--- _id: '48852' 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. author: - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Aneta full_name: Neumann, Aneta last_name: Neumann - first_name: Frank full_name: Neumann, Frank last_name: Neumann 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: Parallel Problem Solving from Nature (PPSN XVI). Springer-Verlag; 2020:346–359. doi:10.1007/978-3-030-58112-1_24' apa: 'Bossek, J., Neumann, A., & Neumann, F. (2020). Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions. Parallel Problem Solving from Nature (PPSN XVI), 346–359. https://doi.org/10.1007/978-3-030-58112-1_24' 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={10.1007/978-3-030-58112-1_24}, 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} }' 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 Parallel Problem Solving from Nature (PPSN XVI), 346–359. Berlin, Heidelberg: Springer-Verlag, 2020. https://doi.org/10.1007/978-3-030-58112-1_24.' 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 Parallel Problem Solving from Nature (PPSN XVI), 2020, pp. 346–359, doi: 10.1007/978-3-030-58112-1_24.' mla: 'Bossek, Jakob, et al. “Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions.” Parallel Problem Solving from Nature (PPSN XVI), Springer-Verlag, 2020, pp. 346–359, doi:10.1007/978-3-030-58112-1_24.' short: 'J. Bossek, A. Neumann, F. Neumann, in: Parallel Problem Solving from Nature (PPSN XVI), Springer-Verlag, Berlin, Heidelberg, 2020, pp. 346–359.' date_created: 2023-11-14T15:58:54Z date_updated: 2023-12-13T10:44:54Z department: - _id: '819' doi: 10.1007/978-3-030-58112-1_24 extern: '1' keyword: - Evolutionary algorithms - Node weight dependent TSP - Traveling Thief Problem language: - iso: eng page: 346–359 place: Berlin, Heidelberg publication: Parallel Problem Solving from Nature (PPSN XVI) publication_identifier: isbn: - 978-3-030-58111-4 publication_status: published publisher: Springer-Verlag status: public title: 'Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions' type: conference user_id: '102979' year: '2020' ... --- _id: '48846' 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{$<$}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. author: - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Christian full_name: Grimme, Christian last_name: Grimme - first_name: Günter full_name: Rudolph, Günter last_name: Rudolph - first_name: Heike full_name: Trautmann, Heike last_name: Trautmann citation: ama: 'Bossek J, Grimme C, Rudolph G, Trautmann H. Towards Decision Support in Dynamic Bi-Objective Vehicle Routing. In: 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE Press; 2020:1–8. doi:10.1109/CEC48606.2020.9185778' apa: Bossek, J., Grimme, C., Rudolph, G., & Trautmann, H. (2020). Towards Decision Support in Dynamic Bi-Objective Vehicle Routing. 2020 IEEE Congress on Evolutionary Computation (CEC), 1–8. https://doi.org/10.1109/CEC48606.2020.9185778 bibtex: '@inproceedings{Bossek_Grimme_Rudolph_Trautmann_2020, place={Glasgow, United Kingdom}, title={Towards Decision Support in Dynamic Bi-Objective Vehicle Routing}, DOI={10.1109/CEC48606.2020.9185778}, 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} }' chicago: 'Bossek, Jakob, Christian Grimme, Günter Rudolph, and Heike Trautmann. “Towards Decision Support in Dynamic Bi-Objective Vehicle Routing.” In 2020 IEEE Congress on Evolutionary Computation (CEC), 1–8. Glasgow, United Kingdom: IEEE Press, 2020. https://doi.org/10.1109/CEC48606.2020.9185778.' ieee: 'J. Bossek, C. Grimme, G. Rudolph, and H. Trautmann, “Towards Decision Support in Dynamic Bi-Objective Vehicle Routing,” in 2020 IEEE Congress on Evolutionary Computation (CEC), 2020, pp. 1–8, doi: 10.1109/CEC48606.2020.9185778.' mla: Bossek, Jakob, et al. “Towards Decision Support in Dynamic Bi-Objective Vehicle Routing.” 2020 IEEE Congress on Evolutionary Computation (CEC), IEEE Press, 2020, pp. 1–8, doi:10.1109/CEC48606.2020.9185778. 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.' date_created: 2023-11-14T15:58:53Z date_updated: 2023-12-13T10:44:17Z department: - _id: '819' doi: 10.1109/CEC48606.2020.9185778 extern: '1' language: - iso: eng page: 1–8 place: Glasgow, United Kingdom publication: 2020 IEEE Congress on Evolutionary Computation (CEC) publication_status: published publisher: IEEE Press status: public title: Towards Decision Support in Dynamic Bi-Objective Vehicle Routing type: conference user_id: '102979' year: '2020' ... --- _id: '48879' abstract: - lang: eng 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. author: - first_name: Anh Viet full_name: Do, Anh Viet last_name: Do - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Aneta full_name: Neumann, Aneta last_name: Neumann - first_name: Frank full_name: Neumann, Frank last_name: Neumann citation: ama: 'Do AV, Bossek J, Neumann A, Neumann F. Evolving Diverse Sets of Tours for the Travelling Salesperson Problem. In: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO’20. Association for Computing Machinery; 2020:681–689. doi:10.1145/3377930.3389844' apa: Do, A. V., Bossek, J., Neumann, A., & Neumann, F. (2020). Evolving Diverse Sets of Tours for the Travelling Salesperson Problem. Proceedings of the Genetic and Evolutionary Computation Conference, 681–689. https://doi.org/10.1145/3377930.3389844 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={10.1145/3377930.3389844}, 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} }' chicago: 'Do, Anh Viet, Jakob Bossek, Aneta Neumann, and Frank Neumann. “Evolving Diverse Sets of Tours for the Travelling Salesperson Problem.” In Proceedings of the Genetic and Evolutionary Computation Conference, 681–689. GECCO’20. New York, NY, USA: Association for Computing Machinery, 2020. https://doi.org/10.1145/3377930.3389844.' ieee: 'A. V. Do, J. Bossek, A. Neumann, and F. Neumann, “Evolving Diverse Sets of Tours for the Travelling Salesperson Problem,” in Proceedings of the Genetic and Evolutionary Computation Conference, 2020, pp. 681–689, doi: 10.1145/3377930.3389844.' mla: Do, Anh Viet, et al. “Evolving Diverse Sets of Tours for the Travelling Salesperson Problem.” Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, 2020, pp. 681–689, doi:10.1145/3377930.3389844. 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.' date_created: 2023-11-14T15:58:58Z date_updated: 2023-12-13T10:48:50Z department: - _id: '819' doi: 10.1145/3377930.3389844 extern: '1' keyword: - diversity maximisation - evolutionary algorithms - travelling salesperson problem language: - iso: eng page: 681–689 place: New York, NY, USA publication: Proceedings of the Genetic and Evolutionary Computation Conference publication_identifier: isbn: - 978-1-4503-7128-5 publisher: Association for Computing Machinery series_title: GECCO’20 status: public title: Evolving Diverse Sets of Tours for the Travelling Salesperson Problem type: conference user_id: '102979' year: '2020' ... --- _id: '48895' abstract: - lang: eng text: Evolutionary algorithms (EAs) are general-purpose problem solvers that usually perform an unbiased search. This is reasonable and desirable in a black-box scenario. For combinatorial optimization problems, often more knowledge about the structure of optimal solutions is given, which can be leveraged by means of biased search operators. We consider the Minimum Spanning Tree (MST) problem in a single- and multi-objective version, and introduce a biased mutation, which puts more emphasis on the selection of edges of low rank in terms of low domination number. We present example graphs where the biased mutation can significantly speed up the expected runtime until (Pareto-)optimal solutions are found. On the other hand, we demonstrate that bias can lead to exponential runtime if "heavy" edges are necessarily part of an optimal solution. However, on general graphs in the single-objective setting, we show that a combined mutation operator which decides for unbiased or biased edge selection in each step with equal probability exhibits a polynomial upper bound - as unbiased mutation - in the worst case and benefits from bias if the circumstances are favorable. author: - first_name: Vahid full_name: Roostapour, Vahid last_name: Roostapour - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Frank full_name: Neumann, Frank last_name: Neumann 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: Proceedings of the 2020 Genetic and Evolutionary Computation Conference. {GECCO} ’20. Association for Computing Machinery; 2020:551–559. doi:10.1145/3377930.3390168' apa: Roostapour, V., Bossek, J., & Neumann, F. (2020). Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem. Proceedings of the 2020 Genetic and Evolutionary Computation Conference, 551–559. https://doi.org/10.1145/3377930.3390168 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={10.1145/3377930.3390168}, 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} }' 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 Proceedings of the 2020 Genetic and Evolutionary Computation Conference, 551–559. {GECCO} ’20. New York, NY, USA: Association for Computing Machinery, 2020. https://doi.org/10.1145/3377930.3390168.' 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 Proceedings of the 2020 Genetic and Evolutionary Computation Conference, 2020, pp. 551–559, doi: 10.1145/3377930.3390168.' mla: Roostapour, Vahid, et al. “Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem.” Proceedings of the 2020 Genetic and Evolutionary Computation Conference, Association for Computing Machinery, 2020, pp. 551–559, doi:10.1145/3377930.3390168. 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.' date_created: 2023-11-14T15:59:00Z date_updated: 2023-12-13T10:49:38Z department: - _id: '819' doi: 10.1145/3377930.3390168 extern: '1' keyword: - biased mutation - evolutionary algorithms - minimum spanning tree problem - runtime analysis language: - iso: eng page: 551–559 place: New York, NY, USA publication: Proceedings of the 2020 Genetic and Evolutionary Computation Conference publication_identifier: isbn: - 978-1-4503-7128-5 publisher: Association for Computing Machinery series_title: '{GECCO} ’20' status: public title: Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem type: conference user_id: '102979' year: '2020' ... --- _id: '48897' 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.' author: - first_name: Moritz full_name: Seiler, Moritz last_name: Seiler - first_name: Janina full_name: Pohl, Janina last_name: Pohl - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke - first_name: Heike full_name: Trautmann, Heike last_name: Trautmann citation: 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: Parallel Problem Solving from {Nature} (PPSN XVI). Springer-Verlag; 2020:48–64. doi:10.1007/978-3-030-58112-1_4' apa: Seiler, M., Pohl, J., Bossek, J., Kerschke, P., & Trautmann, H. (2020). Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. Parallel Problem Solving from {Nature} (PPSN XVI), 48–64. https://doi.org/10.1007/978-3-030-58112-1_4 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={10.1007/978-3-030-58112-1_4}, 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} }' 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 Parallel Problem Solving from {Nature} (PPSN XVI), 48–64. Berlin, Heidelberg: Springer-Verlag, 2020. https://doi.org/10.1007/978-3-030-58112-1_4.' 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 Parallel Problem Solving from {Nature} (PPSN XVI), 2020, pp. 48–64, doi: 10.1007/978-3-030-58112-1_4.' mla: Seiler, Moritz, et al. “Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem.” Parallel Problem Solving from {Nature} (PPSN XVI), Springer-Verlag, 2020, pp. 48–64, doi:10.1007/978-3-030-58112-1_4. 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.' date_created: 2023-11-14T15:59:00Z date_updated: 2023-12-13T10:49:45Z department: - _id: '819' doi: 10.1007/978-3-030-58112-1_4 extern: '1' keyword: - Automated algorithm selection - Deep learning - Feature-based approaches - Traveling Salesperson Problem language: - iso: eng page: 48–64 place: Berlin, Heidelberg publication: Parallel Problem Solving from {Nature} (PPSN XVI) publication_identifier: isbn: - 978-3-030-58111-4 publisher: Springer-Verlag status: public title: Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem type: conference user_id: '102979' year: '2020' ... --- _id: '48848' 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. author: - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke - first_name: Heike full_name: Trautmann, Heike last_name: Trautmann citation: ama: Bossek J, Kerschke P, Trautmann H. A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms. Applied Soft Computing. 2020;88(C). doi:10.1016/j.asoc.2019.105901 apa: Bossek, J., Kerschke, P., & Trautmann, H. (2020). A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms. Applied Soft Computing, 88(C). https://doi.org/10.1016/j.asoc.2019.105901 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={10.1016/j.asoc.2019.105901}, number={C}, journal={Applied Soft Computing}, author={Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}, year={2020} }' chicago: Bossek, Jakob, Pascal Kerschke, and Heike Trautmann. “A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms.” Applied Soft Computing 88, no. C (2020). https://doi.org/10.1016/j.asoc.2019.105901. ieee: 'J. Bossek, P. Kerschke, and H. Trautmann, “A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms,” Applied Soft Computing, vol. 88, no. C, 2020, doi: 10.1016/j.asoc.2019.105901.' mla: Bossek, Jakob, et al. “A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms.” Applied Soft Computing, vol. 88, no. C, 2020, doi:10.1016/j.asoc.2019.105901. short: J. Bossek, P. Kerschke, H. Trautmann, Applied Soft Computing 88 (2020). date_created: 2023-11-14T15:58:53Z date_updated: 2023-12-13T10:52:17Z department: - _id: '819' doi: 10.1016/j.asoc.2019.105901 intvolume: ' 88' issue: C keyword: - Algorithm selection - Combinatorial optimization - Multi-objective optimization - Performance measurement - Traveling Salesperson Problem language: - iso: eng publication: Applied Soft Computing publication_identifier: issn: - 1568-4946 status: public title: A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms type: journal_article user_id: '102979' volume: 88 year: '2020' ... --- _id: '48836' author: - first_name: Thomas full_name: Bartz-Beielstein, Thomas last_name: Bartz-Beielstein - first_name: Carola full_name: Doerr, Carola last_name: Doerr - first_name: Daan full_name: van den Berg, Daan last_name: van den Berg - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Sowmya full_name: Chandrasekaran, Sowmya last_name: Chandrasekaran - first_name: Tome full_name: Eftimov, Tome last_name: Eftimov - first_name: Andreas full_name: Fischbach, Andreas last_name: Fischbach - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke - first_name: William La full_name: Cava, William La last_name: Cava - first_name: Manuel full_name: Lopez-Ibanez, Manuel last_name: Lopez-Ibanez - first_name: Katherine M. full_name: Malan, Katherine M. last_name: Malan - first_name: Jason H. full_name: Moore, Jason H. last_name: Moore - first_name: Boris full_name: Naujoks, Boris last_name: Naujoks - first_name: Patryk full_name: Orzechowski, Patryk last_name: Orzechowski - first_name: Vanessa full_name: Volz, Vanessa last_name: Volz - first_name: Markus full_name: Wagner, Markus last_name: Wagner - first_name: Thomas full_name: Weise, Thomas last_name: Weise citation: ama: 'Bartz-Beielstein T, Doerr C, van den Berg D, et al. Benchmarking in Optimization: Best Practice and Open Issues. Corr. Published online 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., & Weise, T. (2020). Benchmarking in Optimization: Best Practice and Open Issues. Corr.' 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} }' 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.” Corr, 2020.' ieee: 'T. Bartz-Beielstein et al., “Benchmarking in Optimization: Best Practice and Open Issues,” Corr, 2020.' mla: 'Bartz-Beielstein, Thomas, et al. “Benchmarking in Optimization: Best Practice and Open Issues.” Corr, 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). date_created: 2023-11-14T15:58:51Z date_updated: 2023-12-13T10:52:24Z department: - _id: '819' language: - iso: eng publication: Corr status: public title: 'Benchmarking in Optimization: Best Practice and Open Issues' type: journal_article user_id: '102979' year: '2020' ... --- _id: '46330' 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 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.' author: - first_name: Moritz full_name: Seiler, Moritz id: '105520' last_name: Seiler - first_name: Janina full_name: Pohl, Janina last_name: Pohl - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: 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. Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI). ; 2020:48–64. doi:10.1007/978-3-030-58112-1_4' apa: Seiler, M., Pohl, J., Bossek, J., Kerschke, P., & 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, & H. Trautmann (Eds.), Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI) (pp. 48–64). https://doi.org/10.1007/978-3-030-58112-1_4 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={10.1007/978-3-030-58112-1_4}, 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} }' 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 Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI), edited by Thomas Bäck, Mike Preuss, André Deutz, Hao Wang, Carola Doerr, Michael Emmerich, and Heike Trautmann, 48–64. Leiden, The Netherlands, 2020. https://doi.org/10.1007/978-3-030-58112-1_4. 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 Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI), 2020, pp. 48–64, doi: 10.1007/978-3-030-58112-1_4.' mla: Seiler, Moritz, et al. “Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem.” Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI), edited by Thomas Bäck et al., 2020, pp. 48–64, doi:10.1007/978-3-030-58112-1_4. 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.' date_created: 2023-08-04T07:39:05Z date_updated: 2024-06-10T11:57:13Z department: - _id: '34' - _id: '819' doi: 10.1007/978-3-030-58112-1_4 editor: - first_name: Thomas full_name: Bäck, Thomas last_name: Bäck - first_name: Mike full_name: Preuss, Mike last_name: Preuss - first_name: André full_name: Deutz, André last_name: Deutz - first_name: Hao full_name: Wang, Hao last_name: Wang - first_name: Carola full_name: Doerr, Carola last_name: Doerr - first_name: Michael full_name: Emmerich, Michael last_name: Emmerich - first_name: Heike full_name: Trautmann, Heike last_name: Trautmann language: - iso: eng page: 48–64 place: Leiden, The Netherlands publication: Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI) status: public title: Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem type: conference user_id: '15504' year: '2020' ... --- _id: '46334' 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. author: - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: Bossek J, Kerschke P, Trautmann H. A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms. Applied Soft Computing. 2020;88:105901. doi:https://doi.org/10.1016/j.asoc.2019.105901 apa: Bossek, J., Kerschke, P., & Trautmann, H. (2020). A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms. Applied Soft Computing, 88, 105901. https://doi.org/10.1016/j.asoc.2019.105901 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={https://doi.org/10.1016/j.asoc.2019.105901}, journal={Applied Soft Computing}, author={Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}, year={2020}, pages={105901} }' chicago: 'Bossek, Jakob, Pascal Kerschke, and Heike Trautmann. “A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms.” Applied Soft Computing 88 (2020): 105901. https://doi.org/10.1016/j.asoc.2019.105901.' ieee: 'J. Bossek, P. Kerschke, and H. Trautmann, “A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms,” Applied Soft Computing, vol. 88, p. 105901, 2020, doi: https://doi.org/10.1016/j.asoc.2019.105901.' mla: Bossek, Jakob, et al. “A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms.” Applied Soft Computing, vol. 88, 2020, p. 105901, doi:https://doi.org/10.1016/j.asoc.2019.105901. short: J. Bossek, P. Kerschke, H. Trautmann, Applied Soft Computing 88 (2020) 105901. date_created: 2023-08-04T07:42:26Z date_updated: 2024-06-10T12:00:46Z department: - _id: '34' - _id: '819' doi: https://doi.org/10.1016/j.asoc.2019.105901 intvolume: ' 88' keyword: - Algorithm selection - Multi-objective optimization - Performance measurement - Combinatorial optimization - Traveling Salesperson Problem language: - iso: eng page: '105901' publication: Applied Soft Computing publication_identifier: issn: - 1568-4946 status: public title: A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms type: journal_article user_id: '15504' volume: 88 year: '2020' ... --- _id: '46322' 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. author: - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Christian full_name: Grimme, Christian last_name: Grimme - first_name: Günter full_name: Rudolph, Günter last_name: Rudolph - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: 'Bossek J, Grimme C, Rudolph G, Trautmann H. Towards Decision Support in Dynamic Bi-Objective Vehicle Routing. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC). ; 2020:1–8. doi:10.1109/CEC48606.2020.9185778' apa: Bossek, J., Grimme, C., Rudolph, G., & Trautmann, H. (2020). Towards Decision Support in Dynamic Bi-Objective Vehicle Routing. Proceedings of the IEEE Congress on Evolutionary Computation (CEC), 1–8. https://doi.org/10.1109/CEC48606.2020.9185778 bibtex: '@inproceedings{Bossek_Grimme_Rudolph_Trautmann_2020, place={Glasgow, UK}, title={Towards Decision Support in Dynamic Bi-Objective Vehicle Routing}, DOI={10.1109/CEC48606.2020.9185778}, 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} }' chicago: Bossek, Jakob, Christian Grimme, Günter Rudolph, and Heike Trautmann. “Towards Decision Support in Dynamic Bi-Objective Vehicle Routing.” In Proceedings of the IEEE Congress on Evolutionary Computation (CEC), 1–8. Glasgow, UK, 2020. https://doi.org/10.1109/CEC48606.2020.9185778. ieee: 'J. Bossek, C. Grimme, G. Rudolph, and H. Trautmann, “Towards Decision Support in Dynamic Bi-Objective Vehicle Routing,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC), 2020, pp. 1–8, doi: 10.1109/CEC48606.2020.9185778.' mla: Bossek, Jakob, et al. “Towards Decision Support in Dynamic Bi-Objective Vehicle Routing.” Proceedings of the IEEE Congress on Evolutionary Computation (CEC), 2020, pp. 1–8, doi:10.1109/CEC48606.2020.9185778. 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.' date_created: 2023-08-04T07:32:36Z date_updated: 2024-06-10T12:02:05Z department: - _id: '34' - _id: '819' doi: 10.1109/CEC48606.2020.9185778 language: - iso: eng page: 1–8 place: Glasgow, UK publication: Proceedings of the IEEE Congress on Evolutionary Computation (CEC) status: public title: Towards Decision Support in Dynamic Bi-Objective Vehicle Routing type: conference user_id: '15504' year: '2020' ... --- _id: '46324' 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. author: - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: 'Bossek J, Kerschke P, Trautmann H. Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC). IEEE; 2020:1–8.' apa: Bossek, J., Kerschke, P., & Trautmann, H. (2020). Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection. Proceedings of the IEEE Congress on Evolutionary Computation (CEC), 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 Proceedings of the IEEE Congress on Evolutionary Computation (CEC), 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 Proceedings of the IEEE Congress on Evolutionary Computation (CEC), 2020, pp. 1–8. mla: Bossek, Jakob, et al. “Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection.” Proceedings of the IEEE Congress on Evolutionary Computation (CEC), 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.' date_created: 2023-08-04T07:34:40Z date_updated: 2024-06-10T12:01:46Z department: - _id: '34' - _id: '819' language: - iso: eng page: 1–8 place: Glasgow, UK publication: Proceedings of the IEEE Congress on Evolutionary Computation (CEC) publisher: IEEE status: public title: Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection type: conference user_id: '15504' year: '2020' ... --- _id: '46323' abstract: - lang: eng 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. author: - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Christian full_name: Grimme, Christian last_name: Grimme - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: 'Bossek J, Grimme C, Trautmann H. Dynamic Bi-Objective Routing of Multiple Vehicles. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20). ACM; 2020:166–174.' apa: Bossek, J., Grimme, C., & Trautmann, H. (2020). Dynamic Bi-Objective Routing of Multiple Vehicles. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20), 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} }' chicago: 'Bossek, Jakob, Christian Grimme, and Heike Trautmann. “Dynamic Bi-Objective Routing of Multiple Vehicles.” In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20), 166–174. Cancun, Mexico: ACM, 2020.' ieee: J. Bossek, C. Grimme, and H. Trautmann, “Dynamic Bi-Objective Routing of Multiple Vehicles,” in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20), 2020, pp. 166–174. mla: Bossek, Jakob, et al. “Dynamic Bi-Objective Routing of Multiple Vehicles.” Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20), ACM, 2020, pp. 166–174. 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.' date_created: 2023-08-04T07:33:30Z date_updated: 2024-06-10T12:01:57Z department: - _id: '34' - _id: '819' language: - iso: eng page: 166–174 place: Cancun, Mexico publication: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20) publisher: ACM status: public title: Dynamic Bi-Objective Routing of Multiple Vehicles type: conference user_id: '15504' year: '2020' ... --- _id: '48841' 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. author: - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Christian full_name: Grimme, Christian last_name: Grimme - first_name: Stephan full_name: Meisel, Stephan last_name: Meisel - first_name: Günter full_name: Rudolph, Günter last_name: Rudolph - first_name: Heike full_name: Trautmann, Heike last_name: Trautmann citation: 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. Evolutionary Multi-Criterion Optimization (EMO). Lecture Notes in Computer Science. Springer International Publishing; 2019:516–528. doi:10.1007/978-3-030-12598-1_41' apa: 'Bossek, J., Grimme, C., Meisel, S., Rudolph, G., & 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, & P. Reed (Eds.), Evolutionary Multi-Criterion Optimization (EMO) (pp. 516–528). Springer International Publishing. https://doi.org/10.1007/978-3-030-12598-1_41' 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={10.1007/978-3-030-12598-1_41}, 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} }' chicago: 'Bossek, Jakob, Christian Grimme, Stephan Meisel, Günter Rudolph, and Heike Trautmann. “Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm.” In Evolutionary Multi-Criterion Optimization (EMO), 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. https://doi.org/10.1007/978-3-030-12598-1_41.' ieee: 'J. Bossek, C. Grimme, S. Meisel, G. Rudolph, and H. Trautmann, “Bi-Objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm,” in Evolutionary Multi-Criterion Optimization (EMO), 2019, pp. 516–528, doi: 10.1007/978-3-030-12598-1_41.' mla: 'Bossek, Jakob, et al. “Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm.” Evolutionary Multi-Criterion Optimization (EMO), edited by Kalyanmoy Deb et al., Springer International Publishing, 2019, pp. 516–528, doi:10.1007/978-3-030-12598-1_41.' 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.' date_created: 2023-11-14T15:58:52Z date_updated: 2023-12-13T10:43:07Z department: - _id: '819' doi: 10.1007/978-3-030-12598-1_41 editor: - first_name: Kalyanmoy full_name: Deb, Kalyanmoy last_name: Deb - first_name: Erik full_name: Goodman, Erik last_name: Goodman - first_name: Carlos A. full_name: Coello Coello, Carlos A. last_name: Coello Coello - first_name: Kathrin full_name: Klamroth, Kathrin last_name: Klamroth - first_name: Kaisa full_name: Miettinen, Kaisa last_name: Miettinen - first_name: Sanaz full_name: Mostaghim, Sanaz last_name: Mostaghim - first_name: Patrick full_name: Reed, Patrick last_name: Reed extern: '1' keyword: - Combinatorial optimization - Dynamic optimization - Metaheuristics - Multi-objective optimization - Vehicle routing language: - iso: eng page: 516–528 place: Cham publication: Evolutionary Multi-Criterion Optimization (EMO) publication_identifier: isbn: - 978-3-030-12598-1 publication_status: published publisher: Springer International Publishing series_title: Lecture Notes in Computer Science status: public title: 'Bi-Objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm' type: conference user_id: '102979' year: '2019' ... --- _id: '48842' 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.' author: - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke - first_name: Aneta full_name: Neumann, Aneta last_name: Neumann - first_name: Markus full_name: Wagner, Markus last_name: Wagner - first_name: Frank full_name: Neumann, Frank last_name: Neumann - first_name: Heike full_name: Trautmann, Heike last_name: Trautmann citation: 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: Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms. FOGA ’19. Association for Computing Machinery; 2019:58–71. doi:10.1145/3299904.3340307' apa: Bossek, J., Kerschke, P., Neumann, A., Wagner, M., Neumann, F., & Trautmann, H. (2019). Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators. Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 58–71. https://doi.org/10.1145/3299904.3340307 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={10.1145/3299904.3340307}, 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} }' 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 Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 58–71. FOGA ’19. New York, NY, USA: Association for Computing Machinery, 2019. https://doi.org/10.1145/3299904.3340307.' 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 Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 2019, pp. 58–71, doi: 10.1145/3299904.3340307.' mla: Bossek, Jakob, et al. “Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators.” Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, Association for Computing Machinery, 2019, pp. 58–71, doi:10.1145/3299904.3340307. 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.' date_created: 2023-11-14T15:58:52Z date_updated: 2023-12-13T10:42:57Z department: - _id: '819' doi: 10.1145/3299904.3340307 extern: '1' keyword: - benchmarking - instance features - optimization - problem generation - traveling salesperson problem language: - iso: eng page: 58–71 place: New York, NY, USA publication: Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms publication_identifier: isbn: - 978-1-4503-6254-2 publication_status: published publisher: Association for Computing Machinery series_title: FOGA ’19 status: public title: Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators type: conference user_id: '102979' year: '2019' ... --- _id: '48843' 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. author: - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Frank full_name: Neumann, Frank last_name: Neumann - first_name: Pan full_name: Peng, Pan last_name: Peng - first_name: Dirk full_name: Sudholt, Dirk last_name: Sudholt citation: ama: 'Bossek J, Neumann F, Peng P, Sudholt D. Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring. In: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO ’19. Association for Computing Machinery; 2019:1443–1451. doi:10.1145/3321707.3321792' apa: Bossek, J., Neumann, F., Peng, P., & Sudholt, D. (2019). Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring. Proceedings of the Genetic and Evolutionary Computation Conference, 1443–1451. https://doi.org/10.1145/3321707.3321792 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={10.1145/3321707.3321792}, 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} }' chicago: 'Bossek, Jakob, Frank Neumann, Pan Peng, and Dirk Sudholt. “Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring.” In Proceedings of the Genetic and Evolutionary Computation Conference, 1443–1451. GECCO ’19. New York, NY, USA: Association for Computing Machinery, 2019. https://doi.org/10.1145/3321707.3321792.' ieee: 'J. Bossek, F. Neumann, P. Peng, and D. Sudholt, “Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring,” in Proceedings of the Genetic and Evolutionary Computation Conference, 2019, pp. 1443–1451, doi: 10.1145/3321707.3321792.' mla: Bossek, Jakob, et al. “Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring.” Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, 2019, pp. 1443–1451, doi:10.1145/3321707.3321792. 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.' date_created: 2023-11-14T15:58:52Z date_updated: 2023-12-13T10:42:37Z department: - _id: '819' doi: 10.1145/3321707.3321792 extern: '1' keyword: - dynamic optimization - evolutionary algorithms - running time analysis - theory language: - iso: eng page: 1443–1451 place: New York, NY, USA publication: Proceedings of the Genetic and Evolutionary Computation Conference publication_identifier: isbn: - 978-1-4503-6111-8 publication_status: published publisher: Association for Computing Machinery series_title: GECCO ’19 status: public title: Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring type: conference user_id: '102979' year: '2019' ... --- _id: '48840' 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. author: - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Christian full_name: Grimme, Christian last_name: Grimme - first_name: Frank full_name: Neumann, Frank last_name: Neumann citation: ama: 'Bossek J, Grimme C, Neumann F. On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem. In: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO ’19. Association for Computing Machinery; 2019:516–523. doi:10.1145/3321707.3321818' apa: Bossek, J., Grimme, C., & Neumann, F. (2019). On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem. Proceedings of the Genetic and Evolutionary Computation Conference, 516–523. https://doi.org/10.1145/3321707.3321818 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={10.1145/3321707.3321818}, 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} }' chicago: 'Bossek, Jakob, Christian Grimme, and Frank Neumann. “On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem.” In Proceedings of the Genetic and Evolutionary Computation Conference, 516–523. GECCO ’19. New York, NY, USA: Association for Computing Machinery, 2019. https://doi.org/10.1145/3321707.3321818.' ieee: 'J. Bossek, C. Grimme, and F. Neumann, “On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem,” in Proceedings of the Genetic and Evolutionary Computation Conference, 2019, pp. 516–523, doi: 10.1145/3321707.3321818.' mla: Bossek, Jakob, et al. “On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem.” Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, 2019, pp. 516–523, doi:10.1145/3321707.3321818. 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.' date_created: 2023-11-14T15:58:52Z date_updated: 2023-12-13T10:42:24Z department: - _id: '819' doi: 10.1145/3321707.3321818 extern: '1' keyword: - biased mutation - combinatorial optimization - minimum spanning tree - multi-objective optimization language: - iso: eng page: 516–523 place: New York, NY, USA publication: Proceedings of the Genetic and Evolutionary Computation Conference publication_identifier: isbn: - 978-1-4503-6111-8 publication_status: published publisher: Association for Computing Machinery series_title: GECCO ’19 status: public title: On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem type: conference user_id: '102979' year: '2019' ... --- _id: '48858' abstract: - lang: eng 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. author: - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Christian full_name: Grimme, Christian last_name: Grimme citation: 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. Learning and Intelligent Optimization. Lecture Notes in Computer Science. Springer International Publishing; 2019:184–198. doi:10.1007/978-3-030-05348-2_17' apa: Bossek, J., & Grimme, C. (2019). Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-criteria Shortest Path Problems. In R. Battiti, M. Brunato, I. Kotsireas, & P. M. Pardalos (Eds.), Learning and Intelligent Optimization (pp. 184–198). Springer International Publishing. https://doi.org/10.1007/978-3-030-05348-2_17 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={10.1007/978-3-030-05348-2_17}, 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} }' chicago: 'Bossek, Jakob, and Christian Grimme. “Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-Criteria Shortest Path Problems.” In Learning and Intelligent Optimization, edited by Roberto Battiti, Mauro Brunato, Ilias Kotsireas, and Panos M. Pardalos, 184–198. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2019. https://doi.org/10.1007/978-3-030-05348-2_17.' ieee: 'J. Bossek and C. Grimme, “Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-criteria Shortest Path Problems,” in Learning and Intelligent Optimization, 2019, pp. 184–198, doi: 10.1007/978-3-030-05348-2_17.' mla: Bossek, Jakob, and Christian Grimme. “Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-Criteria Shortest Path Problems.” Learning and Intelligent Optimization, edited by Roberto Battiti et al., Springer International Publishing, 2019, pp. 184–198, doi:10.1007/978-3-030-05348-2_17. 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.' date_created: 2023-11-14T15:58:54Z date_updated: 2023-12-13T10:44:44Z department: - _id: '819' doi: 10.1007/978-3-030-05348-2_17 editor: - first_name: Roberto full_name: Battiti, Roberto last_name: Battiti - first_name: Mauro full_name: Brunato, Mauro last_name: Brunato - first_name: Ilias full_name: Kotsireas, Ilias last_name: Kotsireas - first_name: Panos M. full_name: Pardalos, Panos M. last_name: Pardalos extern: '1' language: - iso: eng page: 184–198 place: Cham publication: Learning and Intelligent Optimization publication_identifier: isbn: - 978-3-030-05348-2 publication_status: published publisher: Springer International Publishing series_title: Lecture Notes in Computer Science status: public title: Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-criteria Shortest Path Problems type: conference user_id: '102979' year: '2019' ... --- _id: '48870' 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. author: - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Dirk full_name: Sudholt, Dirk last_name: Sudholt citation: ama: 'Bossek J, Sudholt D. Time Complexity Analysis of RLS and (1 + 1) EA for the Edge Coloring Problem. In: Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms. FOGA ’19. Association for Computing Machinery; 2019:102–115. doi:10.1145/3299904.3340311' apa: Bossek, J., & Sudholt, D. (2019). Time Complexity Analysis of RLS and (1 + 1) EA for the Edge Coloring Problem. Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 102–115. https://doi.org/10.1145/3299904.3340311 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={10.1145/3299904.3340311}, 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 Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 102–115. FOGA ’19. New York, NY, USA: Association for Computing Machinery, 2019. https://doi.org/10.1145/3299904.3340311.' ieee: 'J. Bossek and D. Sudholt, “Time Complexity Analysis of RLS and (1 + 1) EA for the Edge Coloring Problem,” in Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 2019, pp. 102–115, doi: 10.1145/3299904.3340311.' mla: Bossek, Jakob, and Dirk Sudholt. “Time Complexity Analysis of RLS and (1 + 1) EA for the Edge Coloring Problem.” Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, Association for Computing Machinery, 2019, pp. 102–115, doi:10.1145/3299904.3340311. 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.' date_created: 2023-11-14T15:58:56Z date_updated: 2023-12-13T10:46:12Z department: - _id: '819' doi: 10.1145/3299904.3340311 extern: '1' keyword: - edge coloring problem - runtime analysis language: - iso: eng page: 102–115 place: New York, NY, USA publication: Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms publication_identifier: isbn: - 978-1-4503-6254-2 publication_status: published publisher: Association for Computing Machinery series_title: FOGA ’19 status: public title: Time Complexity Analysis of RLS and (1 + 1) EA for the Edge Coloring Problem type: conference user_id: '102979' year: '2019' ... --- _id: '48875' abstract: - lang: eng 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. author: - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Heike full_name: Trautmann, Heike last_name: Trautmann citation: 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. Learning and Intelligent Optimization. Lecture Notes in Computer Science. Springer International Publishing; 2019:215–219. doi:10.1007/978-3-030-05348-2_19' apa: 'Bossek, J., & Trautmann, H. (2019). Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time. In R. Battiti, M. Brunato, I. Kotsireas, & P. M. Pardalos (Eds.), Learning and Intelligent Optimization (pp. 215–219). Springer International Publishing. https://doi.org/10.1007/978-3-030-05348-2_19' 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={10.1007/978-3-030-05348-2_19}, 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} }' chicago: 'Bossek, Jakob, and Heike Trautmann. “Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time.” In Learning and Intelligent Optimization, edited by Roberto Battiti, Mauro Brunato, Ilias Kotsireas, and Panos M. Pardalos, 215–219. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2019. https://doi.org/10.1007/978-3-030-05348-2_19.' ieee: 'J. Bossek and H. Trautmann, “Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time,” in Learning and Intelligent Optimization, 2019, pp. 215–219, doi: 10.1007/978-3-030-05348-2_19.' mla: 'Bossek, Jakob, and Heike Trautmann. “Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time.” Learning and Intelligent Optimization, edited by Roberto Battiti et al., Springer International Publishing, 2019, pp. 215–219, doi:10.1007/978-3-030-05348-2_19.' 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.' date_created: 2023-11-14T15:58:57Z date_updated: 2023-12-13T10:47:32Z department: - _id: '819' doi: 10.1007/978-3-030-05348-2_19 editor: - first_name: Roberto full_name: Battiti, Roberto last_name: Battiti - first_name: Mauro full_name: Brunato, Mauro last_name: Brunato - first_name: Ilias full_name: Kotsireas, Ilias last_name: Kotsireas - first_name: Panos M. full_name: Pardalos, Panos M. last_name: Pardalos extern: '1' keyword: - Algorithm selection - Performance measurement language: - iso: eng page: 215–219 place: Cham publication: Learning and Intelligent Optimization publication_identifier: isbn: - 978-3-030-05348-2 publisher: Springer International Publishing series_title: Lecture Notes in Computer Science status: public title: 'Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time' type: conference user_id: '102979' year: '2019' ... --- _id: '48877' 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. author: - first_name: Giuseppe full_name: Casalicchio, Giuseppe last_name: Casalicchio - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Michel full_name: Lang, Michel last_name: Lang - first_name: Dominik full_name: Kirchhoff, Dominik last_name: Kirchhoff - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke - first_name: Benjamin full_name: Hofner, Benjamin last_name: Hofner - first_name: Heidi full_name: Seibold, Heidi last_name: Seibold - first_name: Joaquin full_name: Vanschoren, Joaquin last_name: Vanschoren - first_name: Bernd full_name: Bischl, Bernd last_name: Bischl citation: ama: 'Casalicchio G, Bossek J, Lang M, et al. OpenML: An R Package to Connect to the Machine Learning Platform OpenML. Computational Statistics. 2019;34(3):977–991. doi:10.1007/s00180-017-0742-2' apa: 'Casalicchio, G., Bossek, J., Lang, M., Kirchhoff, D., Kerschke, P., Hofner, B., Seibold, H., Vanschoren, J., & Bischl, B. (2019). OpenML: An R Package to Connect to the Machine Learning Platform OpenML. Computational Statistics, 34(3), 977–991. https://doi.org/10.1007/s00180-017-0742-2' 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={10.1007/s00180-017-0742-2}, 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} }' 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.” Computational Statistics 34, no. 3 (2019): 977–991. https://doi.org/10.1007/s00180-017-0742-2.' ieee: 'G. Casalicchio et al., “OpenML: An R Package to Connect to the Machine Learning Platform OpenML,” Computational Statistics, vol. 34, no. 3, pp. 977–991, 2019, doi: 10.1007/s00180-017-0742-2.' mla: 'Casalicchio, Giuseppe, et al. “OpenML: An R Package to Connect to the Machine Learning Platform OpenML.” Computational Statistics, vol. 34, no. 3, 2019, pp. 977–991, doi:10.1007/s00180-017-0742-2.' 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. date_created: 2023-11-14T15:58:57Z date_updated: 2023-12-13T10:51:17Z department: - _id: '819' doi: 10.1007/s00180-017-0742-2 intvolume: ' 34' issue: '3' keyword: - Databases - Machine learning - R - Reproducible research language: - iso: eng page: 977–991 publication: Computational Statistics publication_identifier: issn: - 0943-4062 status: public title: 'OpenML: An R Package to Connect to the Machine Learning Platform OpenML' type: journal_article user_id: '102979' volume: 34 year: '2019' ...