Heike","id":"100740"}],"year":"2019","title":"Multimodality in Multi-Objective Optimization — More Boon than Bane?","doi":"10.1007/978-3-030-12598-1_11","series_title":"Lecture Notes in Computer Science","language":[{"iso":"eng"}],"citation":{"ama":"Grimme C, Kerschke P, Trautmann H. Multimodality in Multi-Objective Optimization — More Boon than Bane? In: Deb K, Goodman E, Coello CCA, et al., eds. <i>Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO)</i>. Vol 11411. Lecture Notes in Computer Science. Springer; 2019:126–138. doi:<a href=\"https://doi.org/10.1007/978-3-030-12598-1_11\">10.1007/978-3-030-12598-1_11</a>","bibtex":"@inproceedings{Grimme_Kerschke_Trautmann_2019, place={East Lansing, MI, USA}, series={Lecture Notes in Computer Science}, title={Multimodality in Multi-Objective Optimization — More Boon than Bane?}, volume={11411}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-12598-1_11\">10.1007/978-3-030-12598-1_11</a>}, booktitle={Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO)}, publisher={Springer}, author={Grimme, Christian and Kerschke, Pascal and Trautmann, Heike}, editor={Deb, Kalyanmoy and Goodman, Erik and Coello, Coello Carlos A. and Klamroth, Kathrin and Miettinen, Kaisa and Mostaghim, Sanaz and Reed, Patrick}, year={2019}, pages={126–138}, collection={Lecture Notes in Computer Science} }","mla":"Grimme, Christian, et al. “Multimodality in Multi-Objective Optimization — More Boon than Bane?” <i>Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO)</i>, edited by Kalyanmoy Deb et al., vol. 11411, Springer, 2019, pp. 126–138, doi:<a href=\"https://doi.org/10.1007/978-3-030-12598-1_11\">10.1007/978-3-030-12598-1_11</a>.","short":"C. Grimme, P. Kerschke, H. Trautmann, in: K. Deb, E. Goodman, C.C.A. Coello, K. Klamroth, K. Miettinen, S. Mostaghim, P. Reed (Eds.), Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO), Springer, East Lansing, MI, USA, 2019, pp. 126–138.","chicago":"Grimme, Christian, Pascal Kerschke, and Heike Trautmann. “Multimodality in Multi-Objective Optimization — More Boon than Bane?” In <i>Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO)</i>, edited by Kalyanmoy Deb, Erik Goodman, Coello Carlos A. Coello, Kathrin Klamroth, Kaisa Miettinen, Sanaz Mostaghim, and Patrick Reed, 11411:126–138. Lecture Notes in Computer Science. East Lansing, MI, USA: Springer, 2019. <a href=\"https://doi.org/10.1007/978-3-030-12598-1_11\">https://doi.org/10.1007/978-3-030-12598-1_11</a>.","apa":"Grimme, C., Kerschke, P., &#38; Trautmann, H. (2019). Multimodality in Multi-Objective Optimization — More Boon than Bane? In K. Deb, E. Goodman, C. C. A. Coello, K. Klamroth, K. Miettinen, S. Mostaghim, &#38; P. Reed (Eds.), <i>Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO)</i> (Vol. 11411, pp. 126–138). Springer. <a href=\"https://doi.org/10.1007/978-3-030-12598-1_11\">https://doi.org/10.1007/978-3-030-12598-1_11</a>","ieee":"C. Grimme, P. Kerschke, and H. Trautmann, “Multimodality in Multi-Objective Optimization — More Boon than Bane?,” in <i>Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO)</i>, 2019, vol. 11411, pp. 126–138, doi: <a href=\"https://doi.org/10.1007/978-3-030-12598-1_11\">10.1007/978-3-030-12598-1_11</a>."},"place":"East Lansing, MI, USA","status":"public","volume":11411,"editor":[{"full_name":"Deb, Kalyanmoy","first_name":"Kalyanmoy","last_name":"Deb"},{"full_name":"Goodman, Erik","first_name":"Erik","last_name":"Goodman"},{"first_name":"Coello Carlos A.","last_name":"Coello","full_name":"Coello, Coello Carlos A."},{"first_name":"Kathrin","last_name":"Klamroth","full_name":"Klamroth, Kathrin"},{"full_name":"Miettinen, Kaisa","first_name":"Kaisa","last_name":"Miettinen"},{"last_name":"Mostaghim","first_name":"Sanaz","full_name":"Mostaghim, Sanaz"},{"full_name":"Reed, Patrick","first_name":"Patrick","last_name":"Reed"}],"user_id":"15504","_id":"46343","publisher":"Springer","page":"126–138"},{"issue":"1","publication":"Evolutionary Computation (ECJ)","citation":{"chicago":"Kerschke, Pascal, Holger H Hoos, Frank Neumann, and Heike Trautmann. “Automated Algorithm Selection: Survey and Perspectives.” <i>Evolutionary Computation (ECJ)</i> 27, no. 1 (2019): 3–45. <a href=\"https://doi.org/10.1162/evco_a_00242\">https://doi.org/10.1162/evco_a_00242</a>.","short":"P. Kerschke, H.H. Hoos, F. Neumann, H. Trautmann, Evolutionary Computation (ECJ) 27 (2019) 3–45.","ieee":"P. Kerschke, H. H. Hoos, F. Neumann, and H. Trautmann, “Automated Algorithm Selection: Survey and Perspectives,” <i>Evolutionary Computation (ECJ)</i>, vol. 27, no. 1, pp. 3–45, 2019, doi: <a href=\"https://doi.org/10.1162/evco_a_00242\">10.1162/evco_a_00242</a>.","apa":"Kerschke, P., Hoos, H. H., Neumann, F., &#38; Trautmann, H. (2019). Automated Algorithm Selection: Survey and Perspectives. <i>Evolutionary Computation (ECJ)</i>, <i>27</i>(1), 3–45. <a href=\"https://doi.org/10.1162/evco_a_00242\">https://doi.org/10.1162/evco_a_00242</a>","bibtex":"@article{Kerschke_Hoos_Neumann_Trautmann_2019, title={Automated Algorithm Selection: Survey and Perspectives}, volume={27}, DOI={<a href=\"https://doi.org/10.1162/evco_a_00242\">10.1162/evco_a_00242</a>}, number={1}, journal={Evolutionary Computation (ECJ)}, author={Kerschke, Pascal and Hoos, Holger H and Neumann, Frank and Trautmann, Heike}, year={2019}, pages={3–45} }","ama":"Kerschke P, Hoos HH, Neumann F, Trautmann H. Automated Algorithm Selection: Survey and Perspectives. <i>Evolutionary Computation (ECJ)</i>. 2019;27(1):3–45. doi:<a href=\"https://doi.org/10.1162/evco_a_00242\">10.1162/evco_a_00242</a>","mla":"Kerschke, Pascal, et al. “Automated Algorithm Selection: Survey and Perspectives.” <i>Evolutionary Computation (ECJ)</i>, vol. 27, no. 1, 2019, pp. 3–45, doi:<a href=\"https://doi.org/10.1162/evco_a_00242\">10.1162/evco_a_00242</a>."},"abstract":[{"lang":"eng","text":"It has long been observed that for practically any computational problem that has been intensely studied, different instances are best solved using different algorithms. This is particularly pronounced for computationally hard problems, where in most cases, no single algorithm defines the state of the art; instead, there is a set of algorithms with complementary strengths. This performance complementarity can be exploited in various ways, one of which is based on the idea of selecting, from a set of given algorithms, for each problem instance to be solved the one expected to perform best. The task of automatically selecting an algorithm from a given set is known as the per-instance algorithm selection problem and has been intensely studied over the past 15 years, leading to major improvements in the state of the art in solving a growing number of discrete combinatorial problems, including propositional satisfiability and AI planning. Per-instance algorithm selection also shows much promise for boosting performance in solving continuous and mixed discrete/continuous optimisation problems. This survey provides an overview of research in automated algorithm selection, ranging from early and seminal works to recent and promising application areas. Different from earlier work, it covers applications to discrete and continuous problems, and discusses algorithm selection in context with conceptually related approaches, such as algorithm configuration, scheduling, or portfolio selection. Since informative and cheaply computable problem instance features provide the basis for effective per-instance algorithm selection systems, we also provide an overview of such features for discrete and continuous problems. Finally, we provide perspectives on future work in the area and discuss a number of open research challenges."}],"date_created":"2023-08-04T07:50:33Z","type":"journal_article","department":[{"_id":"34"},{"_id":"819"}],"title":"Automated Algorithm Selection: Survey and Perspectives","year":"2019","status":"public","author":[{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"full_name":"Hoos, Holger H","first_name":"Holger H","last_name":"Hoos"},{"full_name":"Neumann, Frank","last_name":"Neumann","first_name":"Frank"},{"id":"100740","first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","full_name":"Trautmann, Heike"}],"date_updated":"2023-10-16T13:31:40Z","intvolume":"        27","page":"3–45","language":[{"iso":"eng"}],"_id":"46345","user_id":"15504","doi":"10.1162/evco_a_00242","volume":27},{"citation":{"bibtex":"@article{Carnein_Trautmann_2019, title={Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms}, volume={61}, number={3}, journal={Business and Information Systems Engineering (BISE)}, author={Carnein, Matthias and Trautmann, Heike}, year={2019}, pages={277–297} }","chicago":"Carnein, Matthias, and Heike Trautmann. “Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms.” <i>Business and Information Systems Engineering (BISE)</i> 61, no. 3 (2019): 277–297.","short":"M. Carnein, H. Trautmann, Business and Information Systems Engineering (BISE) 61 (2019) 277–297.","ama":"Carnein M, Trautmann H. Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms. <i>Business and Information Systems Engineering (BISE)</i>. 2019;61(3):277–297.","ieee":"M. Carnein and H. Trautmann, “Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms,” <i>Business and Information Systems Engineering (BISE)</i>, vol. 61, no. 3, pp. 277–297, 2019.","apa":"Carnein, M., &#38; Trautmann, H. (2019). Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms. <i>Business and Information Systems Engineering (BISE)</i>, <i>61</i>(3), 277–297.","mla":"Carnein, Matthias, and Heike Trautmann. “Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms.” <i>Business and Information Systems Engineering (BISE)</i>, vol. 61, no. 3, 2019, pp. 277–297."},"publication":"Business and Information Systems Engineering (BISE)","issue":"3","abstract":[{"lang":"eng","text":"Analyzing data streams has received considerable attention over the past decades due to the widespread usage of sensors, social media and other streaming data sources. A core research area in this field is stream clustering which aims to recognize patterns in an unordered, infinite and evolving stream of observations. Clustering can be a crucial support in decision making, since it aims for an optimized aggregated representation of a continuous data stream over time and allows to identify patterns in large and high-dimensional data. A multitude of algorithms and approaches has been developed that are able to find and maintain clusters over time in the challenging streaming scenario. This survey explores, summarizes and categorizes a total of 51 stream clustering algorithms and identifies core research threads over the past decades. In particular, it identifies categories of algorithms based on distance thresholds, density grids and statistical models as well as algorithms for high dimensional data. Furthermore, it discusses applications scenarios, available software and how to configure stream clustering algorithms. This survey is considerably more extensive than comparable studies, more up-to-date and highlights how concepts are interrelated and have been developed over time."}],"date_created":"2023-08-04T07:49:47Z","department":[{"_id":"34"},{"_id":"819"}],"type":"journal_article","author":[{"last_name":"Carnein","first_name":"Matthias","full_name":"Carnein, Matthias"},{"first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike","id":"100740"}],"status":"public","year":"2019","title":"Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms","intvolume":"        61","date_updated":"2023-10-16T13:31:21Z","language":[{"iso":"eng"}],"_id":"46344","page":"277–297","volume":61,"user_id":"15504"},{"date_updated":"2023-10-16T13:29:53Z","author":[{"first_name":"Matthias","last_name":"Carnein","full_name":"Carnein, Matthias"},{"last_name":"Homann","first_name":"Leschek","full_name":"Homann, Leschek"},{"full_name":"Trautmann, Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","first_name":"Heike","id":"100740"},{"full_name":"Vossen, Gottfried","last_name":"Vossen","first_name":"Gottfried"}],"title":"A Recommender System Based on Omni-Channel Customer Data","year":"2019","status":"public","user_id":"15504","language":[{"iso":"eng"}],"_id":"46340","page":"65–74","abstract":[{"lang":"eng","text":"Recommender systems aim to provide personalized suggestions to customers which products to buy or services to consume. They can help to increase sales by helping customers discover new and relevant products. Traditionally, recommender systems use the purchase history of a customer, e.g., the purchased quantity or properties of the items. While this allows to build personalized recommendations, it is a very limited view of the problem. Nowadays, extensive information about customers and their personal preferences is available which goes far beyond their purchase behaviour. For example, customers reveal their preferences in social media, by their browsing habits and online search behaviour or their interest in specific newsletters. In this paper, we investigate how information from different sources and channels can be collected and incorporated into the recommendation process. We demonstrate this, based on a real-life case study of a retailer with several million transactions. We discuss how to employ a recommender system in this scenario, evaluate various recommendation strategies and describe how to incorporate information from different sources and channels, both internal and external. Our results show that the recommendations can be better tailored to the personal preferences of customers."}],"citation":{"mla":"Carnein, Matthias, et al. “A Recommender System Based on Omni-Channel Customer Data.” <i>Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19)</i>, 2019, pp. 65–74.","bibtex":"@inproceedings{Carnein_Homann_Trautmann_Vossen_2019, place={Moscow, Russia}, title={A Recommender System Based on Omni-Channel Customer Data}, booktitle={Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19)}, author={Carnein, Matthias and Homann, Leschek and Trautmann, Heike and Vossen, Gottfried}, year={2019}, pages={65–74} }","ama":"Carnein M, Homann L, Trautmann H, Vossen G. A Recommender System Based on Omni-Channel Customer Data. In: <i>Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19)</i>. ; 2019:65–74.","ieee":"M. Carnein, L. Homann, H. Trautmann, and G. Vossen, “A Recommender System Based on Omni-Channel Customer Data,” in <i>Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19)</i>, 2019, pp. 65–74.","apa":"Carnein, M., Homann, L., Trautmann, H., &#38; Vossen, G. (2019). A Recommender System Based on Omni-Channel Customer Data. <i>Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19)</i>, 65–74.","short":"M. Carnein, L. Homann, H. Trautmann, G. Vossen, in: Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19), Moscow, Russia, 2019, pp. 65–74.","chicago":"Carnein, Matthias, Leschek Homann, Heike Trautmann, and Gottfried Vossen. “A Recommender System Based on Omni-Channel Customer Data.” In <i>Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19)</i>, 65–74. Moscow, Russia, 2019."},"publication":"Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19)","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","date_created":"2023-08-04T07:46:20Z","place":"Moscow, Russia"}]