[{"publication":"Parallel Problem Solving from Nature (PPSN XVI)","type":"conference","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."}],"status":"public","_id":"48852","department":[{"_id":"819"}],"user_id":"102979","keyword":["Evolutionary algorithms","Node weight dependent TSP","Traveling Thief Problem"],"extern":"1","language":[{"iso":"eng"}],"publication_identifier":{"isbn":["978-3-030-58111-4"]},"publication_status":"published","place":"Berlin, Heidelberg","year":"2020","page":"346–359","citation":{"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>","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>.","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>.","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>","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} }","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."},"date_updated":"2023-12-13T10:44:54Z","publisher":"Springer-Verlag","author":[{"first_name":"Jakob","full_name":"Bossek, Jakob","id":"102979","last_name":"Bossek","orcid":"0000-0002-4121-4668"},{"first_name":"Aneta","last_name":"Neumann","full_name":"Neumann, Aneta"},{"first_name":"Frank","full_name":"Neumann, Frank","last_name":"Neumann"}],"date_created":"2023-11-14T15:58:54Z","title":"Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions","doi":"10.1007/978-3-030-58112-1_24"},{"publication_status":"published","year":"2020","place":"Glasgow, United Kingdom","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>","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>.","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} }","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.","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>","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>."},"page":"1–8","date_updated":"2023-12-13T10:44:17Z","publisher":"IEEE Press","date_created":"2023-11-14T15:58:53Z","author":[{"first_name":"Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","full_name":"Bossek, Jakob","id":"102979"},{"first_name":"Christian","last_name":"Grimme","full_name":"Grimme, Christian"},{"last_name":"Rudolph","full_name":"Rudolph, Günter","first_name":"Günter"},{"first_name":"Heike","last_name":"Trautmann","full_name":"Trautmann, Heike"}],"title":"Towards Decision Support in Dynamic Bi-Objective Vehicle Routing","doi":"10.1109/CEC48606.2020.9185778","type":"conference","publication":"2020 IEEE Congress on Evolutionary Computation (CEC)","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"}],"status":"public","_id":"48846","user_id":"102979","department":[{"_id":"819"}],"language":[{"iso":"eng"}],"extern":"1"},{"doi":"10.1145/3377930.3389844","author":[{"full_name":"Do, Anh Viet","last_name":"Do","first_name":"Anh Viet"},{"first_name":"Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","id":"102979","full_name":"Bossek, Jakob"},{"full_name":"Neumann, Aneta","last_name":"Neumann","first_name":"Aneta"},{"first_name":"Frank","full_name":"Neumann, Frank","last_name":"Neumann"}],"date_updated":"2023-12-13T10:48:50Z","page":"681–689","citation":{"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>","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>.","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} }","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.","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>","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>."},"place":"New York, NY, USA","publication_identifier":{"isbn":["978-1-4503-7128-5"]},"extern":"1","department":[{"_id":"819"}],"user_id":"102979","series_title":"GECCO’20","_id":"48879","status":"public","type":"conference","title":"Evolving Diverse Sets of Tours for the Travelling Salesperson Problem","date_created":"2023-11-14T15:58:58Z","publisher":"Association for Computing Machinery","year":"2020","language":[{"iso":"eng"}],"keyword":["diversity maximisation","evolutionary algorithms","travelling salesperson problem"],"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"}],"publication":"Proceedings of the 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","date_created":"2023-11-14T15:59:00Z","author":[{"last_name":"Roostapour","full_name":"Roostapour, Vahid","first_name":"Vahid"},{"first_name":"Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","id":"102979","full_name":"Bossek, Jakob"},{"first_name":"Frank","last_name":"Neumann","full_name":"Neumann, Frank"}],"publisher":"Association for Computing Machinery","date_updated":"2023-12-13T10:49:38Z","citation":{"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>.","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} }","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>","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>","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>.","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>."},"page":"551–559","place":"New York, NY, USA","year":"2020","publication_identifier":{"isbn":["978-1-4503-7128-5"]},"language":[{"iso":"eng"}],"extern":"1","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":[{"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"}],"type":"conference","publication":"Proceedings of the 2020 Genetic and Evolutionary Computation Conference"},{"type":"conference","publication":"Parallel Problem Solving from {Nature} (PPSN XVI)","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."}],"status":"public","_id":"48897","user_id":"102979","department":[{"_id":"819"}],"keyword":["Automated algorithm selection","Deep learning","Feature-based approaches","Traveling Salesperson Problem"],"language":[{"iso":"eng"}],"extern":"1","publication_identifier":{"isbn":["978-3-030-58111-4"]},"year":"2020","place":"Berlin, Heidelberg","citation":{"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.","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} }","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>.","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>","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>.","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>.","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>"},"page":"48–64","publisher":"Springer-Verlag","date_updated":"2023-12-13T10:49:45Z","author":[{"first_name":"Moritz","full_name":"Seiler, Moritz","last_name":"Seiler"},{"first_name":"Janina","full_name":"Pohl, Janina","last_name":"Pohl"},{"orcid":"0000-0002-4121-4668","last_name":"Bossek","full_name":"Bossek, Jakob","id":"102979","first_name":"Jakob"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"first_name":"Heike","last_name":"Trautmann","full_name":"Trautmann, Heike"}],"date_created":"2023-11-14T15:59:00Z","title":"Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem","doi":"10.1007/978-3-030-58112-1_4"},{"publication":"Applied Soft Computing","type":"journal_article","status":"public","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 \\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."}],"department":[{"_id":"819"}],"user_id":"102979","_id":"48848","language":[{"iso":"eng"}],"keyword":["Algorithm selection","Combinatorial optimization","Multi-objective optimization","Performance measurement","Traveling Salesperson Problem"],"issue":"C","publication_identifier":{"issn":["1568-4946"]},"intvolume":"        88","citation":{"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>.","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} }","short":"J. Bossek, P. Kerschke, H. Trautmann, Applied Soft Computing 88 (2020).","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>","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>.","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>."},"year":"2020","volume":88,"author":[{"full_name":"Bossek, Jakob","id":"102979","orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob"},{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"},{"last_name":"Trautmann","full_name":"Trautmann, Heike","first_name":"Heike"}],"date_created":"2023-11-14T15:58:53Z","date_updated":"2023-12-13T10:52:17Z","doi":"10.1016/j.asoc.2019.105901","title":"A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms"},{"date_updated":"2023-12-13T10:52:24Z","date_created":"2023-11-14T15:58:51Z","author":[{"first_name":"Thomas","full_name":"Bartz-Beielstein, Thomas","last_name":"Bartz-Beielstein"},{"first_name":"Carola","last_name":"Doerr","full_name":"Doerr, Carola"},{"first_name":"Daan","last_name":"van den Berg","full_name":"van den Berg, Daan"},{"last_name":"Bossek","orcid":"0000-0002-4121-4668","id":"102979","full_name":"Bossek, Jakob","first_name":"Jakob"},{"last_name":"Chandrasekaran","full_name":"Chandrasekaran, Sowmya","first_name":"Sowmya"},{"full_name":"Eftimov, Tome","last_name":"Eftimov","first_name":"Tome"},{"full_name":"Fischbach, Andreas","last_name":"Fischbach","first_name":"Andreas"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"first_name":"William La","last_name":"Cava","full_name":"Cava, William La"},{"full_name":"Lopez-Ibanez, Manuel","last_name":"Lopez-Ibanez","first_name":"Manuel"},{"full_name":"Malan, Katherine M.","last_name":"Malan","first_name":"Katherine M."},{"first_name":"Jason H.","full_name":"Moore, Jason H.","last_name":"Moore"},{"last_name":"Naujoks","full_name":"Naujoks, Boris","first_name":"Boris"},{"first_name":"Patryk","full_name":"Orzechowski, Patryk","last_name":"Orzechowski"},{"last_name":"Volz","full_name":"Volz, Vanessa","first_name":"Vanessa"},{"full_name":"Wagner, Markus","last_name":"Wagner","first_name":"Markus"},{"last_name":"Weise","full_name":"Weise, Thomas","first_name":"Thomas"}],"title":"Benchmarking in Optimization: Best Practice and Open Issues","year":"2020","citation":{"ieee":"T. Bartz-Beielstein <i>et al.</i>, “Benchmarking in Optimization: Best Practice and Open Issues,” <i>Corr</i>, 2020.","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.","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.","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} }","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).","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>."},"_id":"48836","user_id":"102979","department":[{"_id":"819"}],"language":[{"iso":"eng"}],"type":"journal_article","publication":"Corr","status":"public"},{"year":"2020","place":"Leiden, The Netherlands","citation":{"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.","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} }","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>.","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>","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>.","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>.","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>"},"page":"48–64","date_updated":"2024-06-10T11:57:13Z","author":[{"first_name":"Moritz","last_name":"Seiler","id":"105520","full_name":"Seiler, Moritz"},{"first_name":"Janina","full_name":"Pohl, Janina","last_name":"Pohl"},{"first_name":"Jakob","id":"102979","full_name":"Bossek, Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","full_name":"Trautmann, Heike","id":"100740"}],"date_created":"2023-08-04T07:39:05Z","title":"Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem","doi":"10.1007/978-3-030-58112-1_4","type":"conference","publication":"Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI)","editor":[{"first_name":"Thomas","full_name":"Bäck, Thomas","last_name":"Bäck"},{"full_name":"Preuss, Mike","last_name":"Preuss","first_name":"Mike"},{"full_name":"Deutz, André","last_name":"Deutz","first_name":"André"},{"first_name":"Hao","last_name":"Wang","full_name":"Wang, Hao"},{"first_name":"Carola","last_name":"Doerr","full_name":"Doerr, Carola"},{"first_name":"Michael","last_name":"Emmerich","full_name":"Emmerich, Michael"},{"first_name":"Heike","last_name":"Trautmann","full_name":"Trautmann, Heike"}],"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"}],"status":"public","_id":"46330","user_id":"15504","department":[{"_id":"34"},{"_id":"819"}],"language":[{"iso":"eng"}]},{"intvolume":"        88","page":"105901","citation":{"short":"J. Bossek, P. Kerschke, H. Trautmann, Applied Soft Computing 88 (2020) 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>.","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} }","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>","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>.","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>"},"year":"2020","publication_identifier":{"issn":["1568-4946"]},"doi":"https://doi.org/10.1016/j.asoc.2019.105901","title":"A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms","volume":88,"date_created":"2023-08-04T07:42:26Z","author":[{"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","full_name":"Trautmann, Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","first_name":"Heike"}],"date_updated":"2024-06-10T12:00:46Z","status":"public","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","type":"journal_article","language":[{"iso":"eng"}],"keyword":["Algorithm selection","Multi-objective optimization","Performance measurement","Combinatorial optimization","Traveling Salesperson Problem"],"department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","_id":"46334"},{"publication":"Proceedings of the IEEE Congress on Evolutionary Computation (CEC)","type":"conference","status":"public","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 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.","lang":"eng"}],"department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","_id":"46322","language":[{"iso":"eng"}],"page":"1–8","citation":{"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>","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>","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>.","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} }","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."},"year":"2020","place":"Glasgow, UK","author":[{"id":"102979","full_name":"Bossek, Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","first_name":"Jakob"},{"first_name":"Christian","full_name":"Grimme, Christian","last_name":"Grimme"},{"last_name":"Rudolph","full_name":"Rudolph, Günter","first_name":"Günter"},{"orcid":"0000-0002-9788-8282","last_name":"Trautmann","full_name":"Trautmann, Heike","id":"100740","first_name":"Heike"}],"date_created":"2023-08-04T07:32:36Z","date_updated":"2024-06-10T12:02:05Z","doi":"10.1109/CEC48606.2020.9185778","title":"Towards Decision Support in Dynamic Bi-Objective Vehicle Routing"},{"place":"Glasgow, UK","year":"2020","page":"1–8","citation":{"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.","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.","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.","short":"J. Bossek, P. Kerschke, H. Trautmann, in: Proceedings of the IEEE Congress on Evolutionary Computation (CEC), IEEE, Glasgow, UK, 2020, pp. 1–8.","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} }","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.","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."},"title":"Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection","publisher":"IEEE","date_updated":"2024-06-10T12:01:46Z","author":[{"first_name":"Jakob","id":"102979","full_name":"Bossek, Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike","id":"100740"}],"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."}],"status":"public","publication":"Proceedings of the IEEE Congress on Evolutionary Computation (CEC)","type":"conference","language":[{"iso":"eng"}],"_id":"46324","department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504"},{"publisher":"ACM","date_updated":"2024-06-10T12:01:57Z","date_created":"2023-08-04T07:33:30Z","author":[{"last_name":"Bossek","orcid":"0000-0002-4121-4668","id":"102979","full_name":"Bossek, Jakob","first_name":"Jakob"},{"full_name":"Grimme, Christian","last_name":"Grimme","first_name":"Christian"},{"full_name":"Trautmann, Heike","id":"100740","orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike"}],"title":"Dynamic Bi-Objective Routing of Multiple Vehicles","place":"Cancun, Mexico","year":"2020","page":"166–174","citation":{"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.","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} }","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.","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.","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.","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.","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."},"_id":"46323","department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","language":[{"iso":"eng"}],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20)","type":"conference","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"}],"status":"public"},{"editor":[{"last_name":"Deb","full_name":"Deb, Kalyanmoy","first_name":"Kalyanmoy"},{"full_name":"Goodman, Erik","last_name":"Goodman","first_name":"Erik"},{"last_name":"Coello Coello","full_name":"Coello Coello, Carlos A.","first_name":"Carlos A."},{"full_name":"Klamroth, Kathrin","last_name":"Klamroth","first_name":"Kathrin"},{"last_name":"Miettinen","full_name":"Miettinen, Kaisa","first_name":"Kaisa"},{"last_name":"Mostaghim","full_name":"Mostaghim, Sanaz","first_name":"Sanaz"},{"last_name":"Reed","full_name":"Reed, Patrick","first_name":"Patrick"}],"status":"public","type":"conference","extern":"1","_id":"48841","department":[{"_id":"819"}],"user_id":"102979","series_title":"Lecture Notes in Computer Science","place":"Cham","page":"516–528","citation":{"mla":"Bossek, Jakob, et al. “Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm.” <i>Evolutionary Multi-Criterion Optimization (EMO)</i>, edited by Kalyanmoy Deb et al., Springer International Publishing, 2019, pp. 516–528, doi:<a href=\"https://doi.org/10.1007/978-3-030-12598-1_41\">10.1007/978-3-030-12598-1_41</a>.","short":"J. Bossek, C. Grimme, S. Meisel, G. Rudolph, H. Trautmann, in: K. Deb, E. Goodman, C.A. Coello Coello, K. Klamroth, K. Miettinen, S. Mostaghim, P. Reed (Eds.), Evolutionary Multi-Criterion Optimization (EMO), Springer International Publishing, Cham, 2019, pp. 516–528.","bibtex":"@inproceedings{Bossek_Grimme_Meisel_Rudolph_Trautmann_2019, place={Cham}, series={Lecture Notes in Computer Science}, title={Bi-Objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-12598-1_41\">10.1007/978-3-030-12598-1_41</a>}, booktitle={Evolutionary Multi-Criterion Optimization (EMO)}, publisher={Springer International Publishing}, author={Bossek, Jakob and Grimme, Christian and Meisel, Stephan and Rudolph, Günter 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={516–528}, collection={Lecture Notes in Computer Science} }","apa":"Bossek, J., Grimme, C., Meisel, S., Rudolph, G., &#38; Trautmann, H. (2019). Bi-Objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm. In K. Deb, E. Goodman, C. A. Coello Coello, K. Klamroth, K. Miettinen, S. Mostaghim, &#38; P. Reed (Eds.), <i>Evolutionary Multi-Criterion Optimization (EMO)</i> (pp. 516–528). Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-030-12598-1_41\">https://doi.org/10.1007/978-3-030-12598-1_41</a>","ama":"Bossek J, Grimme C, Meisel S, Rudolph G, Trautmann H. Bi-Objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm. In: Deb K, Goodman E, Coello Coello CA, et al., eds. <i>Evolutionary Multi-Criterion Optimization (EMO)</i>. Lecture Notes in Computer Science. Springer International Publishing; 2019:516–528. doi:<a href=\"https://doi.org/10.1007/978-3-030-12598-1_41\">10.1007/978-3-030-12598-1_41</a>","ieee":"J. Bossek, C. Grimme, S. Meisel, G. Rudolph, and H. Trautmann, “Bi-Objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm,” in <i>Evolutionary Multi-Criterion Optimization (EMO)</i>, 2019, pp. 516–528, doi: <a href=\"https://doi.org/10.1007/978-3-030-12598-1_41\">10.1007/978-3-030-12598-1_41</a>.","chicago":"Bossek, Jakob, Christian Grimme, Stephan Meisel, Günter Rudolph, and Heike Trautmann. “Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm.” In <i>Evolutionary Multi-Criterion Optimization (EMO)</i>, edited by Kalyanmoy Deb, Erik Goodman, Carlos A. Coello Coello, Kathrin Klamroth, Kaisa Miettinen, Sanaz Mostaghim, and Patrick Reed, 516–528. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2019. <a href=\"https://doi.org/10.1007/978-3-030-12598-1_41\">https://doi.org/10.1007/978-3-030-12598-1_41</a>."},"publication_identifier":{"isbn":["978-3-030-12598-1"]},"publication_status":"published","doi":"10.1007/978-3-030-12598-1_41","date_updated":"2023-12-13T10:43:07Z","author":[{"full_name":"Bossek, Jakob","id":"102979","last_name":"Bossek","orcid":"0000-0002-4121-4668","first_name":"Jakob"},{"first_name":"Christian","last_name":"Grimme","full_name":"Grimme, Christian"},{"first_name":"Stephan","full_name":"Meisel, Stephan","last_name":"Meisel"},{"full_name":"Rudolph, Günter","last_name":"Rudolph","first_name":"Günter"},{"full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike"}],"abstract":[{"lang":"eng","text":"We tackle a bi-objective dynamic orienteering problem where customer requests arise as time passes by. The goal is to minimize the tour length traveled by a single delivery vehicle while simultaneously keeping the number of dismissed dynamic customers to a minimum. We propose a dynamic Evolutionary Multi-Objective Algorithm which is grounded on insights gained from a previous series of work on an a-posteriori version of the problem, where all request times are known in advance. In our experiments, we simulate different decision maker strategies and evaluate the development of the Pareto-front approximations on exemplary problem instances. It turns out, that despite severely reduced computational budget and no oracle-knowledge of request times the dynamic EMOA is capable of producing approximations which partially dominate the results of the a-posteriori EMOA and dynamic integer linear programming strategies."}],"publication":"Evolutionary Multi-Criterion Optimization (EMO)","keyword":["Combinatorial optimization","Dynamic optimization","Metaheuristics","Multi-objective optimization","Vehicle routing"],"language":[{"iso":"eng"}],"year":"2019","title":"Bi-Objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm","publisher":"Springer International Publishing","date_created":"2023-11-14T15:58:52Z"},{"publication":"Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms","abstract":[{"lang":"eng","text":"Evolutionary algorithms have successfully been applied to evolve problem instances that exhibit a significant difference in performance for a given algorithm or a pair of algorithms inter alia for the Traveling Salesperson Problem (TSP). Creating a large variety of instances is crucial for successful applications in the blooming field of algorithm selection. In this paper, we introduce new and creative mutation operators for evolving instances of the TSP. We show that adopting those operators in an evolutionary algorithm allows for the generation of benchmark sets with highly desirable properties: (1) novelty by clear visual distinction to established benchmark sets in the field, (2) visual and quantitative diversity in the space of TSP problem characteristics, and (3) significant performance differences with respect to the restart versions of heuristic state-of-the-art TSP solvers EAX and LKH. The important aspect of diversity is addressed and achieved solely by the proposed mutation operators and not enforced by explicit diversity preservation."}],"language":[{"iso":"eng"}],"keyword":["benchmarking","instance features","optimization","problem generation","traveling salesperson problem"],"year":"2019","date_created":"2023-11-14T15:58:52Z","publisher":"Association for Computing Machinery","title":"Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators","type":"conference","status":"public","user_id":"102979","series_title":"FOGA ’19","department":[{"_id":"819"}],"_id":"48842","extern":"1","publication_status":"published","publication_identifier":{"isbn":["978-1-4503-6254-2"]},"citation":{"apa":"Bossek, J., Kerschke, P., Neumann, A., Wagner, M., Neumann, F., &#38; Trautmann, H. (2019). Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators. <i>Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, 58–71. <a href=\"https://doi.org/10.1145/3299904.3340307\">https://doi.org/10.1145/3299904.3340307</a>","short":"J. Bossek, P. Kerschke, A. Neumann, M. Wagner, F. Neumann, H. Trautmann, in: Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, Association for Computing Machinery, New York, NY, USA, 2019, pp. 58–71.","mla":"Bossek, Jakob, et al. “Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators.” <i>Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, Association for Computing Machinery, 2019, pp. 58–71, doi:<a href=\"https://doi.org/10.1145/3299904.3340307\">10.1145/3299904.3340307</a>.","bibtex":"@inproceedings{Bossek_Kerschke_Neumann_Wagner_Neumann_Trautmann_2019, place={New York, NY, USA}, series={FOGA ’19}, title={Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators}, DOI={<a href=\"https://doi.org/10.1145/3299904.3340307\">10.1145/3299904.3340307</a>}, booktitle={Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Kerschke, Pascal and Neumann, Aneta and Wagner, Markus and Neumann, Frank and Trautmann, Heike}, year={2019}, pages={58–71}, collection={FOGA ’19} }","ama":"Bossek J, Kerschke P, Neumann A, Wagner M, Neumann F, Trautmann H. Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators. In: <i>Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>. FOGA ’19. Association for Computing Machinery; 2019:58–71. doi:<a href=\"https://doi.org/10.1145/3299904.3340307\">10.1145/3299904.3340307</a>","ieee":"J. Bossek, P. Kerschke, A. Neumann, M. Wagner, F. Neumann, and H. Trautmann, “Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators,” in <i>Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, 2019, pp. 58–71, doi: <a href=\"https://doi.org/10.1145/3299904.3340307\">10.1145/3299904.3340307</a>.","chicago":"Bossek, Jakob, Pascal Kerschke, Aneta Neumann, Markus Wagner, Frank Neumann, and Heike Trautmann. “Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators.” In <i>Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, 58–71. FOGA ’19. New York, NY, USA: Association for Computing Machinery, 2019. <a href=\"https://doi.org/10.1145/3299904.3340307\">https://doi.org/10.1145/3299904.3340307</a>."},"page":"58–71","place":"New York, NY, USA","author":[{"last_name":"Bossek","orcid":"0000-0002-4121-4668","id":"102979","full_name":"Bossek, Jakob","first_name":"Jakob"},{"first_name":"Pascal","full_name":"Kerschke, Pascal","last_name":"Kerschke"},{"first_name":"Aneta","last_name":"Neumann","full_name":"Neumann, Aneta"},{"first_name":"Markus","full_name":"Wagner, Markus","last_name":"Wagner"},{"last_name":"Neumann","full_name":"Neumann, Frank","first_name":"Frank"},{"last_name":"Trautmann","full_name":"Trautmann, Heike","first_name":"Heike"}],"date_updated":"2023-12-13T10:42:57Z","doi":"10.1145/3299904.3340307"},{"year":"2019","title":"Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring","date_created":"2023-11-14T15:58:52Z","publisher":"Association for Computing Machinery","abstract":[{"lang":"eng","text":"We contribute to the theoretical understanding of randomized search heuristics for dynamic problems. We consider the classical graph coloring problem and investigate the dynamic setting where edges are added to the current graph. We then analyze the expected time for randomized search heuristics to recompute high quality solutions. This includes the (1+1) EA and RLS in a setting where the number of colors is bounded and we are minimizing the number of conflicts as well as iterated local search algorithms that use an unbounded color palette and aim to use the smallest colors and - as a consequence - the smallest number of colors. We identify classes of bipartite graphs where reoptimization is as hard as or even harder than optimization from scratch, i. e. starting with a random initialization. Even adding a single edge can lead to hard symmetry problems. However, graph classes that are hard for one algorithm turn out to be easy for others. In most cases our bounds show that reoptimization is faster than optimizing from scratch. Furthermore, we show how to speed up computations by using problem specific operators concentrating on parts of the graph where changes have occurred."}],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference","language":[{"iso":"eng"}],"keyword":["dynamic optimization","evolutionary algorithms","running time analysis","theory"],"citation":{"short":"J. Bossek, F. Neumann, P. Peng, D. Sudholt, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2019, pp. 1443–1451.","mla":"Bossek, Jakob, et al. “Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2019, pp. 1443–1451, doi:<a href=\"https://doi.org/10.1145/3321707.3321792\">10.1145/3321707.3321792</a>.","bibtex":"@inproceedings{Bossek_Neumann_Peng_Sudholt_2019, place={New York, NY, USA}, series={GECCO ’19}, title={Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring}, DOI={<a href=\"https://doi.org/10.1145/3321707.3321792\">10.1145/3321707.3321792</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Neumann, Frank and Peng, Pan and Sudholt, Dirk}, year={2019}, pages={1443–1451}, collection={GECCO ’19} }","apa":"Bossek, J., Neumann, F., Peng, P., &#38; Sudholt, D. (2019). Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 1443–1451. <a href=\"https://doi.org/10.1145/3321707.3321792\">https://doi.org/10.1145/3321707.3321792</a>","ama":"Bossek J, Neumann F, Peng P, Sudholt D. Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO ’19. Association for Computing Machinery; 2019:1443–1451. doi:<a href=\"https://doi.org/10.1145/3321707.3321792\">10.1145/3321707.3321792</a>","ieee":"J. Bossek, F. Neumann, P. Peng, and D. Sudholt, “Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2019, pp. 1443–1451, doi: <a href=\"https://doi.org/10.1145/3321707.3321792\">10.1145/3321707.3321792</a>.","chicago":"Bossek, Jakob, Frank Neumann, Pan Peng, and Dirk Sudholt. “Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 1443–1451. GECCO ’19. New York, NY, USA: Association for Computing Machinery, 2019. <a href=\"https://doi.org/10.1145/3321707.3321792\">https://doi.org/10.1145/3321707.3321792</a>."},"page":"1443–1451","place":"New York, NY, USA","publication_status":"published","publication_identifier":{"isbn":["978-1-4503-6111-8"]},"doi":"10.1145/3321707.3321792","author":[{"full_name":"Bossek, Jakob","id":"102979","last_name":"Bossek","orcid":"0000-0002-4121-4668","first_name":"Jakob"},{"last_name":"Neumann","full_name":"Neumann, Frank","first_name":"Frank"},{"first_name":"Pan","last_name":"Peng","full_name":"Peng, Pan"},{"first_name":"Dirk","full_name":"Sudholt, Dirk","last_name":"Sudholt"}],"date_updated":"2023-12-13T10:42:37Z","status":"public","type":"conference","extern":"1","series_title":"GECCO ’19","user_id":"102979","department":[{"_id":"819"}],"_id":"48843"},{"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","keyword":["biased mutation","combinatorial optimization","minimum spanning tree","multi-objective optimization"],"language":[{"iso":"eng"}],"year":"2019","title":"On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem","publisher":"Association for Computing Machinery","date_created":"2023-11-14T15:58:52Z","status":"public","type":"conference","extern":"1","_id":"48840","user_id":"102979","series_title":"GECCO ’19","department":[{"_id":"819"}],"place":"New York, NY, USA","citation":{"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.","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} }","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>.","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>","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>"},"page":"516–523","publication_status":"published","publication_identifier":{"isbn":["978-1-4503-6111-8"]},"doi":"10.1145/3321707.3321818","date_updated":"2023-12-13T10:42:24Z","author":[{"first_name":"Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","id":"102979","full_name":"Bossek, Jakob"},{"last_name":"Grimme","full_name":"Grimme, Christian","first_name":"Christian"},{"full_name":"Neumann, Frank","last_name":"Neumann","first_name":"Frank"}]},{"department":[{"_id":"819"}],"series_title":"Lecture Notes in Computer Science","user_id":"102979","_id":"48858","extern":"1","type":"conference","status":"public","editor":[{"full_name":"Battiti, Roberto","last_name":"Battiti","first_name":"Roberto"},{"first_name":"Mauro","full_name":"Brunato, Mauro","last_name":"Brunato"},{"last_name":"Kotsireas","full_name":"Kotsireas, Ilias","first_name":"Ilias"},{"full_name":"Pardalos, Panos M.","last_name":"Pardalos","first_name":"Panos M."}],"author":[{"last_name":"Bossek","orcid":"0000-0002-4121-4668","id":"102979","full_name":"Bossek, Jakob","first_name":"Jakob"},{"first_name":"Christian","last_name":"Grimme","full_name":"Grimme, Christian"}],"date_updated":"2023-12-13T10:44:44Z","doi":"10.1007/978-3-030-05348-2_17","publication_identifier":{"isbn":["978-3-030-05348-2"]},"publication_status":"published","page":"184–198","citation":{"ieee":"J. Bossek and C. Grimme, “Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-criteria Shortest Path Problems,” in <i>Learning and Intelligent Optimization</i>, 2019, pp. 184–198, doi: <a href=\"https://doi.org/10.1007/978-3-030-05348-2_17\">10.1007/978-3-030-05348-2_17</a>.","chicago":"Bossek, Jakob, and Christian Grimme. “Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-Criteria Shortest Path Problems.” In <i>Learning and Intelligent Optimization</i>, edited by Roberto Battiti, Mauro Brunato, Ilias Kotsireas, and Panos M. Pardalos, 184–198. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2019. <a href=\"https://doi.org/10.1007/978-3-030-05348-2_17\">https://doi.org/10.1007/978-3-030-05348-2_17</a>.","ama":"Bossek J, Grimme C. Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-criteria Shortest Path Problems. In: Battiti R, Brunato M, Kotsireas I, Pardalos PM, eds. <i>Learning and Intelligent Optimization</i>. Lecture Notes in Computer Science. Springer International Publishing; 2019:184–198. doi:<a href=\"https://doi.org/10.1007/978-3-030-05348-2_17\">10.1007/978-3-030-05348-2_17</a>","apa":"Bossek, J., &#38; Grimme, C. (2019). Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-criteria Shortest Path Problems. In R. Battiti, M. Brunato, I. Kotsireas, &#38; P. M. Pardalos (Eds.), <i>Learning and Intelligent Optimization</i> (pp. 184–198). Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-030-05348-2_17\">https://doi.org/10.1007/978-3-030-05348-2_17</a>","mla":"Bossek, Jakob, and Christian Grimme. “Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-Criteria Shortest Path Problems.” <i>Learning and Intelligent Optimization</i>, edited by Roberto Battiti et al., Springer International Publishing, 2019, pp. 184–198, doi:<a href=\"https://doi.org/10.1007/978-3-030-05348-2_17\">10.1007/978-3-030-05348-2_17</a>.","short":"J. Bossek, C. Grimme, in: R. Battiti, M. Brunato, I. Kotsireas, P.M. Pardalos (Eds.), Learning and Intelligent Optimization, Springer International Publishing, Cham, 2019, pp. 184–198.","bibtex":"@inproceedings{Bossek_Grimme_2019, place={Cham}, series={Lecture Notes in Computer Science}, title={Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-criteria Shortest Path Problems}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-05348-2_17\">10.1007/978-3-030-05348-2_17</a>}, booktitle={Learning and Intelligent Optimization}, publisher={Springer International Publishing}, author={Bossek, Jakob and Grimme, Christian}, editor={Battiti, Roberto and Brunato, Mauro and Kotsireas, Ilias and Pardalos, Panos M.}, year={2019}, pages={184–198}, collection={Lecture Notes in Computer Science} }"},"place":"Cham","language":[{"iso":"eng"}],"publication":"Learning and Intelligent Optimization","abstract":[{"text":"The $$\\textbackslash mathcal NP$$-hard multi-criteria shortest path problem (mcSPP) is of utmost practical relevance, e.~g., in navigation system design and logistics. We address the problem of approximating the Pareto-front of the mcSPP with sum objectives. We do so by proposing a new mutation operator for multi-objective evolutionary algorithms that solves single-objective versions of the shortest path problem on subgraphs. A rigorous empirical benchmark on a diverse set of problem instances shows the effectiveness of the approach in comparison to a well-known mutation operator in terms of convergence speed and approximation quality. In addition, we glance at the neighbourhood structure and similarity of obtained Pareto-optimal solutions and derive promising directions for future work.","lang":"eng"}],"date_created":"2023-11-14T15:58:54Z","publisher":"Springer International Publishing","title":"Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-criteria Shortest Path Problems","year":"2019"},{"language":[{"iso":"eng"}],"keyword":["edge coloring problem","runtime analysis"],"publication":"Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms","abstract":[{"lang":"eng","text":"The edge coloring problem asks for an assignment of colors to edges of a graph such that no two incident edges share the same color and the number of colors is minimized. It is known that all graphs with maximum degree {$\\Delta$} can be colored with {$\\Delta$} or {$\\Delta$} + 1 colors, but it is NP-hard to determine whether {$\\Delta$} colors are sufficient. We present the first runtime analysis of evolutionary algorithms (EAs) for the edge coloring problem. Simple EAs such as RLS and (1+1) EA efficiently find (2{$\\Delta$} - 1)-colorings on arbitrary graphs and optimal colorings for even and odd cycles, paths, star graphs and arbitrary trees. A partial analysis for toroids also suggests efficient runtimes in bipartite graphs with many cycles. Experiments support these findings and investigate additional graph classes such as hypercubes, complete graphs and complete bipartite graphs. Theoretical and experimental results suggest that simple EAs find optimal colorings for all these graph classes in expected time O({$\\Delta\\mathscrl$}2m log m), where m is the number of edges and {$\\mathscrl$} is the length of the longest simple path in the graph."}],"date_created":"2023-11-14T15:58:56Z","publisher":"Association for Computing Machinery","title":"Time Complexity Analysis of RLS and (1 + 1) EA for the Edge Coloring Problem","year":"2019","series_title":"FOGA ’19","user_id":"102979","department":[{"_id":"819"}],"_id":"48870","extern":"1","type":"conference","status":"public","author":[{"full_name":"Bossek, Jakob","id":"102979","orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob"},{"full_name":"Sudholt, Dirk","last_name":"Sudholt","first_name":"Dirk"}],"date_updated":"2023-12-13T10:46:12Z","doi":"10.1145/3299904.3340311","publication_status":"published","publication_identifier":{"isbn":["978-1-4503-6254-2"]},"citation":{"apa":"Bossek, J., &#38; Sudholt, D. (2019). Time Complexity Analysis of RLS and (1 + 1) EA for the Edge Coloring Problem. <i>Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, 102–115. <a href=\"https://doi.org/10.1145/3299904.3340311\">https://doi.org/10.1145/3299904.3340311</a>","short":"J. Bossek, D. Sudholt, in: Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, Association for Computing Machinery, New York, NY, USA, 2019, pp. 102–115.","mla":"Bossek, Jakob, and Dirk Sudholt. “Time Complexity Analysis of RLS and (1 + 1) EA for the Edge Coloring Problem.” <i>Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, Association for Computing Machinery, 2019, pp. 102–115, doi:<a href=\"https://doi.org/10.1145/3299904.3340311\">10.1145/3299904.3340311</a>.","bibtex":"@inproceedings{Bossek_Sudholt_2019, place={New York, NY, USA}, series={FOGA ’19}, title={Time Complexity Analysis of RLS and (1 + 1) EA for the Edge Coloring Problem}, DOI={<a href=\"https://doi.org/10.1145/3299904.3340311\">10.1145/3299904.3340311</a>}, booktitle={Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Sudholt, Dirk}, year={2019}, pages={102–115}, collection={FOGA ’19} }","chicago":"Bossek, Jakob, and Dirk Sudholt. “Time Complexity Analysis of RLS and (1 + 1) EA for the Edge Coloring Problem.” In <i>Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, 102–115. FOGA ’19. New York, NY, USA: Association for Computing Machinery, 2019. <a href=\"https://doi.org/10.1145/3299904.3340311\">https://doi.org/10.1145/3299904.3340311</a>.","ieee":"J. Bossek and D. Sudholt, “Time Complexity Analysis of RLS and (1 + 1) EA for the Edge Coloring Problem,” in <i>Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, 2019, pp. 102–115, doi: <a href=\"https://doi.org/10.1145/3299904.3340311\">10.1145/3299904.3340311</a>.","ama":"Bossek J, Sudholt D. Time Complexity Analysis of RLS and (1 + 1) EA for the Edge Coloring Problem. In: <i>Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>. FOGA ’19. Association for Computing Machinery; 2019:102–115. doi:<a href=\"https://doi.org/10.1145/3299904.3340311\">10.1145/3299904.3340311</a>"},"page":"102–115","place":"New York, NY, USA"},{"_id":"48875","department":[{"_id":"819"}],"user_id":"102979","series_title":"Lecture Notes in Computer Science","extern":"1","type":"conference","editor":[{"first_name":"Roberto","last_name":"Battiti","full_name":"Battiti, Roberto"},{"full_name":"Brunato, Mauro","last_name":"Brunato","first_name":"Mauro"},{"first_name":"Ilias","last_name":"Kotsireas","full_name":"Kotsireas, Ilias"},{"first_name":"Panos M.","last_name":"Pardalos","full_name":"Pardalos, Panos M."}],"status":"public","date_updated":"2023-12-13T10:47:32Z","author":[{"orcid":"0000-0002-4121-4668","last_name":"Bossek","full_name":"Bossek, Jakob","id":"102979","first_name":"Jakob"},{"full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike"}],"doi":"10.1007/978-3-030-05348-2_19","publication_identifier":{"isbn":["978-3-030-05348-2"]},"place":"Cham","page":"215–219","citation":{"mla":"Bossek, Jakob, and Heike Trautmann. “Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time.” <i>Learning and Intelligent Optimization</i>, edited by Roberto Battiti et al., Springer International Publishing, 2019, pp. 215–219, doi:<a href=\"https://doi.org/10.1007/978-3-030-05348-2_19\">10.1007/978-3-030-05348-2_19</a>.","bibtex":"@inproceedings{Bossek_Trautmann_2019, place={Cham}, series={Lecture Notes in Computer Science}, title={Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-05348-2_19\">10.1007/978-3-030-05348-2_19</a>}, booktitle={Learning and Intelligent Optimization}, publisher={Springer International Publishing}, author={Bossek, Jakob and Trautmann, Heike}, editor={Battiti, Roberto and Brunato, Mauro and Kotsireas, Ilias and Pardalos, Panos M.}, year={2019}, pages={215–219}, collection={Lecture Notes in Computer Science} }","short":"J. Bossek, H. Trautmann, in: R. Battiti, M. Brunato, I. Kotsireas, P.M. Pardalos (Eds.), Learning and Intelligent Optimization, Springer International Publishing, Cham, 2019, pp. 215–219.","apa":"Bossek, J., &#38; Trautmann, H. (2019). Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time. In R. Battiti, M. Brunato, I. Kotsireas, &#38; P. M. Pardalos (Eds.), <i>Learning and Intelligent Optimization</i> (pp. 215–219). Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-030-05348-2_19\">https://doi.org/10.1007/978-3-030-05348-2_19</a>","ieee":"J. Bossek and H. Trautmann, “Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time,” in <i>Learning and Intelligent Optimization</i>, 2019, pp. 215–219, doi: <a href=\"https://doi.org/10.1007/978-3-030-05348-2_19\">10.1007/978-3-030-05348-2_19</a>.","chicago":"Bossek, Jakob, and Heike Trautmann. “Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time.” In <i>Learning and Intelligent Optimization</i>, edited by Roberto Battiti, Mauro Brunato, Ilias Kotsireas, and Panos M. Pardalos, 215–219. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2019. <a href=\"https://doi.org/10.1007/978-3-030-05348-2_19\">https://doi.org/10.1007/978-3-030-05348-2_19</a>.","ama":"Bossek J, Trautmann H. Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time. In: Battiti R, Brunato M, Kotsireas I, Pardalos PM, eds. <i>Learning and Intelligent Optimization</i>. Lecture Notes in Computer Science. Springer International Publishing; 2019:215–219. doi:<a href=\"https://doi.org/10.1007/978-3-030-05348-2_19\">10.1007/978-3-030-05348-2_19</a>"},"keyword":["Algorithm selection","Performance measurement"],"language":[{"iso":"eng"}],"publication":"Learning and Intelligent Optimization","abstract":[{"text":"A multiobjective perspective onto common performance measures such as the PAR10 score or the expected runtime of single-objective stochastic solvers is presented by directly investigating the tradeoff between the fraction of failed runs and the average runtime. Multi-objective indicators operating in the bi-objective space allow for an overall performance comparison on a set of instances paving the way for instance-based automated algorithm selection techniques.","lang":"eng"}],"publisher":"Springer International Publishing","date_created":"2023-11-14T15:58:57Z","title":"Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time","year":"2019"},{"publication_identifier":{"issn":["0943-4062"]},"issue":"3","year":"2019","citation":{"apa":"Casalicchio, G., Bossek, J., Lang, M., Kirchhoff, D., Kerschke, P., Hofner, B., Seibold, H., Vanschoren, J., &#38; Bischl, B. (2019). OpenML: An R Package to Connect to the Machine Learning Platform OpenML. <i>Computational Statistics</i>, <i>34</i>(3), 977–991. <a href=\"https://doi.org/10.1007/s00180-017-0742-2\">https://doi.org/10.1007/s00180-017-0742-2</a>","mla":"Casalicchio, Giuseppe, et al. “OpenML: An R Package to Connect to the Machine Learning Platform OpenML.” <i>Computational Statistics</i>, vol. 34, no. 3, 2019, pp. 977–991, doi:<a href=\"https://doi.org/10.1007/s00180-017-0742-2\">10.1007/s00180-017-0742-2</a>.","bibtex":"@article{Casalicchio_Bossek_Lang_Kirchhoff_Kerschke_Hofner_Seibold_Vanschoren_Bischl_2019, title={OpenML: An R Package to Connect to the Machine Learning Platform OpenML}, volume={34}, DOI={<a href=\"https://doi.org/10.1007/s00180-017-0742-2\">10.1007/s00180-017-0742-2</a>}, number={3}, journal={Computational Statistics}, author={Casalicchio, Giuseppe and Bossek, Jakob and Lang, Michel and Kirchhoff, Dominik and Kerschke, Pascal and Hofner, Benjamin and Seibold, Heidi and Vanschoren, Joaquin and Bischl, Bernd}, year={2019}, pages={977–991} }","short":"G. Casalicchio, J. Bossek, M. Lang, D. Kirchhoff, P. Kerschke, B. Hofner, H. Seibold, J. Vanschoren, B. Bischl, Computational Statistics 34 (2019) 977–991.","ieee":"G. Casalicchio <i>et al.</i>, “OpenML: An R Package to Connect to the Machine Learning Platform OpenML,” <i>Computational Statistics</i>, vol. 34, no. 3, pp. 977–991, 2019, doi: <a href=\"https://doi.org/10.1007/s00180-017-0742-2\">10.1007/s00180-017-0742-2</a>.","chicago":"Casalicchio, Giuseppe, Jakob Bossek, Michel Lang, Dominik Kirchhoff, Pascal Kerschke, Benjamin Hofner, Heidi Seibold, Joaquin Vanschoren, and Bernd Bischl. “OpenML: An R Package to Connect to the Machine Learning Platform OpenML.” <i>Computational Statistics</i> 34, no. 3 (2019): 977–991. <a href=\"https://doi.org/10.1007/s00180-017-0742-2\">https://doi.org/10.1007/s00180-017-0742-2</a>.","ama":"Casalicchio G, Bossek J, Lang M, et al. OpenML: An R Package to Connect to the Machine Learning Platform OpenML. <i>Computational Statistics</i>. 2019;34(3):977–991. doi:<a href=\"https://doi.org/10.1007/s00180-017-0742-2\">10.1007/s00180-017-0742-2</a>"},"page":"977–991","intvolume":"        34","date_updated":"2023-12-13T10:51:17Z","date_created":"2023-11-14T15:58:57Z","author":[{"first_name":"Giuseppe","full_name":"Casalicchio, Giuseppe","last_name":"Casalicchio"},{"first_name":"Jakob","id":"102979","full_name":"Bossek, Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668"},{"last_name":"Lang","full_name":"Lang, Michel","first_name":"Michel"},{"last_name":"Kirchhoff","full_name":"Kirchhoff, Dominik","first_name":"Dominik"},{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"},{"last_name":"Hofner","full_name":"Hofner, Benjamin","first_name":"Benjamin"},{"first_name":"Heidi","last_name":"Seibold","full_name":"Seibold, Heidi"},{"first_name":"Joaquin","full_name":"Vanschoren, Joaquin","last_name":"Vanschoren"},{"first_name":"Bernd","full_name":"Bischl, Bernd","last_name":"Bischl"}],"volume":34,"title":"OpenML: An R Package to Connect to the Machine Learning Platform OpenML","doi":"10.1007/s00180-017-0742-2","type":"journal_article","publication":"Computational Statistics","abstract":[{"lang":"eng","text":"OpenML is an online machine learning platform where researchers can easily share data, machine learning tasks and experiments as well as organize them online to work and collaborate more efficiently. In this paper, we present an R package to interface with the OpenML platform and illustrate its usage in combination with the machine learning R package mlr (Bischl et al. J Mach Learn Res 17(170):1—5, 2016). We show how the OpenML package allows R users to easily search, download and upload data sets and machine learning tasks. Furthermore, we also show how to upload results of experiments, share them with others and download results from other users. Beyond ensuring reproducibility of results, the OpenML platform automates much of the drudge work, speeds up research, facilitates collaboration and increases the users’ visibility online."}],"status":"public","_id":"48877","user_id":"102979","department":[{"_id":"819"}],"keyword":["Databases","Machine learning","R","Reproducible research"],"language":[{"iso":"eng"}]}]