[{"abstract":[{"text":"In this work we focus on the well-known Euclidean Traveling Salesperson Problem (TSP) and two highly competitive inexact heuristic TSP solvers, EAX and LKH, in the context of per-instance algorithm selection (AS). We evolve instances with 1000 nodes where the solvers show strongly different performance profiles. These instances serve as a basis for an exploratory study on the identification of well-discriminating problem characteristics (features). Our results in a nutshell: we show that even though (1) promising features exist, (2) these are in line with previous results from the literature, and (3) models trained with these features are more accurate than models adopting sophisticated feature selection methods, the advantage is not close to the virtual best solver in terms of penalized average runtime and so is the performance gain over the single best solver. However, we show that a feature-free deep neural network based approach solely based on visual representation of the instances already matches classical AS model results and thus shows huge potential for future studies.","lang":"eng"}],"publication":"Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI)","citation":{"short":"M. Seiler, J. Pohl, J. Bossek, P. Kerschke, H. Trautmann, in: T. Bäck, M. Preuss, A. Deutz, H. Wang, C. Doerr, M. Emmerich, H. Trautmann (Eds.), Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI), Leiden, The Netherlands, 2020, pp. 48–64.","chicago":"Seiler, Moritz, Janina Pohl, Jakob Bossek, Pascal Kerschke, and Heike Trautmann. “Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem.” In <i>Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI)</i>, edited by Thomas Bäck, Mike Preuss, André Deutz, Hao Wang, Carola Doerr, Michael Emmerich, and Heike Trautmann, 48–64. Leiden, The Netherlands, 2020. <a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">https://doi.org/10.1007/978-3-030-58112-1_4</a>.","apa":"Seiler, M., Pohl, J., Bossek, J., Kerschke, P., &#38; Trautmann, H. (2020). Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. In T. Bäck, M. Preuss, A. Deutz, H. Wang, C. Doerr, M. Emmerich, &#38; H. Trautmann (Eds.), <i>Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI)</i> (pp. 48–64). <a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">https://doi.org/10.1007/978-3-030-58112-1_4</a>","ieee":"M. Seiler, J. Pohl, J. Bossek, P. Kerschke, and H. Trautmann, “Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem,” in <i>Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI)</i>, 2020, pp. 48–64, doi: <a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">10.1007/978-3-030-58112-1_4</a>.","ama":"Seiler M, Pohl J, Bossek J, Kerschke P, Trautmann H. Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. In: Bäck T, Preuss M, Deutz A, et al., eds. <i>Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI)</i>. ; 2020:48–64. doi:<a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">10.1007/978-3-030-58112-1_4</a>","bibtex":"@inproceedings{Seiler_Pohl_Bossek_Kerschke_Trautmann_2020, place={Leiden, The Netherlands}, title={Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">10.1007/978-3-030-58112-1_4</a>}, booktitle={Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI)}, author={Seiler, Moritz and Pohl, Janina and Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}, editor={Bäck, Thomas and Preuss, Mike and Deutz, André and Wang, Hao and Doerr, Carola and Emmerich, Michael and Trautmann, Heike}, year={2020}, pages={48–64} }","mla":"Seiler, Moritz, et al. “Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem.” <i>Proceedings of the 16$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XVI)</i>, edited by Thomas Bäck et al., 2020, pp. 48–64, doi:<a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">10.1007/978-3-030-58112-1_4</a>."},"type":"conference","department":[{"_id":"34"},{"_id":"819"}],"place":"Leiden, The Netherlands","date_created":"2023-08-04T07:39:05Z","date_updated":"2024-06-10T11:57:13Z","title":"Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem","year":"2020","status":"public","author":[{"first_name":"Moritz","last_name":"Seiler","full_name":"Seiler, Moritz","id":"105520"},{"full_name":"Pohl, Janina","first_name":"Janina","last_name":"Pohl"},{"first_name":"Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","full_name":"Bossek, Jakob","id":"102979"},{"last_name":"Kerschke","first_name":"Pascal","full_name":"Kerschke, Pascal"},{"id":"100740","orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike","full_name":"Trautmann, Heike"}],"doi":"10.1007/978-3-030-58112-1_4","user_id":"15504","editor":[{"full_name":"Bäck, Thomas","last_name":"Bäck","first_name":"Thomas"},{"first_name":"Mike","last_name":"Preuss","full_name":"Preuss, Mike"},{"full_name":"Deutz, André","first_name":"André","last_name":"Deutz"},{"full_name":"Wang, Hao","first_name":"Hao","last_name":"Wang"},{"full_name":"Doerr, Carola","first_name":"Carola","last_name":"Doerr"},{"full_name":"Emmerich, Michael","first_name":"Michael","last_name":"Emmerich"},{"last_name":"Trautmann","first_name":"Heike","full_name":"Trautmann, Heike"}],"page":"48–64","language":[{"iso":"eng"}],"_id":"46330"},{"abstract":[{"lang":"eng","text":"We build upon a recently proposed multi-objective view onto performance measurement of single-objective stochastic solvers. The trade-off between the fraction of failed runs and the mean runtime of successful runs – both to be minimized – is directly analyzed based on a study on algorithm selection of inexact state-of-the-art solvers for the famous Traveling Salesperson Problem (TSP). Moreover, we adopt the hypervolume indicator (HV) commonly used in multi-objective optimization for simultaneously assessing both conflicting objectives and investigate relations to commonly used performance indicators, both theoretically and empirically. Next to Penalized Average Runtime (PAR) and Penalized Quantile Runtime (PQR), the HV measure is used as a core concept within the construction of per-instance algorithm selection models offering interesting insights into complementary behavior of inexact TSP solvers."}],"publication":"Applied Soft Computing","citation":{"ieee":"J. Bossek, P. Kerschke, and H. Trautmann, “A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms,” <i>Applied Soft Computing</i>, vol. 88, p. 105901, 2020, doi: <a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>.","apa":"Bossek, J., Kerschke, P., &#38; Trautmann, H. (2020). A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms. <i>Applied Soft Computing</i>, <i>88</i>, 105901. <a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>","short":"J. Bossek, P. Kerschke, H. Trautmann, Applied Soft Computing 88 (2020) 105901.","chicago":"Bossek, Jakob, Pascal Kerschke, and Heike Trautmann. “A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms.” <i>Applied Soft Computing</i> 88 (2020): 105901. <a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>.","mla":"Bossek, Jakob, et al. “A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms.” <i>Applied Soft Computing</i>, vol. 88, 2020, p. 105901, doi:<a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>.","bibtex":"@article{Bossek_Kerschke_Trautmann_2020, title={A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms}, volume={88}, DOI={<a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>}, journal={Applied Soft Computing}, author={Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}, year={2020}, pages={105901} }","ama":"Bossek J, Kerschke P, Trautmann H. A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms. <i>Applied Soft Computing</i>. 2020;88:105901. doi:<a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>"},"keyword":["Algorithm selection","Multi-objective optimization","Performance measurement","Combinatorial optimization","Traveling Salesperson Problem"],"type":"journal_article","department":[{"_id":"34"},{"_id":"819"}],"date_created":"2023-08-04T07:42:26Z","date_updated":"2024-06-10T12:00:46Z","intvolume":"        88","status":"public","title":"A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms","year":"2020","author":[{"full_name":"Bossek, Jakob","last_name":"Bossek","first_name":"Jakob","orcid":"0000-0002-4121-4668","id":"102979"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"full_name":"Trautmann, Heike","orcid":"0000-0002-9788-8282","first_name":"Heike","last_name":"Trautmann","id":"100740"}],"publication_identifier":{"issn":["1568-4946"]},"doi":"https://doi.org/10.1016/j.asoc.2019.105901","user_id":"15504","volume":88,"page":"105901","_id":"46334","language":[{"iso":"eng"}]},{"user_id":"15504","doi":"10.1109/CEC48606.2020.9185778","page":"1–8","language":[{"iso":"eng"}],"_id":"46322","date_updated":"2024-06-10T12:02:05Z","year":"2020","status":"public","title":"Towards Decision Support in Dynamic Bi-Objective Vehicle Routing","author":[{"first_name":"Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","full_name":"Bossek, Jakob","id":"102979"},{"full_name":"Grimme, Christian","first_name":"Christian","last_name":"Grimme"},{"last_name":"Rudolph","first_name":"Günter","full_name":"Rudolph, Günter"},{"id":"100740","full_name":"Trautmann, Heike","first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282"}],"type":"conference","department":[{"_id":"34"},{"_id":"819"}],"date_created":"2023-08-04T07:32:36Z","place":"Glasgow, UK","abstract":[{"text":"We consider a dynamic bi-objective vehicle routing problem, where a subset of customers ask for service over time. Therein, the distance traveled by a single vehicle and the number of unserved dynamic requests is minimized by a dynamic evolutionary multi-objective algorithm (DEMOA), which operates on discrete time windows (eras). A decision is made at each era by a decision-maker, thus any decision depends on irreversible decisions made in foregoing eras. To understand effects of sequences of decision-making and interactions/dependencies between decisions made, we conduct a series of experiments. More precisely, we fix a set of decision-maker preferences D and the number of eras n t and analyze all |D| nt combinations of decision-maker options. We find that for random uniform instances (a) the final selected solutions mainly depend on the final decision and not on the decision history, (b) solutions are quite robust with respect to the number of unvisited dynamic customers, and (c) solutions of the dynamic approach can even dominate solutions obtained by a clairvoyant EMOA. In contrast, for instances with clustered customers, we observe a strong dependency on decision-making history as well as more variance in solution diversity.","lang":"eng"}],"publication":"Proceedings of the IEEE Congress on Evolutionary Computation (CEC)","citation":{"ama":"Bossek J, Grimme C, Rudolph G, Trautmann H. Towards Decision Support in Dynamic Bi-Objective Vehicle Routing. In: <i>Proceedings of the IEEE Congress on Evolutionary Computation (CEC)</i>. ; 2020:1–8. doi:<a href=\"https://doi.org/10.1109/CEC48606.2020.9185778\">10.1109/CEC48606.2020.9185778</a>","bibtex":"@inproceedings{Bossek_Grimme_Rudolph_Trautmann_2020, place={Glasgow, UK}, title={Towards Decision Support in Dynamic Bi-Objective Vehicle Routing}, DOI={<a href=\"https://doi.org/10.1109/CEC48606.2020.9185778\">10.1109/CEC48606.2020.9185778</a>}, booktitle={Proceedings of the IEEE Congress on Evolutionary Computation (CEC)}, author={Bossek, Jakob and Grimme, Christian and Rudolph, Günter and Trautmann, Heike}, year={2020}, pages={1–8} }","mla":"Bossek, Jakob, et al. “Towards Decision Support in Dynamic Bi-Objective Vehicle Routing.” <i>Proceedings of the IEEE Congress on Evolutionary Computation (CEC)</i>, 2020, pp. 1–8, doi:<a href=\"https://doi.org/10.1109/CEC48606.2020.9185778\">10.1109/CEC48606.2020.9185778</a>.","short":"J. Bossek, C. Grimme, G. Rudolph, H. Trautmann, in: Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Glasgow, UK, 2020, pp. 1–8.","chicago":"Bossek, Jakob, Christian Grimme, Günter Rudolph, and Heike Trautmann. “Towards Decision Support in Dynamic Bi-Objective Vehicle Routing.” In <i>Proceedings of the IEEE Congress on Evolutionary Computation (CEC)</i>, 1–8. Glasgow, UK, 2020. <a href=\"https://doi.org/10.1109/CEC48606.2020.9185778\">https://doi.org/10.1109/CEC48606.2020.9185778</a>.","apa":"Bossek, J., Grimme, C., Rudolph, G., &#38; Trautmann, H. (2020). Towards Decision Support in Dynamic Bi-Objective Vehicle Routing. <i>Proceedings of the IEEE Congress on Evolutionary Computation (CEC)</i>, 1–8. <a href=\"https://doi.org/10.1109/CEC48606.2020.9185778\">https://doi.org/10.1109/CEC48606.2020.9185778</a>","ieee":"J. Bossek, C. Grimme, G. Rudolph, and H. Trautmann, “Towards Decision Support in Dynamic Bi-Objective Vehicle Routing,” in <i>Proceedings of the IEEE Congress on Evolutionary Computation (CEC)</i>, 2020, pp. 1–8, doi: <a href=\"https://doi.org/10.1109/CEC48606.2020.9185778\">10.1109/CEC48606.2020.9185778</a>."}},{"abstract":[{"lang":"eng","text":"The Traveling-Salesperson-Problem (TSP) is arguably one of the best-known NP-hard combinatorial optimization problems. The two sophisticated heuristic solvers LKH and EAX and respective (restart) variants manage to calculate close-to optimal or even optimal solutions, also for large instances with several thousand nodes in reasonable time. In this work we extend existing benchmarking studies by addressing anytime behaviour of inexact TSP solvers based on empirical runtime distributions leading to an increased understanding of solver behaviour and the respective relation to problem hardness. It turns out that performance ranking of solvers is highly dependent on the focused approximation quality. Insights on intersection points of performances offer huge potential for the construction of hybridized solvers depending on instance features. Moreover, instance features tailored to anytime performance and corresponding performance indicators will highly improve automated algorithm selection models by including comprehensive information on solver quality."}],"citation":{"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} }","ama":"Bossek J, Kerschke P, Trautmann H. Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection. In: <i>Proceedings of the IEEE Congress on Evolutionary Computation (CEC)</i>. IEEE; 2020:1–8.","short":"J. Bossek, P. Kerschke, H. Trautmann, in: Proceedings of the IEEE Congress on Evolutionary Computation (CEC), IEEE, Glasgow, UK, 2020, pp. 1–8.","chicago":"Bossek, Jakob, Pascal Kerschke, and Heike Trautmann. “Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection.” In <i>Proceedings of the IEEE Congress on Evolutionary Computation (CEC)</i>, 1–8. Glasgow, UK: IEEE, 2020.","ieee":"J. Bossek, P. Kerschke, and H. Trautmann, “Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection,” in <i>Proceedings of the IEEE Congress on Evolutionary Computation (CEC)</i>, 2020, pp. 1–8.","apa":"Bossek, J., Kerschke, P., &#38; Trautmann, H. (2020). Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection. <i>Proceedings of the IEEE Congress on Evolutionary Computation (CEC)</i>, 1–8.","mla":"Bossek, Jakob, et al. “Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection.” <i>Proceedings of the IEEE Congress on Evolutionary Computation (CEC)</i>, IEEE, 2020, pp. 1–8."},"publication":"Proceedings of the IEEE Congress on Evolutionary Computation (CEC)","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","date_created":"2023-08-04T07:34:40Z","place":"Glasgow, UK","date_updated":"2024-06-10T12:01:46Z","author":[{"orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob","full_name":"Bossek, Jakob","id":"102979"},{"last_name":"Kerschke","first_name":"Pascal","full_name":"Kerschke, Pascal"},{"first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","full_name":"Trautmann, Heike","id":"100740"}],"year":"2020","title":"Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection","status":"public","user_id":"15504","publisher":"IEEE","_id":"46324","language":[{"iso":"eng"}],"page":"1–8"},{"date_updated":"2024-06-10T12:01:57Z","year":"2020","title":"Dynamic Bi-Objective Routing of Multiple Vehicles","status":"public","author":[{"full_name":"Bossek, Jakob","first_name":"Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","id":"102979"},{"first_name":"Christian","last_name":"Grimme","full_name":"Grimme, Christian"},{"id":"100740","full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike","orcid":"0000-0002-9788-8282"}],"user_id":"15504","page":"166–174","publisher":"ACM","_id":"46323","language":[{"iso":"eng"}],"abstract":[{"text":"In practice, e.g. in delivery and service scenarios, Vehicle-Routing-Problems (VRPs) often imply repeated decision making on dynamic customer requests. As in classical VRPs, tours have to be planned short while the number of serviced customers has to be maximized at the same time resulting in a multi-objective problem. Beyond that, however, dynamic requests lead to the need for re-planning of not yet realized tour parts, while already realized tour parts are irreversible. In this paper we study this type of bi-objective dynamic VRP including sequential decision making and concurrent realization of decisions. We adopt a recently proposed Dynamic Evolutionary Multi-Objective Algorithm (DEMOA) for a related VRP problem and extend it to the more realistic (here considered) scenario of multiple vehicles. We empirically show that our DEMOA is competitive with a multi-vehicle offline and clairvoyant variant of the proposed DEMOA as well as with the dynamic single-vehicle approach proposed earlier.","lang":"eng"}],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20)","citation":{"ieee":"J. Bossek, C. Grimme, and H. Trautmann, “Dynamic Bi-Objective Routing of Multiple Vehicles,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20)</i>, 2020, pp. 166–174.","apa":"Bossek, J., Grimme, C., &#38; Trautmann, H. (2020). Dynamic Bi-Objective Routing of Multiple Vehicles. <i>Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20)</i>, 166–174.","chicago":"Bossek, Jakob, Christian Grimme, and Heike Trautmann. “Dynamic Bi-Objective Routing of Multiple Vehicles.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20)</i>, 166–174. Cancun, Mexico: ACM, 2020.","short":"J. Bossek, C. Grimme, H. Trautmann, in: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20), ACM, Cancun, Mexico, 2020, pp. 166–174.","mla":"Bossek, Jakob, et al. “Dynamic Bi-Objective Routing of Multiple Vehicles.” <i>Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20)</i>, ACM, 2020, pp. 166–174.","bibtex":"@inproceedings{Bossek_Grimme_Trautmann_2020, place={Cancun, Mexico}, title={Dynamic Bi-Objective Routing of Multiple Vehicles}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20)}, publisher={ACM}, author={Bossek, Jakob and Grimme, Christian and Trautmann, Heike}, year={2020}, pages={166–174} }","ama":"Bossek J, Grimme C, Trautmann H. Dynamic Bi-Objective Routing of Multiple Vehicles. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’20)</i>. ACM; 2020:166–174."},"type":"conference","department":[{"_id":"34"},{"_id":"819"}],"place":"Cancun, Mexico","date_created":"2023-08-04T07:33:30Z"},{"type":"conference","department":[{"_id":"34"},{"_id":"819"}],"date_created":"2023-08-04T07:49:08Z","abstract":[{"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.","lang":"eng"}],"publication":"Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO)","doi":"10.1007/978-3-030-12598-1_11","language":[{"iso":"eng"}],"series_title":"Lecture Notes in Computer Science","date_updated":"2023-10-16T13:31:03Z","intvolume":"     11411","title":"Multimodality in Multi-Objective Optimization — More Boon than Bane?","year":"2019","author":[{"full_name":"Grimme, Christian","last_name":"Grimme","first_name":"Christian"},{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"},{"full_name":"Trautmann, Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike","id":"100740"}],"place":"East Lansing, MI, USA","citation":{"chicago":"Grimme, Christian, Pascal Kerschke, and Heike Trautmann. “Multimodality in Multi-Objective Optimization — More Boon than Bane?” In <i>Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO)</i>, edited by Kalyanmoy Deb, Erik Goodman, Coello Carlos A. Coello, Kathrin Klamroth, Kaisa Miettinen, Sanaz Mostaghim, and Patrick Reed, 11411:126–138. Lecture Notes in Computer Science. East Lansing, MI, USA: Springer, 2019. <a href=\"https://doi.org/10.1007/978-3-030-12598-1_11\">https://doi.org/10.1007/978-3-030-12598-1_11</a>.","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.","apa":"Grimme, C., Kerschke, P., &#38; Trautmann, H. (2019). Multimodality in Multi-Objective Optimization — More Boon than Bane? In K. Deb, E. Goodman, C. C. A. Coello, K. Klamroth, K. Miettinen, S. Mostaghim, &#38; P. Reed (Eds.), <i>Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO)</i> (Vol. 11411, pp. 126–138). Springer. <a href=\"https://doi.org/10.1007/978-3-030-12598-1_11\">https://doi.org/10.1007/978-3-030-12598-1_11</a>","ieee":"C. Grimme, P. Kerschke, and H. Trautmann, “Multimodality in Multi-Objective Optimization — More Boon than Bane?,” in <i>Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO)</i>, 2019, vol. 11411, pp. 126–138, doi: <a href=\"https://doi.org/10.1007/978-3-030-12598-1_11\">10.1007/978-3-030-12598-1_11</a>.","ama":"Grimme C, Kerschke P, Trautmann H. Multimodality in Multi-Objective Optimization — More Boon than Bane? In: Deb K, Goodman E, Coello CCA, et al., eds. <i>Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO)</i>. Vol 11411. Lecture Notes in Computer Science. Springer; 2019:126–138. doi:<a href=\"https://doi.org/10.1007/978-3-030-12598-1_11\">10.1007/978-3-030-12598-1_11</a>","bibtex":"@inproceedings{Grimme_Kerschke_Trautmann_2019, place={East Lansing, MI, USA}, series={Lecture Notes in Computer Science}, title={Multimodality in Multi-Objective Optimization — More Boon than Bane?}, volume={11411}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-12598-1_11\">10.1007/978-3-030-12598-1_11</a>}, booktitle={Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO)}, publisher={Springer}, author={Grimme, Christian and Kerschke, Pascal and Trautmann, Heike}, editor={Deb, Kalyanmoy and Goodman, Erik and Coello, Coello Carlos A. and Klamroth, Kathrin and Miettinen, Kaisa and Mostaghim, Sanaz and Reed, Patrick}, year={2019}, pages={126–138}, collection={Lecture Notes in Computer Science} }","mla":"Grimme, Christian, et al. “Multimodality in Multi-Objective Optimization — More Boon than Bane?” <i>Proceedings of the 10$^th$ International Conference on Evolutionary Multi-Criterion Optimization (EMO)</i>, edited by Kalyanmoy Deb et al., vol. 11411, Springer, 2019, pp. 126–138, doi:<a href=\"https://doi.org/10.1007/978-3-030-12598-1_11\">10.1007/978-3-030-12598-1_11</a>."},"user_id":"15504","editor":[{"full_name":"Deb, Kalyanmoy","first_name":"Kalyanmoy","last_name":"Deb"},{"first_name":"Erik","last_name":"Goodman","full_name":"Goodman, Erik"},{"full_name":"Coello, Coello Carlos A.","first_name":"Coello Carlos A.","last_name":"Coello"},{"full_name":"Klamroth, Kathrin","first_name":"Kathrin","last_name":"Klamroth"},{"last_name":"Miettinen","first_name":"Kaisa","full_name":"Miettinen, Kaisa"},{"full_name":"Mostaghim, Sanaz","first_name":"Sanaz","last_name":"Mostaghim"},{"first_name":"Patrick","last_name":"Reed","full_name":"Reed, Patrick"}],"volume":11411,"page":"126–138","_id":"46343","publisher":"Springer","status":"public"},{"date_created":"2023-08-04T07:50:33Z","type":"journal_article","department":[{"_id":"34"},{"_id":"819"}],"publication":"Evolutionary Computation (ECJ)","issue":"1","citation":{"short":"P. Kerschke, H.H. Hoos, F. Neumann, H. Trautmann, Evolutionary Computation (ECJ) 27 (2019) 3–45.","chicago":"Kerschke, Pascal, Holger H Hoos, Frank Neumann, and Heike Trautmann. “Automated Algorithm Selection: Survey and Perspectives.” <i>Evolutionary Computation (ECJ)</i> 27, no. 1 (2019): 3–45. <a href=\"https://doi.org/10.1162/evco_a_00242\">https://doi.org/10.1162/evco_a_00242</a>.","apa":"Kerschke, P., Hoos, H. H., Neumann, F., &#38; Trautmann, H. (2019). Automated Algorithm Selection: Survey and Perspectives. <i>Evolutionary Computation (ECJ)</i>, <i>27</i>(1), 3–45. <a href=\"https://doi.org/10.1162/evco_a_00242\">https://doi.org/10.1162/evco_a_00242</a>","ieee":"P. Kerschke, H. H. Hoos, F. Neumann, and H. Trautmann, “Automated Algorithm Selection: Survey and Perspectives,” <i>Evolutionary Computation (ECJ)</i>, vol. 27, no. 1, pp. 3–45, 2019, doi: <a href=\"https://doi.org/10.1162/evco_a_00242\">10.1162/evco_a_00242</a>.","ama":"Kerschke P, Hoos HH, Neumann F, Trautmann H. Automated Algorithm Selection: Survey and Perspectives. <i>Evolutionary Computation (ECJ)</i>. 2019;27(1):3–45. doi:<a href=\"https://doi.org/10.1162/evco_a_00242\">10.1162/evco_a_00242</a>","bibtex":"@article{Kerschke_Hoos_Neumann_Trautmann_2019, title={Automated Algorithm Selection: Survey and Perspectives}, volume={27}, DOI={<a href=\"https://doi.org/10.1162/evco_a_00242\">10.1162/evco_a_00242</a>}, number={1}, journal={Evolutionary Computation (ECJ)}, author={Kerschke, Pascal and Hoos, Holger H and Neumann, Frank and Trautmann, Heike}, year={2019}, pages={3–45} }","mla":"Kerschke, Pascal, et al. “Automated Algorithm Selection: Survey and Perspectives.” <i>Evolutionary Computation (ECJ)</i>, vol. 27, no. 1, 2019, pp. 3–45, doi:<a href=\"https://doi.org/10.1162/evco_a_00242\">10.1162/evco_a_00242</a>."},"abstract":[{"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.","lang":"eng"}],"page":"3–45","language":[{"iso":"eng"}],"_id":"46345","doi":"10.1162/evco_a_00242","user_id":"15504","volume":27,"year":"2019","status":"public","title":"Automated Algorithm Selection: Survey and Perspectives","author":[{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"first_name":"Holger H","last_name":"Hoos","full_name":"Hoos, Holger H"},{"last_name":"Neumann","first_name":"Frank","full_name":"Neumann, Frank"},{"full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike","orcid":"0000-0002-9788-8282","id":"100740"}],"date_updated":"2023-10-16T13:31:40Z","intvolume":"        27"},{"type":"journal_article","department":[{"_id":"34"},{"_id":"819"}],"date_created":"2023-08-04T07:49:47Z","abstract":[{"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.","lang":"eng"}],"publication":"Business and Information Systems Engineering (BISE)","issue":"3","citation":{"bibtex":"@article{Carnein_Trautmann_2019, title={Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms}, volume={61}, number={3}, journal={Business and Information Systems Engineering (BISE)}, author={Carnein, Matthias and Trautmann, Heike}, year={2019}, pages={277–297} }","chicago":"Carnein, Matthias, and Heike Trautmann. “Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms.” <i>Business and Information Systems Engineering (BISE)</i> 61, no. 3 (2019): 277–297.","ama":"Carnein M, Trautmann H. Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms. <i>Business and Information Systems Engineering (BISE)</i>. 2019;61(3):277–297.","short":"M. Carnein, H. Trautmann, Business and Information Systems Engineering (BISE) 61 (2019) 277–297.","ieee":"M. Carnein and H. Trautmann, “Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms,” <i>Business and Information Systems Engineering (BISE)</i>, vol. 61, no. 3, pp. 277–297, 2019.","apa":"Carnein, M., &#38; Trautmann, H. (2019). Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms. <i>Business and Information Systems Engineering (BISE)</i>, <i>61</i>(3), 277–297.","mla":"Carnein, Matthias, and Heike Trautmann. “Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms.” <i>Business and Information Systems Engineering (BISE)</i>, vol. 61, no. 3, 2019, pp. 277–297."},"user_id":"15504","volume":61,"page":"277–297","_id":"46344","language":[{"iso":"eng"}],"date_updated":"2023-10-16T13:31:21Z","intvolume":"        61","status":"public","year":"2019","title":"Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms","author":[{"first_name":"Matthias","last_name":"Carnein","full_name":"Carnein, Matthias"},{"id":"100740","orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike","full_name":"Trautmann, Heike"}]},{"date_updated":"2023-10-16T13:29:53Z","author":[{"full_name":"Carnein, Matthias","first_name":"Matthias","last_name":"Carnein"},{"full_name":"Homann, Leschek","last_name":"Homann","first_name":"Leschek"},{"first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike","id":"100740"},{"full_name":"Vossen, Gottfried","first_name":"Gottfried","last_name":"Vossen"}],"title":"A Recommender System Based on Omni-Channel Customer Data","year":"2019","status":"public","user_id":"15504","language":[{"iso":"eng"}],"_id":"46340","page":"65–74","abstract":[{"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.","lang":"eng"}],"citation":{"apa":"Carnein, M., Homann, L., Trautmann, H., &#38; Vossen, G. (2019). A Recommender System Based on Omni-Channel Customer Data. <i>Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19)</i>, 65–74.","ieee":"M. Carnein, L. Homann, H. Trautmann, and G. Vossen, “A Recommender System Based on Omni-Channel Customer Data,” in <i>Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19)</i>, 2019, pp. 65–74.","short":"M. Carnein, L. Homann, H. Trautmann, G. Vossen, in: Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19), Moscow, Russia, 2019, pp. 65–74.","chicago":"Carnein, Matthias, Leschek Homann, Heike Trautmann, and Gottfried Vossen. “A Recommender System Based on Omni-Channel Customer Data.” In <i>Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19)</i>, 65–74. Moscow, Russia, 2019.","mla":"Carnein, Matthias, et al. “A Recommender System Based on Omni-Channel Customer Data.” <i>Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19)</i>, 2019, pp. 65–74.","ama":"Carnein M, Homann L, Trautmann H, Vossen G. A Recommender System Based on Omni-Channel Customer Data. In: <i>Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19)</i>. ; 2019:65–74.","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} }"},"publication":"Proceedings of the 21$^st$ IEEE Conference on Business Informatics (CBI’ 19)","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","date_created":"2023-08-04T07:46:20Z","place":"Moscow, Russia"},{"type":"conference","department":[{"_id":"34"},{"_id":"819"}],"place":"Macau, China","date_created":"2023-08-04T07:47:20Z","abstract":[{"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.","lang":"eng"}],"publication":"Proceedings of the 23$^rd$ Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD ’19)","citation":{"chicago":"Carnein, Matthias, and Heike Trautmann. “Customer Segmentation Based on Transactional Data Using Stream Clustering.” In <i>Proceedings of the 23$^rd$ Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD ’19)</i>, 280–292. Macau, China, 2019.","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.","ieee":"M. Carnein and H. Trautmann, “Customer Segmentation Based on Transactional Data Using Stream Clustering,” in <i>Proceedings of the 23$^rd$ Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD ’19)</i>, 2019, pp. 280–292.","apa":"Carnein, M., &#38; Trautmann, H. (2019). Customer Segmentation Based on Transactional Data Using Stream Clustering. <i>Proceedings of the 23$^rd$ Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD ’19)</i>, 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} }","ama":"Carnein M, Trautmann H. Customer Segmentation Based on Transactional Data Using Stream Clustering. In: <i>Proceedings of the 23$^rd$ Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD ’19)</i>. ; 2019:280–292.","mla":"Carnein, Matthias, and Heike Trautmann. “Customer Segmentation Based on Transactional Data Using Stream Clustering.” <i>Proceedings of the 23$^rd$ Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD ’19)</i>, 2019, pp. 280–292."},"user_id":"15504","page":"280–292","_id":"46341","language":[{"iso":"eng"}],"date_updated":"2023-10-16T13:30:10Z","status":"public","title":"Customer Segmentation Based on Transactional Data Using Stream Clustering","year":"2019","author":[{"first_name":"Matthias","last_name":"Carnein","full_name":"Carnein, Matthias"},{"last_name":"Trautmann","first_name":"Heike","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike","id":"100740"}]},{"type":"conference","department":[{"_id":"34"},{"_id":"819"}],"date_created":"2023-08-04T07:48:15Z","place":"Leiden, The Netherlands","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."}],"publication":"AIP Conference Proceedings","citation":{"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.","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 <i>AIP Conference Proceedings</i>, 020052-1-020052–54. Leiden, The Netherlands: AIP Publishing, 2019. <a href=\"https://doi.org/10.1063/1.5090019\">https://doi.org/10.1063/1.5090019</a>.","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 <i>AIP Conference Proceedings</i>, 2019, pp. 020052-1-020052–4, doi: <a href=\"https://doi.org/10.1063/1.5090019\">10.1063/1.5090019</a>.","apa":"Grimme, C., Kerschke, P., Emmerich, M. T. M., Preuss, M., Deutz, A. H., &#38; Trautmann, H. (2019). Sliding to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal Multi-Objective Optimization. <i>AIP Conference Proceedings</i>, 020052-1-020052–020054. <a href=\"https://doi.org/10.1063/1.5090019\">https://doi.org/10.1063/1.5090019</a>","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={<a href=\"https://doi.org/10.1063/1.5090019\">10.1063/1.5090019</a>}, 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} }","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: <i>AIP Conference Proceedings</i>. AIP Publishing; 2019:020052-1-020052-020054. doi:<a href=\"https://doi.org/10.1063/1.5090019\">10.1063/1.5090019</a>","mla":"Grimme, Christian, et al. “Sliding to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal Multi-Objective Optimization.” <i>AIP Conference Proceedings</i>, AIP Publishing, 2019, pp. 020052-1-020052–54, doi:<a href=\"https://doi.org/10.1063/1.5090019\">10.1063/1.5090019</a>."},"user_id":"15504","doi":"10.1063/1.5090019","page":"020052-1-020052-4","_id":"46342","publisher":"AIP Publishing","language":[{"iso":"eng"}],"date_updated":"2023-10-16T13:30:43Z","year":"2019","status":"public","title":"Sliding to the Global Optimum: How to Benefit from Non-Global Optima in Multimodal Multi-Objective Optimization","author":[{"last_name":"Grimme","first_name":"Christian","full_name":"Grimme, Christian"},{"full_name":"Kerschke, Pascal","first_name":"Pascal","last_name":"Kerschke"},{"full_name":"Emmerich, Michael T M","last_name":"Emmerich","first_name":"Michael T M"},{"full_name":"Preuss, Mike","last_name":"Preuss","first_name":"Mike"},{"first_name":"André H","last_name":"Deutz","full_name":"Deutz, André H"},{"id":"100740","last_name":"Trautmann","first_name":"Heike","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike"}]},{"department":[{"_id":"34"},{"_id":"819"}],"type":"book_chapter","date_created":"2023-08-04T07:43:30Z","abstract":[{"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.","lang":"eng"}],"citation":{"mla":"Kerschke, Pascal, and Heike Trautmann. “Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package Flacco.” <i>Applications in Statistical Computing</i>, edited by Nadja Bauer et al., Springer, 2019, pp. 93–123, doi:<a href=\"https://doi.org/10.1007/978-3-030-25147-5_7\">10.1007/978-3-030-25147-5_7</a>.","bibtex":"@inbook{Kerschke_Trautmann_2019, title={Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-package flacco}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-25147-5_7\">10.1007/978-3-030-25147-5_7</a>}, 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} }","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. <i>Applications in Statistical Computing</i>. Springer; 2019:93–123. doi:<a href=\"https://doi.org/10.1007/978-3-030-25147-5_7\">10.1007/978-3-030-25147-5_7</a>","ieee":"P. Kerschke and H. Trautmann, “Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-package flacco,” in <i>Applications in Statistical Computing</i>, N. Bauer, K. Ickstadt, K. Lübke, G. Szepannek, H. Trautmann, and M. Vichi, Eds. Springer, 2019, pp. 93–123.","apa":"Kerschke, P., &#38; 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, &#38; M. Vichi (Eds.), <i>Applications in Statistical Computing</i> (pp. 93–123). Springer. <a href=\"https://doi.org/10.1007/978-3-030-25147-5_7\">https://doi.org/10.1007/978-3-030-25147-5_7</a>","chicago":"Kerschke, Pascal, and Heike Trautmann. “Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package Flacco.” In <i>Applications in Statistical Computing</i>, edited by Nadja Bauer, Katja Ickstadt, Karsten Lübke, Gero Szepannek, Heike Trautmann, and Maurizio Vichi, 93–123. Springer, 2019. <a href=\"https://doi.org/10.1007/978-3-030-25147-5_7\">https://doi.org/10.1007/978-3-030-25147-5_7</a>.","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."},"publication":"Applications in Statistical Computing","editor":[{"full_name":"Bauer, Nadja","first_name":"Nadja","last_name":"Bauer"},{"first_name":"Katja","last_name":"Ickstadt","full_name":"Ickstadt, Katja"},{"last_name":"Lübke","first_name":"Karsten","full_name":"Lübke, Karsten"},{"last_name":"Szepannek","first_name":"Gero","full_name":"Szepannek, Gero"},{"first_name":"Heike","last_name":"Trautmann","full_name":"Trautmann, Heike"},{"first_name":"Maurizio","last_name":"Vichi","full_name":"Vichi, Maurizio"}],"doi":"10.1007/978-3-030-25147-5_7","user_id":"15504","language":[{"iso":"eng"}],"_id":"46336","publisher":"Springer","page":"93–123","date_updated":"2023-10-16T13:08:22Z","author":[{"last_name":"Kerschke","first_name":"Pascal","full_name":"Kerschke, Pascal"},{"id":"100740","first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","full_name":"Trautmann, Heike"}],"title":"Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-package flacco","year":"2019","status":"public"},{"date_updated":"2023-10-16T13:07:21Z","year":"2019","status":"public","title":"Applications in Statistical Computing — From Music Data Analysis to Industrial Quality Improvement","author":[{"id":"100740","full_name":"Trautmann, Heike","first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann"}],"publication_identifier":{"isbn":["978-3-030-25147-5"]},"user_id":"15504","language":[{"iso":"eng"}],"_id":"46335","publisher":"Springer International Publishing","series_title":"Studies in Classification, Data Analysis, and Knowledge Organization","citation":{"apa":"Trautmann, H. (2019). <i>Applications in Statistical Computing — From Music Data Analysis to Industrial Quality Improvement</i>. Springer International Publishing.","mla":"Trautmann, Heike. <i>Applications in Statistical Computing — From Music Data Analysis to Industrial Quality Improvement</i>. Springer International Publishing, 2019.","ieee":"H. Trautmann, <i>Applications in Statistical Computing — From Music Data Analysis to Industrial Quality Improvement</i>. Springer International Publishing, 2019.","chicago":"Trautmann, Heike. <i>Applications in Statistical Computing — From Music Data Analysis to Industrial Quality Improvement</i>. Studies in Classification, Data Analysis, and Knowledge Organization. Springer International Publishing, 2019.","short":"H. Trautmann, Applications in Statistical Computing — From Music Data Analysis to Industrial Quality Improvement, Springer International Publishing, 2019.","ama":"Trautmann H. <i>Applications in Statistical Computing — From Music Data Analysis to Industrial Quality Improvement</i>. Springer International Publishing; 2019.","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} }"},"type":"book","department":[{"_id":"34"},{"_id":"819"}],"date_created":"2023-08-04T07:43:09Z"},{"date_updated":"2023-10-16T13:31:57Z","intvolume":"        27","year":"2019","status":"public","title":"Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning","author":[{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"},{"id":"100740","orcid":"0000-0002-9788-8282","first_name":"Heike","last_name":"Trautmann","full_name":"Trautmann, Heike"}],"doi":"10.1162/evco_a_00236","user_id":"15504","volume":27,"page":"99–127","language":[{"iso":"eng"}],"_id":"46346","abstract":[{"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.","lang":"eng"}],"publication":"Evolutionary Computation (ECJ)","issue":"1","citation":{"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={<a href=\"https://doi.org/10.1162/evco_a_00236\">10.1162/evco_a_00236</a>}, number={1}, journal={Evolutionary Computation (ECJ)}, author={Kerschke, Pascal and Trautmann, Heike}, year={2019}, pages={99–127} }","ama":"Kerschke P, Trautmann H. Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning. <i>Evolutionary Computation (ECJ)</i>. 2019;27(1):99–127. doi:<a href=\"https://doi.org/10.1162/evco_a_00236\">10.1162/evco_a_00236</a>","mla":"Kerschke, Pascal, and Heike Trautmann. “Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning.” <i>Evolutionary Computation (ECJ)</i>, vol. 27, no. 1, 2019, pp. 99–127, doi:<a href=\"https://doi.org/10.1162/evco_a_00236\">10.1162/evco_a_00236</a>.","short":"P. Kerschke, H. Trautmann, Evolutionary Computation (ECJ) 27 (2019) 99–127.","chicago":"Kerschke, Pascal, and Heike Trautmann. “Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning.” <i>Evolutionary Computation (ECJ)</i> 27, no. 1 (2019): 99–127. <a href=\"https://doi.org/10.1162/evco_a_00236\">https://doi.org/10.1162/evco_a_00236</a>.","ieee":"P. Kerschke and H. Trautmann, “Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning,” <i>Evolutionary Computation (ECJ)</i>, vol. 27, no. 1, pp. 99–127, 2019, doi: <a href=\"https://doi.org/10.1162/evco_a_00236\">10.1162/evco_a_00236</a>.","apa":"Kerschke, P., &#38; Trautmann, H. (2019). Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning. <i>Evolutionary Computation (ECJ)</i>, <i>27</i>(1), 99–127. <a href=\"https://doi.org/10.1162/evco_a_00236\">https://doi.org/10.1162/evco_a_00236</a>"},"type":"journal_article","department":[{"_id":"34"},{"_id":"819"}],"date_created":"2023-08-04T07:51:18Z"},{"author":[{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"},{"full_name":"Wang, Hao","last_name":"Wang","first_name":"Hao"},{"last_name":"Preuss","first_name":"Mike","full_name":"Preuss, Mike"},{"full_name":"Grimme, Christian","last_name":"Grimme","first_name":"Christian"},{"first_name":"André","last_name":"Deutz","full_name":"Deutz, André"},{"id":"100740","last_name":"Trautmann","orcid":"0000-0002-9788-8282","first_name":"Heike","full_name":"Trautmann, Heike"},{"full_name":"Emmerich, Michael","first_name":"Michael","last_name":"Emmerich"}],"year":"2019","title":"Search Dynamics on Multimodal Multi-Objective Problems","status":"public","intvolume":"        27","date_updated":"2023-10-16T13:32:18Z","language":[{"iso":"eng"}],"_id":"46347","page":"577–609","volume":27,"user_id":"15504","doi":"10.1162/evco_a_00234","citation":{"ama":"Kerschke P, Wang H, Preuss M, et al. Search Dynamics on Multimodal Multi-Objective Problems. <i>Evolutionary Computation (ECJ)</i>. 2019;27(4):577–609. doi:<a href=\"https://doi.org/10.1162/evco_a_00234\">10.1162/evco_a_00234</a>","bibtex":"@article{Kerschke_Wang_Preuss_Grimme_Deutz_Trautmann_Emmerich_2019, title={Search Dynamics on Multimodal Multi-Objective Problems}, volume={27}, DOI={<a href=\"https://doi.org/10.1162/evco_a_00234\">10.1162/evco_a_00234</a>}, 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} }","mla":"Kerschke, Pascal, et al. “Search Dynamics on Multimodal Multi-Objective Problems.” <i>Evolutionary Computation (ECJ)</i>, vol. 27, no. 4, 2019, pp. 577–609, doi:<a href=\"https://doi.org/10.1162/evco_a_00234\">10.1162/evco_a_00234</a>.","chicago":"Kerschke, Pascal, Hao Wang, Mike Preuss, Christian Grimme, André Deutz, Heike Trautmann, and Michael Emmerich. “Search Dynamics on Multimodal Multi-Objective Problems.” <i>Evolutionary Computation (ECJ)</i> 27, no. 4 (2019): 577–609. <a href=\"https://doi.org/10.1162/evco_a_00234\">https://doi.org/10.1162/evco_a_00234</a>.","short":"P. Kerschke, H. Wang, M. Preuss, C. Grimme, A. Deutz, H. Trautmann, M. Emmerich, Evolutionary Computation (ECJ) 27 (2019) 577–609.","apa":"Kerschke, P., Wang, H., Preuss, M., Grimme, C., Deutz, A., Trautmann, H., &#38; Emmerich, M. (2019). Search Dynamics on Multimodal Multi-Objective Problems. <i>Evolutionary Computation (ECJ)</i>, <i>27</i>(4), 577–609. <a href=\"https://doi.org/10.1162/evco_a_00234\">https://doi.org/10.1162/evco_a_00234</a>","ieee":"P. Kerschke <i>et al.</i>, “Search Dynamics on Multimodal Multi-Objective Problems,” <i>Evolutionary Computation (ECJ)</i>, vol. 27, no. 4, pp. 577–609, 2019, doi: <a href=\"https://doi.org/10.1162/evco_a_00234\">10.1162/evco_a_00234</a>."},"publication":"Evolutionary Computation (ECJ)","issue":"4","abstract":[{"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.","lang":"eng"}],"date_created":"2023-08-04T07:52:06Z","department":[{"_id":"34"},{"_id":"819"}],"type":"journal_article"},{"publication":"Evolutionary Multi-Criterion Optimization (EMO)","abstract":[{"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.","lang":"eng"}],"extern":"1","date_created":"2023-11-14T15:58:52Z","department":[{"_id":"819"}],"type":"conference","keyword":["Combinatorial optimization","Dynamic optimization","Metaheuristics","Multi-objective optimization","Vehicle routing"],"publication_identifier":{"isbn":["978-3-030-12598-1"]},"author":[{"id":"102979","full_name":"Bossek, Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob"},{"first_name":"Christian","last_name":"Grimme","full_name":"Grimme, Christian"},{"last_name":"Meisel","first_name":"Stephan","full_name":"Meisel, Stephan"},{"full_name":"Rudolph, Günter","first_name":"Günter","last_name":"Rudolph"},{"full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike"}],"title":"Bi-Objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm","year":"2019","date_updated":"2023-12-13T10:43:07Z","publication_status":"published","series_title":"Lecture Notes in Computer Science","language":[{"iso":"eng"}],"doi":"10.1007/978-3-030-12598-1_41","citation":{"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.","chicago":"Bossek, Jakob, Christian Grimme, Stephan Meisel, Günter Rudolph, and Heike Trautmann. “Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm.” In <i>Evolutionary Multi-Criterion Optimization (EMO)</i>, edited by Kalyanmoy Deb, Erik Goodman, Carlos A. Coello Coello, Kathrin Klamroth, Kaisa Miettinen, Sanaz Mostaghim, and Patrick Reed, 516–528. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2019. <a href=\"https://doi.org/10.1007/978-3-030-12598-1_41\">https://doi.org/10.1007/978-3-030-12598-1_41</a>.","ieee":"J. Bossek, C. Grimme, S. Meisel, G. Rudolph, and H. Trautmann, “Bi-Objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm,” in <i>Evolutionary Multi-Criterion Optimization (EMO)</i>, 2019, pp. 516–528, doi: <a href=\"https://doi.org/10.1007/978-3-030-12598-1_41\">10.1007/978-3-030-12598-1_41</a>.","apa":"Bossek, J., Grimme, C., Meisel, S., Rudolph, G., &#38; Trautmann, H. (2019). Bi-Objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm. In K. Deb, E. Goodman, C. A. Coello Coello, K. Klamroth, K. Miettinen, S. Mostaghim, &#38; P. Reed (Eds.), <i>Evolutionary Multi-Criterion Optimization (EMO)</i> (pp. 516–528). Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-030-12598-1_41\">https://doi.org/10.1007/978-3-030-12598-1_41</a>","bibtex":"@inproceedings{Bossek_Grimme_Meisel_Rudolph_Trautmann_2019, place={Cham}, series={Lecture Notes in Computer Science}, title={Bi-Objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-12598-1_41\">10.1007/978-3-030-12598-1_41</a>}, booktitle={Evolutionary Multi-Criterion Optimization (EMO)}, publisher={Springer International Publishing}, author={Bossek, Jakob and Grimme, Christian and Meisel, Stephan and Rudolph, Günter and Trautmann, Heike}, editor={Deb, Kalyanmoy and Goodman, Erik and Coello Coello, Carlos A. and Klamroth, Kathrin and Miettinen, Kaisa and Mostaghim, Sanaz and Reed, Patrick}, year={2019}, pages={516–528}, collection={Lecture Notes in Computer Science} }","ama":"Bossek J, Grimme C, Meisel S, Rudolph G, Trautmann H. Bi-Objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm. In: Deb K, Goodman E, Coello Coello CA, et al., eds. <i>Evolutionary Multi-Criterion Optimization (EMO)</i>. Lecture Notes in Computer Science. Springer International Publishing; 2019:516–528. doi:<a href=\"https://doi.org/10.1007/978-3-030-12598-1_41\">10.1007/978-3-030-12598-1_41</a>","mla":"Bossek, Jakob, et al. “Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm.” <i>Evolutionary Multi-Criterion Optimization (EMO)</i>, edited by Kalyanmoy Deb et al., Springer International Publishing, 2019, pp. 516–528, doi:<a href=\"https://doi.org/10.1007/978-3-030-12598-1_41\">10.1007/978-3-030-12598-1_41</a>."},"place":"Cham","status":"public","_id":"48841","publisher":"Springer International Publishing","page":"516–528","editor":[{"last_name":"Deb","first_name":"Kalyanmoy","full_name":"Deb, Kalyanmoy"},{"first_name":"Erik","last_name":"Goodman","full_name":"Goodman, Erik"},{"last_name":"Coello Coello","first_name":"Carlos A.","full_name":"Coello Coello, Carlos A."},{"full_name":"Klamroth, Kathrin","last_name":"Klamroth","first_name":"Kathrin"},{"last_name":"Miettinen","first_name":"Kaisa","full_name":"Miettinen, Kaisa"},{"full_name":"Mostaghim, Sanaz","last_name":"Mostaghim","first_name":"Sanaz"},{"full_name":"Reed, Patrick","last_name":"Reed","first_name":"Patrick"}],"user_id":"102979"},{"date_updated":"2023-12-13T10:42:57Z","publication_status":"published","title":"Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators","year":"2019","author":[{"full_name":"Bossek, Jakob","first_name":"Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","id":"102979"},{"last_name":"Kerschke","first_name":"Pascal","full_name":"Kerschke, Pascal"},{"last_name":"Neumann","first_name":"Aneta","full_name":"Neumann, Aneta"},{"last_name":"Wagner","first_name":"Markus","full_name":"Wagner, Markus"},{"last_name":"Neumann","first_name":"Frank","full_name":"Neumann, Frank"},{"full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike"}],"publication_identifier":{"isbn":["978-1-4503-6254-2"]},"doi":"10.1145/3299904.3340307","series_title":"FOGA ’19","language":[{"iso":"eng"}],"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."}],"extern":"1","publication":"Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms","type":"conference","keyword":["benchmarking","instance features","optimization","problem generation","traveling salesperson problem"],"department":[{"_id":"819"}],"date_created":"2023-11-14T15:58:52Z","status":"public","user_id":"102979","page":"58–71","_id":"48842","publisher":"Association for Computing Machinery","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: <i>Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>. FOGA ’19. Association for Computing Machinery; 2019:58–71. doi:<a href=\"https://doi.org/10.1145/3299904.3340307\">10.1145/3299904.3340307</a>","bibtex":"@inproceedings{Bossek_Kerschke_Neumann_Wagner_Neumann_Trautmann_2019, place={New York, NY, USA}, series={FOGA ’19}, title={Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators}, DOI={<a href=\"https://doi.org/10.1145/3299904.3340307\">10.1145/3299904.3340307</a>}, booktitle={Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Kerschke, Pascal and Neumann, Aneta and Wagner, Markus and Neumann, Frank and Trautmann, Heike}, year={2019}, pages={58–71}, collection={FOGA ’19} }","mla":"Bossek, Jakob, et al. “Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators.” <i>Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, Association for Computing Machinery, 2019, pp. 58–71, doi:<a href=\"https://doi.org/10.1145/3299904.3340307\">10.1145/3299904.3340307</a>.","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.","chicago":"Bossek, Jakob, Pascal Kerschke, Aneta Neumann, Markus Wagner, Frank Neumann, and Heike Trautmann. “Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators.” In <i>Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, 58–71. FOGA ’19. New York, NY, USA: Association for Computing Machinery, 2019. <a href=\"https://doi.org/10.1145/3299904.3340307\">https://doi.org/10.1145/3299904.3340307</a>.","apa":"Bossek, J., Kerschke, P., Neumann, A., Wagner, M., Neumann, F., &#38; Trautmann, H. (2019). Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators. <i>Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, 58–71. <a href=\"https://doi.org/10.1145/3299904.3340307\">https://doi.org/10.1145/3299904.3340307</a>","ieee":"J. Bossek, P. Kerschke, A. Neumann, M. Wagner, F. Neumann, and H. Trautmann, “Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators,” in <i>Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, 2019, pp. 58–71, doi: <a href=\"https://doi.org/10.1145/3299904.3340307\">10.1145/3299904.3340307</a>."},"place":"New York, NY, USA"},{"place":"New York, NY, USA","citation":{"ama":"Bossek J, Neumann F, Peng P, Sudholt D. Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO ’19. Association for Computing Machinery; 2019:1443–1451. doi:<a href=\"https://doi.org/10.1145/3321707.3321792\">10.1145/3321707.3321792</a>","bibtex":"@inproceedings{Bossek_Neumann_Peng_Sudholt_2019, place={New York, NY, USA}, series={GECCO ’19}, title={Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring}, DOI={<a href=\"https://doi.org/10.1145/3321707.3321792\">10.1145/3321707.3321792</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Neumann, Frank and Peng, Pan and Sudholt, Dirk}, year={2019}, pages={1443–1451}, collection={GECCO ’19} }","mla":"Bossek, Jakob, et al. “Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2019, pp. 1443–1451, doi:<a href=\"https://doi.org/10.1145/3321707.3321792\">10.1145/3321707.3321792</a>.","chicago":"Bossek, Jakob, Frank Neumann, Pan Peng, and Dirk Sudholt. “Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 1443–1451. GECCO ’19. New York, NY, USA: Association for Computing Machinery, 2019. <a href=\"https://doi.org/10.1145/3321707.3321792\">https://doi.org/10.1145/3321707.3321792</a>.","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.","apa":"Bossek, J., Neumann, F., Peng, P., &#38; Sudholt, D. (2019). Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 1443–1451. <a href=\"https://doi.org/10.1145/3321707.3321792\">https://doi.org/10.1145/3321707.3321792</a>","ieee":"J. Bossek, F. Neumann, P. Peng, and D. Sudholt, “Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2019, pp. 1443–1451, doi: <a href=\"https://doi.org/10.1145/3321707.3321792\">10.1145/3321707.3321792</a>."},"user_id":"102979","publisher":"Association for Computing Machinery","_id":"48843","page":"1443–1451","status":"public","department":[{"_id":"819"}],"type":"conference","keyword":["dynamic optimization","evolutionary algorithms","running time analysis","theory"],"date_created":"2023-11-14T15:58:52Z","extern":"1","abstract":[{"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.","lang":"eng"}],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference","doi":"10.1145/3321707.3321792","language":[{"iso":"eng"}],"series_title":"GECCO ’19","publication_status":"published","date_updated":"2023-12-13T10:42:37Z","publication_identifier":{"isbn":["978-1-4503-6111-8"]},"author":[{"id":"102979","last_name":"Bossek","orcid":"0000-0002-4121-4668","first_name":"Jakob","full_name":"Bossek, Jakob"},{"first_name":"Frank","last_name":"Neumann","full_name":"Neumann, Frank"},{"first_name":"Pan","last_name":"Peng","full_name":"Peng, Pan"},{"last_name":"Sudholt","first_name":"Dirk","full_name":"Sudholt, Dirk"}],"year":"2019","title":"Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring"},{"date_created":"2023-11-14T15:58:52Z","type":"conference","keyword":["biased mutation","combinatorial optimization","minimum spanning tree","multi-objective optimization"],"department":[{"_id":"819"}],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference","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."}],"extern":"1","language":[{"iso":"eng"}],"series_title":"GECCO ’19","doi":"10.1145/3321707.3321818","year":"2019","title":"On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem","publication_identifier":{"isbn":["978-1-4503-6111-8"]},"author":[{"full_name":"Bossek, Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","first_name":"Jakob","id":"102979"},{"first_name":"Christian","last_name":"Grimme","full_name":"Grimme, Christian"},{"full_name":"Neumann, Frank","first_name":"Frank","last_name":"Neumann"}],"date_updated":"2023-12-13T10:42:24Z","publication_status":"published","place":"New York, NY, USA","citation":{"short":"J. Bossek, C. Grimme, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2019, pp. 516–523.","chicago":"Bossek, Jakob, Christian Grimme, and Frank Neumann. “On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 516–523. GECCO ’19. New York, NY, USA: Association for Computing Machinery, 2019. <a href=\"https://doi.org/10.1145/3321707.3321818\">https://doi.org/10.1145/3321707.3321818</a>.","apa":"Bossek, J., Grimme, C., &#38; Neumann, F. (2019). On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 516–523. <a href=\"https://doi.org/10.1145/3321707.3321818\">https://doi.org/10.1145/3321707.3321818</a>","ieee":"J. Bossek, C. Grimme, and F. Neumann, “On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2019, pp. 516–523, doi: <a href=\"https://doi.org/10.1145/3321707.3321818\">10.1145/3321707.3321818</a>.","ama":"Bossek J, Grimme C, Neumann F. On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO ’19. Association for Computing Machinery; 2019:516–523. doi:<a href=\"https://doi.org/10.1145/3321707.3321818\">10.1145/3321707.3321818</a>","bibtex":"@inproceedings{Bossek_Grimme_Neumann_2019, place={New York, NY, USA}, series={GECCO ’19}, title={On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem}, DOI={<a href=\"https://doi.org/10.1145/3321707.3321818\">10.1145/3321707.3321818</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Grimme, Christian and Neumann, Frank}, year={2019}, pages={516–523}, collection={GECCO ’19} }","mla":"Bossek, Jakob, et al. “On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2019, pp. 516–523, doi:<a href=\"https://doi.org/10.1145/3321707.3321818\">10.1145/3321707.3321818</a>."},"page":"516–523","publisher":"Association for Computing Machinery","_id":"48840","user_id":"102979","status":"public"},{"status":"public","editor":[{"first_name":"Roberto","last_name":"Battiti","full_name":"Battiti, Roberto"},{"full_name":"Brunato, Mauro","last_name":"Brunato","first_name":"Mauro"},{"full_name":"Kotsireas, Ilias","first_name":"Ilias","last_name":"Kotsireas"},{"first_name":"Panos M.","last_name":"Pardalos","full_name":"Pardalos, Panos M."}],"user_id":"102979","_id":"48858","publisher":"Springer International Publishing","page":"184–198","citation":{"chicago":"Bossek, Jakob, and Christian Grimme. “Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-Criteria Shortest Path Problems.” In <i>Learning and Intelligent Optimization</i>, edited by Roberto Battiti, Mauro Brunato, Ilias Kotsireas, and Panos M. Pardalos, 184–198. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2019. <a href=\"https://doi.org/10.1007/978-3-030-05348-2_17\">https://doi.org/10.1007/978-3-030-05348-2_17</a>.","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.","ieee":"J. Bossek and C. Grimme, “Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-criteria Shortest Path Problems,” in <i>Learning and Intelligent Optimization</i>, 2019, pp. 184–198, doi: <a href=\"https://doi.org/10.1007/978-3-030-05348-2_17\">10.1007/978-3-030-05348-2_17</a>.","apa":"Bossek, J., &#38; Grimme, C. (2019). Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-criteria Shortest Path Problems. In R. Battiti, M. Brunato, I. Kotsireas, &#38; P. M. Pardalos (Eds.), <i>Learning and Intelligent Optimization</i> (pp. 184–198). Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-030-05348-2_17\">https://doi.org/10.1007/978-3-030-05348-2_17</a>","bibtex":"@inproceedings{Bossek_Grimme_2019, place={Cham}, series={Lecture Notes in Computer Science}, title={Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-criteria Shortest Path Problems}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-05348-2_17\">10.1007/978-3-030-05348-2_17</a>}, booktitle={Learning and Intelligent Optimization}, publisher={Springer International Publishing}, author={Bossek, Jakob and Grimme, Christian}, editor={Battiti, Roberto and Brunato, Mauro and Kotsireas, Ilias and Pardalos, Panos M.}, year={2019}, pages={184–198}, collection={Lecture Notes in Computer Science} }","ama":"Bossek J, Grimme C. Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-criteria Shortest Path Problems. In: Battiti R, Brunato M, Kotsireas I, Pardalos PM, eds. <i>Learning and Intelligent Optimization</i>. Lecture Notes in Computer Science. Springer International Publishing; 2019:184–198. doi:<a href=\"https://doi.org/10.1007/978-3-030-05348-2_17\">10.1007/978-3-030-05348-2_17</a>","mla":"Bossek, Jakob, and Christian Grimme. “Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-Criteria Shortest Path Problems.” <i>Learning and Intelligent Optimization</i>, edited by Roberto Battiti et al., Springer International Publishing, 2019, pp. 184–198, doi:<a href=\"https://doi.org/10.1007/978-3-030-05348-2_17\">10.1007/978-3-030-05348-2_17</a>."},"place":"Cham","publication_status":"published","date_updated":"2023-12-13T10:44:44Z","publication_identifier":{"isbn":["978-3-030-05348-2"]},"author":[{"id":"102979","last_name":"Bossek","first_name":"Jakob","orcid":"0000-0002-4121-4668","full_name":"Bossek, Jakob"},{"last_name":"Grimme","first_name":"Christian","full_name":"Grimme, Christian"}],"title":"Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-criteria Shortest Path Problems","year":"2019","doi":"10.1007/978-3-030-05348-2_17","language":[{"iso":"eng"}],"series_title":"Lecture Notes in Computer Science","extern":"1","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."}],"publication":"Learning and Intelligent Optimization","department":[{"_id":"819"}],"type":"conference","date_created":"2023-11-14T15:58:54Z"}]