--- _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: '46343' abstract: - lang: eng text: This paper addresses multimodality of multi-objective (MO) optimization landscapes. Contrary to common perception of local optima, according to which they are hindering the progress of optimization algorithms, it will be shown that local efficient sets in a multi-objective setting can assist optimizers in finding global efficient sets. We use sophisticated visualization techniques, which rely on gradient field heatmaps, to highlight those insights into landscape characteristics. Finally, the MO local optimizer MOGSA is introduced, which exploits those observations by sliding down the multi-objective gradient hill and moving along the local efficient sets. author: - first_name: Christian full_name: Grimme, Christian last_name: Grimme - 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: 'Grimme C, Kerschke P, Trautmann H. Multimodality in Multi-Objective Optimization — More Boon than Bane? In: Deb K, Goodman E, Coello CCA, et al., eds. Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO). Vol 11411. Lecture Notes in Computer Science. Springer; 2019:126–138. doi:10.1007/978-3-030-12598-1_11' apa: Grimme, C., Kerschke, P., & Trautmann, H. (2019). Multimodality in Multi-Objective Optimization — More Boon than Bane? In K. Deb, E. Goodman, C. C. A. Coello, K. Klamroth, K. Miettinen, S. Mostaghim, & P. Reed (Eds.), Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO) (Vol. 11411, pp. 126–138). Springer. https://doi.org/10.1007/978-3-030-12598-1_11 bibtex: '@inproceedings{Grimme_Kerschke_Trautmann_2019, place={East Lansing, MI, USA}, series={Lecture Notes in Computer Science}, title={Multimodality in Multi-Objective Optimization — More Boon than Bane?}, volume={11411}, DOI={10.1007/978-3-030-12598-1_11}, booktitle={Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO)}, publisher={Springer}, author={Grimme, Christian and Kerschke, Pascal and Trautmann, Heike}, editor={Deb, Kalyanmoy and Goodman, Erik and Coello, Coello Carlos A. and Klamroth, Kathrin and Miettinen, Kaisa and Mostaghim, Sanaz and Reed, Patrick}, year={2019}, pages={126–138}, collection={Lecture Notes in Computer Science} }' chicago: 'Grimme, Christian, Pascal Kerschke, and Heike Trautmann. “Multimodality in Multi-Objective Optimization — More Boon than Bane?” In Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO), edited by Kalyanmoy Deb, Erik Goodman, Coello Carlos A. Coello, Kathrin Klamroth, Kaisa Miettinen, Sanaz Mostaghim, and Patrick Reed, 11411:126–138. Lecture Notes in Computer Science. East Lansing, MI, USA: Springer, 2019. https://doi.org/10.1007/978-3-030-12598-1_11.' ieee: 'C. Grimme, P. Kerschke, and H. Trautmann, “Multimodality in Multi-Objective Optimization — More Boon than Bane?,” in Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO), 2019, vol. 11411, pp. 126–138, doi: 10.1007/978-3-030-12598-1_11.' mla: Grimme, Christian, et al. “Multimodality in Multi-Objective Optimization — More Boon than Bane?” Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO), edited by Kalyanmoy Deb et al., vol. 11411, Springer, 2019, pp. 126–138, doi:10.1007/978-3-030-12598-1_11. short: 'C. Grimme, P. Kerschke, H. Trautmann, in: K. Deb, E. Goodman, C.C.A. Coello, K. Klamroth, K. Miettinen, S. Mostaghim, P. Reed (Eds.), Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO), Springer, East Lansing, MI, USA, 2019, pp. 126–138.' date_created: 2023-08-04T07:49:08Z date_updated: 2023-10-16T13:31:03Z department: - _id: '34' - _id: '819' doi: 10.1007/978-3-030-12598-1_11 editor: - first_name: Kalyanmoy full_name: Deb, Kalyanmoy last_name: Deb - first_name: Erik full_name: Goodman, Erik last_name: Goodman - first_name: Coello Carlos A. full_name: Coello, Coello Carlos A. last_name: 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 intvolume: ' 11411' language: - iso: eng page: 126–138 place: East Lansing, MI, USA publication: Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO) publisher: Springer series_title: Lecture Notes in Computer Science status: public title: Multimodality in Multi-Objective Optimization — More Boon than Bane? type: conference user_id: '15504' volume: 11411 year: '2019' ... --- _id: '46345' abstract: - lang: eng text: It has long been observed that for practically any computational problem that has been intensely studied, different instances are best solved using different algorithms. This is particularly pronounced for computationally hard problems, where in most cases, no single algorithm defines the state of the art; instead, there is a set of algorithms with complementary strengths. This performance complementarity can be exploited in various ways, one of which is based on the idea of selecting, from a set of given algorithms, for each problem instance to be solved the one expected to perform best. The task of automatically selecting an algorithm from a given set is known as the per-instance algorithm selection problem and has been intensely studied over the past 15 years, leading to major improvements in the state of the art in solving a growing number of discrete combinatorial problems, including propositional satisfiability and AI planning. Per-instance algorithm selection also shows much promise for boosting performance in solving continuous and mixed discrete/continuous optimisation problems. This survey provides an overview of research in automated algorithm selection, ranging from early and seminal works to recent and promising application areas. Different from earlier work, it covers applications to discrete and continuous problems, and discusses algorithm selection in context with conceptually related approaches, such as algorithm configuration, scheduling, or portfolio selection. Since informative and cheaply computable problem instance features provide the basis for effective per-instance algorithm selection systems, we also provide an overview of such features for discrete and continuous problems. Finally, we provide perspectives on future work in the area and discuss a number of open research challenges. author: - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke - first_name: Holger H full_name: Hoos, Holger H last_name: Hoos - first_name: Frank full_name: Neumann, Frank last_name: Neumann - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: 'Kerschke P, Hoos HH, Neumann F, Trautmann H. Automated Algorithm Selection: Survey and Perspectives. Evolutionary Computation (ECJ). 2019;27(1):3–45. doi:10.1162/evco_a_00242' apa: 'Kerschke, P., Hoos, H. H., Neumann, F., & Trautmann, H. (2019). Automated Algorithm Selection: Survey and Perspectives. Evolutionary Computation (ECJ), 27(1), 3–45. https://doi.org/10.1162/evco_a_00242' bibtex: '@article{Kerschke_Hoos_Neumann_Trautmann_2019, title={Automated Algorithm Selection: Survey and Perspectives}, volume={27}, DOI={10.1162/evco_a_00242}, number={1}, journal={Evolutionary Computation (ECJ)}, author={Kerschke, Pascal and Hoos, Holger H and Neumann, Frank and Trautmann, Heike}, year={2019}, pages={3–45} }' chicago: 'Kerschke, Pascal, Holger H Hoos, Frank Neumann, and Heike Trautmann. “Automated Algorithm Selection: Survey and Perspectives.” Evolutionary Computation (ECJ) 27, no. 1 (2019): 3–45. https://doi.org/10.1162/evco_a_00242.' ieee: 'P. Kerschke, H. H. Hoos, F. Neumann, and H. Trautmann, “Automated Algorithm Selection: Survey and Perspectives,” Evolutionary Computation (ECJ), vol. 27, no. 1, pp. 3–45, 2019, doi: 10.1162/evco_a_00242.' mla: 'Kerschke, Pascal, et al. “Automated Algorithm Selection: Survey and Perspectives.” Evolutionary Computation (ECJ), vol. 27, no. 1, 2019, pp. 3–45, doi:10.1162/evco_a_00242.' short: P. Kerschke, H.H. Hoos, F. Neumann, H. Trautmann, Evolutionary Computation (ECJ) 27 (2019) 3–45. date_created: 2023-08-04T07:50:33Z date_updated: 2023-10-16T13:31:40Z department: - _id: '34' - _id: '819' doi: 10.1162/evco_a_00242 intvolume: ' 27' issue: '1' language: - iso: eng page: 3–45 publication: Evolutionary Computation (ECJ) status: public title: 'Automated Algorithm Selection: Survey and Perspectives' type: journal_article user_id: '15504' volume: 27 year: '2019' ... --- _id: '46344' abstract: - lang: eng text: Analyzing data streams has received considerable attention over the past decades due to the widespread usage of sensors, social media and other streaming data sources. A core research area in this field is stream clustering which aims to recognize patterns in an unordered, infinite and evolving stream of observations. Clustering can be a crucial support in decision making, since it aims for an optimized aggregated representation of a continuous data stream over time and allows to identify patterns in large and high-dimensional data. A multitude of algorithms and approaches has been developed that are able to find and maintain clusters over time in the challenging streaming scenario. This survey explores, summarizes and categorizes a total of 51 stream clustering algorithms and identifies core research threads over the past decades. In particular, it identifies categories of algorithms based on distance thresholds, density grids and statistical models as well as algorithms for high dimensional data. Furthermore, it discusses applications scenarios, available software and how to configure stream clustering algorithms. This survey is considerably more extensive than comparable studies, more up-to-date and highlights how concepts are interrelated and have been developed over time. author: - first_name: Matthias full_name: Carnein, Matthias last_name: Carnein - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: 'Carnein M, Trautmann H. Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms. Business and Information Systems Engineering (BISE). 2019;61(3):277–297.' apa: 'Carnein, M., & Trautmann, H. (2019). Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms. Business and Information Systems Engineering (BISE), 61(3), 277–297.' bibtex: '@article{Carnein_Trautmann_2019, title={Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms}, volume={61}, number={3}, journal={Business and Information Systems Engineering (BISE)}, author={Carnein, Matthias and Trautmann, Heike}, year={2019}, pages={277–297} }' chicago: 'Carnein, Matthias, and Heike Trautmann. “Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms.” Business and Information Systems Engineering (BISE) 61, no. 3 (2019): 277–297.' ieee: 'M. Carnein and H. Trautmann, “Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms,” Business and Information Systems Engineering (BISE), vol. 61, no. 3, pp. 277–297, 2019.' mla: 'Carnein, Matthias, and Heike Trautmann. “Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms.” Business and Information Systems Engineering (BISE), vol. 61, no. 3, 2019, pp. 277–297.' short: M. Carnein, H. Trautmann, Business and Information Systems Engineering (BISE) 61 (2019) 277–297. date_created: 2023-08-04T07:49:47Z date_updated: 2023-10-16T13:31:21Z department: - _id: '34' - _id: '819' intvolume: ' 61' issue: '3' language: - iso: eng page: 277–297 publication: Business and Information Systems Engineering (BISE) status: public title: 'Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms' type: journal_article user_id: '15504' volume: 61 year: '2019' ... --- _id: '46340' abstract: - lang: eng text: Recommender systems aim to provide personalized suggestions to customers which products to buy or services to consume. They can help to increase sales by helping customers discover new and relevant products. Traditionally, recommender systems use the purchase history of a customer, e.g., the purchased quantity or properties of the items. While this allows to build personalized recommendations, it is a very limited view of the problem. Nowadays, extensive information about customers and their personal preferences is available which goes far beyond their purchase behaviour. For example, customers reveal their preferences in social media, by their browsing habits and online search behaviour or their interest in specific newsletters. In this paper, we investigate how information from different sources and channels can be collected and incorporated into the recommendation process. We demonstrate this, based on a real-life case study of a retailer with several million transactions. We discuss how to employ a recommender system in this scenario, evaluate various recommendation strategies and describe how to incorporate information from different sources and channels, both internal and external. Our results show that the recommendations can be better tailored to the personal preferences of customers. author: - first_name: Matthias full_name: Carnein, Matthias last_name: Carnein - first_name: Leschek full_name: Homann, Leschek last_name: Homann - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 - first_name: Gottfried full_name: Vossen, Gottfried last_name: Vossen citation: ama: 'Carnein M, Homann L, Trautmann H, Vossen G. A Recommender System Based on Omni-Channel Customer Data. In: Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19). ; 2019:65–74.' apa: Carnein, M., Homann, L., Trautmann, H., & Vossen, G. (2019). A Recommender System Based on Omni-Channel Customer Data. Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19), 65–74. bibtex: '@inproceedings{Carnein_Homann_Trautmann_Vossen_2019, place={Moscow, Russia}, title={A Recommender System Based on Omni-Channel Customer Data}, booktitle={Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19)}, author={Carnein, Matthias and Homann, Leschek and Trautmann, Heike and Vossen, Gottfried}, year={2019}, pages={65–74} }' chicago: Carnein, Matthias, Leschek Homann, Heike Trautmann, and Gottfried Vossen. “A Recommender System Based on Omni-Channel Customer Data.” In Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19), 65–74. Moscow, Russia, 2019. ieee: M. Carnein, L. Homann, H. Trautmann, and G. Vossen, “A Recommender System Based on Omni-Channel Customer Data,” in Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19), 2019, pp. 65–74. mla: Carnein, Matthias, et al. “A Recommender System Based on Omni-Channel Customer Data.” Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19), 2019, pp. 65–74. short: 'M. Carnein, L. Homann, H. Trautmann, G. Vossen, in: Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19), Moscow, Russia, 2019, pp. 65–74.' date_created: 2023-08-04T07:46:20Z date_updated: 2023-10-16T13:29:53Z department: - _id: '34' - _id: '819' language: - iso: eng page: 65–74 place: Moscow, Russia publication: Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19) status: public title: A Recommender System Based on Omni-Channel Customer Data type: conference user_id: '15504' year: '2019' ... --- _id: '46341' abstract: - lang: eng text: Customer Segmentation aims to identify groups of customers that share similar interest or behaviour. It is an essential tool in marketing and can be used to target customer segments with tailored marketing strategies. Customer segmentation is often based on clustering techniques. This analysis is typically performed as a snapshot analysis where segments are identified at a specific point in time. However, this ignores the fact that customer segments are highly volatile and segments change over time. Once segments change, the entire analysis needs to be repeated and strategies adapted. In this paper we explore stream clustering as a tool to alleviate this problem. We propose a new stream clustering algorithm which allows to identify and track customer segments over time. The biggest challenge is that customer segmentation often relies on the transaction history of a customer. Since this data changes over time, it is necessary to update customers which have already been incorporated into the clustering. We show how to perform this step incrementally, without the need for periodic re-computations. As a result, customer segmentation can be performed continuously, faster and is more scalable. We demonstrate the performance of our algorithm using a large real-life case study. author: - first_name: Matthias full_name: Carnein, Matthias last_name: Carnein - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: 'Carnein M, Trautmann H. Customer Segmentation Based on Transactional Data Using Stream Clustering. In: Proceedings of the 23$^rd$ Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD ’19). ; 2019:280–292.' apa: Carnein, M., & Trautmann, H. (2019). Customer Segmentation Based on Transactional Data Using Stream Clustering. Proceedings of the 23$^rd$ Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD ’19), 280–292. bibtex: '@inproceedings{Carnein_Trautmann_2019, place={Macau, China}, title={Customer Segmentation Based on Transactional Data Using Stream Clustering}, booktitle={Proceedings of the 23$^rd$ Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD ’19)}, author={Carnein, Matthias and Trautmann, Heike}, year={2019}, pages={280–292} }' chicago: Carnein, Matthias, and Heike Trautmann. “Customer Segmentation Based on Transactional Data Using Stream Clustering.” In Proceedings of the 23$^rd$ Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD ’19), 280–292. Macau, China, 2019. ieee: M. Carnein and H. Trautmann, “Customer Segmentation Based on Transactional Data Using Stream Clustering,” in Proceedings of the 23$^rd$ Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD ’19), 2019, pp. 280–292. mla: Carnein, Matthias, and Heike Trautmann. “Customer Segmentation Based on Transactional Data Using Stream Clustering.” Proceedings of the 23$^rd$ Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD ’19), 2019, pp. 280–292. short: 'M. Carnein, H. Trautmann, in: Proceedings of the 23$^rd$ Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD ’19), Macau, China, 2019, pp. 280–292.' date_created: 2023-08-04T07:47:20Z date_updated: 2023-10-16T13:30:10Z department: - _id: '34' - _id: '819' language: - iso: eng page: 280–292 place: Macau, China publication: Proceedings of the 23$^rd$ Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD ’19) status: public title: Customer Segmentation Based on Transactional Data Using Stream Clustering type: conference user_id: '15504' year: '2019' ... --- _id: '46342' abstract: - lang: eng text: There is a range of phenomena in continuous, global multi-objective optimization, that cannot occur in single-objective optimization. For instance, in some multi-objective optimization problems it is possible to follow continuous paths of gradients of straightforward weighted scalarization functions, starting from locally efficient solutions, in order to reach globally Pareto optimal solutions. This paper seeks to better characterize multimodal multi-objective landscapes and to better understand the transitions from local optima to global optima in simple, path-oriented search procedures. author: - first_name: Christian full_name: Grimme, Christian last_name: Grimme - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke - first_name: Michael T M full_name: Emmerich, Michael T M last_name: Emmerich - first_name: Mike full_name: Preuss, Mike last_name: Preuss - first_name: André H full_name: Deutz, André H last_name: Deutz - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: 'Grimme C, Kerschke P, Emmerich MTM, Preuss M, Deutz AH, Trautmann H. Sliding to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal Multi-Objective Optimization. In: AIP Conference Proceedings. AIP Publishing; 2019:020052-1-020052-020054. doi:10.1063/1.5090019' apa: 'Grimme, C., Kerschke, P., Emmerich, M. T. M., Preuss, M., Deutz, A. H., & Trautmann, H. (2019). Sliding to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal Multi-Objective Optimization. AIP Conference Proceedings, 020052-1-020052–020054. https://doi.org/10.1063/1.5090019' bibtex: '@inproceedings{Grimme_Kerschke_Emmerich_Preuss_Deutz_Trautmann_2019, place={Leiden, The Netherlands}, title={Sliding to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal Multi-Objective Optimization}, DOI={10.1063/1.5090019}, booktitle={AIP Conference Proceedings}, publisher={AIP Publishing}, author={Grimme, Christian and Kerschke, Pascal and Emmerich, Michael T M and Preuss, Mike and Deutz, André H and Trautmann, Heike}, year={2019}, pages={020052-1-020052–4} }' chicago: 'Grimme, Christian, Pascal Kerschke, Michael T M Emmerich, Mike Preuss, André H Deutz, and Heike Trautmann. “Sliding to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal Multi-Objective Optimization.” In AIP Conference Proceedings, 020052-1-020052–54. Leiden, The Netherlands: AIP Publishing, 2019. https://doi.org/10.1063/1.5090019.' ieee: 'C. Grimme, P. Kerschke, M. T. M. Emmerich, M. Preuss, A. H. Deutz, and H. Trautmann, “Sliding to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal Multi-Objective Optimization,” in AIP Conference Proceedings, 2019, pp. 020052-1-020052–4, doi: 10.1063/1.5090019.' mla: 'Grimme, Christian, et al. “Sliding to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal Multi-Objective Optimization.” AIP Conference Proceedings, AIP Publishing, 2019, pp. 020052-1-020052–54, doi:10.1063/1.5090019.' short: 'C. Grimme, P. Kerschke, M.T.M. Emmerich, M. Preuss, A.H. Deutz, H. Trautmann, in: AIP Conference Proceedings, AIP Publishing, Leiden, The Netherlands, 2019, pp. 020052-1-020052–4.' date_created: 2023-08-04T07:48:15Z date_updated: 2023-10-16T13:30:43Z department: - _id: '34' - _id: '819' doi: 10.1063/1.5090019 language: - iso: eng page: 020052-1-020052-4 place: Leiden, The Netherlands publication: AIP Conference Proceedings publisher: AIP Publishing status: public title: 'Sliding to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal Multi-Objective Optimization' type: conference user_id: '15504' year: '2019' ... --- _id: '46336' abstract: - lang: eng text: Choosing the best-performing optimizer(s) out of a portfolio of optimization algorithms is usually a difficult and complex task. It gets even worse, if the underlying functions are unknown, i.e., so-called black-box problems, and function evaluations are considered to be expensive. In case of continuous single-objective optimization problems, exploratory landscape analysis (ELA), a sophisticated and effective approach for characterizing the landscapes of such problems by means of numerical values before actually performing the optimization task itself, is advantageous. Unfortunately, until now it has been quite complicated to compute multiple ELA features simultaneously, as the corresponding code has been—if at all—spread across multiple platforms or at least across several packages within these platforms. This article presents a broad summary of existing ELA approaches and introduces flacco, an R-package for feature-based landscape analysis of continuous and constrained optimization problems. Although its functions neither solve the optimization problem itself nor the related algorithm selection problem (ASP), it offers easy access to an essential ingredient of the ASP by providing a wide collection of ELA features on a single platform—even within a single package. In addition, flacco provides multiple visualization techniques, which enhance the understanding of some of these numerical features, and thereby make certain landscape properties more comprehensible. On top of that, we will introduce the package’s built-in, as well as web-hosted and hence platform-independent, graphical user interface (GUI). It facilitates the usage of the package—especially for people who are not familiar with R—and thus makes flacco a very convenient toolbox when working towards algorithm selection of continuous single-objective optimization problems. author: - 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: 'Kerschke P, Trautmann H. Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-package flacco. In: Bauer N, Ickstadt K, Lübke K, Szepannek G, Trautmann H, Vichi M, eds. Applications in Statistical Computing. Springer; 2019:93–123. doi:10.1007/978-3-030-25147-5_7' apa: Kerschke, P., & Trautmann, H. (2019). Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-package flacco. In N. Bauer, K. Ickstadt, K. Lübke, G. Szepannek, H. Trautmann, & M. Vichi (Eds.), Applications in Statistical Computing (pp. 93–123). Springer. https://doi.org/10.1007/978-3-030-25147-5_7 bibtex: '@inbook{Kerschke_Trautmann_2019, title={Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-package flacco}, DOI={10.1007/978-3-030-25147-5_7}, booktitle={Applications in Statistical Computing}, publisher={Springer}, author={Kerschke, Pascal and Trautmann, Heike}, editor={Bauer, Nadja and Ickstadt, Katja and Lübke, Karsten and Szepannek, Gero and Trautmann, Heike and Vichi, Maurizio}, year={2019}, pages={93–123} }' chicago: Kerschke, Pascal, and Heike Trautmann. “Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package Flacco.” In Applications in Statistical Computing, edited by Nadja Bauer, Katja Ickstadt, Karsten Lübke, Gero Szepannek, Heike Trautmann, and Maurizio Vichi, 93–123. Springer, 2019. https://doi.org/10.1007/978-3-030-25147-5_7. ieee: P. Kerschke and H. Trautmann, “Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-package flacco,” in Applications in Statistical Computing, N. Bauer, K. Ickstadt, K. Lübke, G. Szepannek, H. Trautmann, and M. Vichi, Eds. Springer, 2019, pp. 93–123. mla: Kerschke, Pascal, and Heike Trautmann. “Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package Flacco.” Applications in Statistical Computing, edited by Nadja Bauer et al., Springer, 2019, pp. 93–123, doi:10.1007/978-3-030-25147-5_7. short: 'P. Kerschke, H. Trautmann, in: N. Bauer, K. Ickstadt, K. Lübke, G. Szepannek, H. Trautmann, M. Vichi (Eds.), Applications in Statistical Computing, Springer, 2019, pp. 93–123.' date_created: 2023-08-04T07:43:30Z date_updated: 2023-10-16T13:08:22Z department: - _id: '34' - _id: '819' doi: 10.1007/978-3-030-25147-5_7 editor: - first_name: Nadja full_name: Bauer, Nadja last_name: Bauer - first_name: Katja full_name: Ickstadt, Katja last_name: Ickstadt - first_name: Karsten full_name: Lübke, Karsten last_name: Lübke - first_name: Gero full_name: Szepannek, Gero last_name: Szepannek - first_name: Heike full_name: Trautmann, Heike last_name: Trautmann - first_name: Maurizio full_name: Vichi, Maurizio last_name: Vichi language: - iso: eng page: 93–123 publication: Applications in Statistical Computing publisher: Springer status: public title: Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-package flacco type: book_chapter user_id: '15504' year: '2019' ... --- _id: '46335' author: - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: Trautmann H. Applications in Statistical Computing — From Music Data Analysis to Industrial Quality Improvement. Springer International Publishing; 2019. apa: Trautmann, H. (2019). Applications in Statistical Computing — From Music Data Analysis to Industrial Quality Improvement. Springer International Publishing. bibtex: '@book{Trautmann_2019, series={Studies in Classification, Data Analysis, and Knowledge Organization}, title={Applications in Statistical Computing — From Music Data Analysis to Industrial Quality Improvement}, publisher={Springer International Publishing}, author={Trautmann, Heike}, year={2019}, collection={Studies in Classification, Data Analysis, and Knowledge Organization} }' chicago: Trautmann, Heike. Applications in Statistical Computing — From Music Data Analysis to Industrial Quality Improvement. Studies in Classification, Data Analysis, and Knowledge Organization. Springer International Publishing, 2019. ieee: H. Trautmann, Applications in Statistical Computing — From Music Data Analysis to Industrial Quality Improvement. Springer International Publishing, 2019. mla: Trautmann, Heike. Applications in Statistical Computing — From Music Data Analysis to Industrial Quality Improvement. Springer International Publishing, 2019. short: H. Trautmann, Applications in Statistical Computing — From Music Data Analysis to Industrial Quality Improvement, Springer International Publishing, 2019. date_created: 2023-08-04T07:43:09Z date_updated: 2023-10-16T13:07:21Z department: - _id: '34' - _id: '819' language: - iso: eng publication_identifier: isbn: - 978-3-030-25147-5 publisher: Springer International Publishing series_title: Studies in Classification, Data Analysis, and Knowledge Organization status: public title: Applications in Statistical Computing — From Music Data Analysis to Industrial Quality Improvement type: book user_id: '15504' year: '2019' ... --- _id: '46346' abstract: - lang: eng text: In this article, we build upon previous work on designing informative and efficient Exploratory Landscape Analysis features for characterizing problems' landscapes and show their effectiveness in automatically constructing algorithm selection models in continuous black-box optimization problems. Focusing on algorithm performance results of the COCO platform of several years, we construct a representative set of high-performing complementary solvers and present an algorithm selection model that, compared to the portfolio's single best solver, on average requires less than half of the resources for solving a given problem. Therefore, there is a huge gain in efficiency compared to classical ensemble methods combined with an increased insight into problem characteristics and algorithm properties by using informative features. The model acts on the assumption that the function set of the Black-Box Optimization Benchmark is representative enough for practical applications. The model allows for selecting the best suited optimization algorithm within the considered set for unseen problems prior to the optimization itself based on a small sample of function evaluations. Note that such a sample can even be reused for the initial population of an evolutionary (optimization) algorithm so that even the feature costs become negligible. author: - 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: Kerschke P, Trautmann H. Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning. Evolutionary Computation (ECJ). 2019;27(1):99–127. doi:10.1162/evco_a_00236 apa: Kerschke, P., & Trautmann, H. (2019). Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning. Evolutionary Computation (ECJ), 27(1), 99–127. https://doi.org/10.1162/evco_a_00236 bibtex: '@article{Kerschke_Trautmann_2019, title={Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning}, volume={27}, DOI={10.1162/evco_a_00236}, number={1}, journal={Evolutionary Computation (ECJ)}, author={Kerschke, Pascal and Trautmann, Heike}, year={2019}, pages={99–127} }' chicago: 'Kerschke, Pascal, and Heike Trautmann. “Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning.” Evolutionary Computation (ECJ) 27, no. 1 (2019): 99–127. https://doi.org/10.1162/evco_a_00236.' ieee: 'P. Kerschke and H. Trautmann, “Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning,” Evolutionary Computation (ECJ), vol. 27, no. 1, pp. 99–127, 2019, doi: 10.1162/evco_a_00236.' mla: Kerschke, Pascal, and Heike Trautmann. “Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning.” Evolutionary Computation (ECJ), vol. 27, no. 1, 2019, pp. 99–127, doi:10.1162/evco_a_00236. short: P. Kerschke, H. Trautmann, Evolutionary Computation (ECJ) 27 (2019) 99–127. date_created: 2023-08-04T07:51:18Z date_updated: 2023-10-16T13:31:57Z department: - _id: '34' - _id: '819' doi: 10.1162/evco_a_00236 intvolume: ' 27' issue: '1' language: - iso: eng page: 99–127 publication: Evolutionary Computation (ECJ) status: public title: Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning type: journal_article user_id: '15504' volume: 27 year: '2019' ... --- _id: '46347' abstract: - lang: eng text: We continue recent work on the definition of multimodality in multiobjective optimization (MO) and the introduction of a test bed for multimodal MO problems. This goes beyond well-known diversity maintenance approaches but instead focuses on the landscape topology induced by the objective functions. More general multimodal MO problems are considered by allowing ellipsoid contours for single-objective subproblems. An experimental analysis compares two MO algorithms, one that explicitly relies on hypervolume gradient approximation, and one that is based on local search, both on a selection of generated example problems. We do not focus on performance but on the interaction induced by the problems and algorithms, which can be described by means of specific characteristics explicitly designed for the multimodal MO setting. Furthermore, we widen the scope of our analysis by additionally applying visualization techniques in the decision space. This strengthens and extends the foundations for Exploratory Landscape Analysis (ELA) in MO. author: - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke - first_name: Hao full_name: Wang, Hao last_name: Wang - first_name: Mike full_name: Preuss, Mike last_name: Preuss - first_name: Christian full_name: Grimme, Christian last_name: Grimme - first_name: André full_name: Deutz, André last_name: Deutz - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 - first_name: Michael full_name: Emmerich, Michael last_name: Emmerich citation: ama: Kerschke P, Wang H, Preuss M, et al. Search Dynamics on Multimodal Multi-Objective Problems. Evolutionary Computation (ECJ). 2019;27(4):577–609. doi:10.1162/evco_a_00234 apa: Kerschke, P., Wang, H., Preuss, M., Grimme, C., Deutz, A., Trautmann, H., & Emmerich, M. (2019). Search Dynamics on Multimodal Multi-Objective Problems. Evolutionary Computation (ECJ), 27(4), 577–609. https://doi.org/10.1162/evco_a_00234 bibtex: '@article{Kerschke_Wang_Preuss_Grimme_Deutz_Trautmann_Emmerich_2019, title={Search Dynamics on Multimodal Multi-Objective Problems}, volume={27}, DOI={10.1162/evco_a_00234}, number={4}, journal={Evolutionary Computation (ECJ)}, author={Kerschke, Pascal and Wang, Hao and Preuss, Mike and Grimme, Christian and Deutz, André and Trautmann, Heike and Emmerich, Michael}, year={2019}, pages={577–609} }' chicago: 'Kerschke, Pascal, Hao Wang, Mike Preuss, Christian Grimme, André Deutz, Heike Trautmann, and Michael Emmerich. “Search Dynamics on Multimodal Multi-Objective Problems.” Evolutionary Computation (ECJ) 27, no. 4 (2019): 577–609. https://doi.org/10.1162/evco_a_00234.' ieee: 'P. Kerschke et al., “Search Dynamics on Multimodal Multi-Objective Problems,” Evolutionary Computation (ECJ), vol. 27, no. 4, pp. 577–609, 2019, doi: 10.1162/evco_a_00234.' mla: Kerschke, Pascal, et al. “Search Dynamics on Multimodal Multi-Objective Problems.” Evolutionary Computation (ECJ), vol. 27, no. 4, 2019, pp. 577–609, doi:10.1162/evco_a_00234. short: P. Kerschke, H. Wang, M. Preuss, C. Grimme, A. Deutz, H. Trautmann, M. Emmerich, Evolutionary Computation (ECJ) 27 (2019) 577–609. date_created: 2023-08-04T07:52:06Z date_updated: 2023-10-16T13:32:18Z department: - _id: '34' - _id: '819' doi: 10.1162/evco_a_00234 intvolume: ' 27' issue: '4' language: - iso: eng page: 577–609 publication: Evolutionary Computation (ECJ) status: public title: Search Dynamics on Multimodal Multi-Objective Problems type: journal_article user_id: '15504' volume: 27 year: '2019' ... --- _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' ...