[{"page":"280–292","language":[{"iso":"eng"}],"_id":"46341","user_id":"15504","year":"2019","title":"Customer Segmentation Based on Transactional Data Using Stream Clustering","status":"public","author":[{"last_name":"Carnein","first_name":"Matthias","full_name":"Carnein, Matthias"},{"full_name":"Trautmann, Heike","orcid":"0000-0002-9788-8282","first_name":"Heike","last_name":"Trautmann","id":"100740"}],"date_updated":"2023-10-16T13:30:10Z","date_created":"2023-08-04T07:47:20Z","place":"Macau, China","type":"conference","department":[{"_id":"34"},{"_id":"819"}],"publication":"Proceedings of the 23$^rd$ Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD ’19)","citation":{"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.","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.","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.","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."},"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"}]},{"doi":"10.1063/1.5090019","user_id":"15504","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"},{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"},{"full_name":"Emmerich, Michael T M","last_name":"Emmerich","first_name":"Michael T M"},{"last_name":"Preuss","first_name":"Mike","full_name":"Preuss, Mike"},{"full_name":"Deutz, André H","last_name":"Deutz","first_name":"André H"},{"orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike","full_name":"Trautmann, Heike","id":"100740"}],"type":"conference","department":[{"_id":"34"},{"_id":"819"}],"place":"Leiden, The Netherlands","date_created":"2023-08-04T07:48:15Z","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":{"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>.","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>.","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.","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>"}},{"editor":[{"full_name":"Bauer, Nadja","first_name":"Nadja","last_name":"Bauer"},{"full_name":"Ickstadt, Katja","first_name":"Katja","last_name":"Ickstadt"},{"full_name":"Lübke, Karsten","first_name":"Karsten","last_name":"Lübke"},{"full_name":"Szepannek, Gero","first_name":"Gero","last_name":"Szepannek"},{"first_name":"Heike","last_name":"Trautmann","full_name":"Trautmann, Heike"},{"full_name":"Vichi, Maurizio","first_name":"Maurizio","last_name":"Vichi"}],"doi":"10.1007/978-3-030-25147-5_7","user_id":"15504","_id":"46336","publisher":"Springer","language":[{"iso":"eng"}],"page":"93–123","date_updated":"2023-10-16T13:08:22Z","author":[{"last_name":"Kerschke","first_name":"Pascal","full_name":"Kerschke, Pascal"},{"full_name":"Trautmann, Heike","first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","id":"100740"}],"status":"public","title":"Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-package flacco","year":"2019","department":[{"_id":"34"},{"_id":"819"}],"type":"book_chapter","date_created":"2023-08-04T07:43:30Z","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."}],"citation":{"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.","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>.","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>","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.","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>","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} }","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>."},"publication":"Applications in Statistical Computing"},{"citation":{"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.","ama":"Trautmann H. <i>Applications in Statistical Computing — From Music Data Analysis to Industrial Quality Improvement</i>. Springer International Publishing; 2019.","short":"H. Trautmann, Applications in Statistical Computing — From Music Data Analysis to Industrial Quality Improvement, 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} }","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."},"date_created":"2023-08-04T07:43:09Z","department":[{"_id":"34"},{"_id":"819"}],"type":"book","author":[{"id":"100740","full_name":"Trautmann, Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","first_name":"Heike"}],"publication_identifier":{"isbn":["978-3-030-25147-5"]},"status":"public","title":"Applications in Statistical Computing — From Music Data Analysis to Industrial Quality Improvement","year":"2019","date_updated":"2023-10-16T13:07:21Z","_id":"46335","language":[{"iso":"eng"}],"series_title":"Studies in Classification, Data Analysis, and Knowledge Organization","publisher":"Springer International Publishing","user_id":"15504"},{"type":"journal_article","department":[{"_id":"34"},{"_id":"819"}],"date_created":"2023-08-04T07:51:18Z","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":{"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>","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} }","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>.","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>.","short":"P. Kerschke, H. Trautmann, Evolutionary Computation (ECJ) 27 (2019) 99–127.","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>","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>."},"user_id":"15504","doi":"10.1162/evco_a_00236","volume":27,"page":"99–127","_id":"46346","language":[{"iso":"eng"}],"date_updated":"2023-10-16T13:31:57Z","intvolume":"        27","status":"public","year":"2019","title":"Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning","author":[{"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"}]},{"date_updated":"2023-10-16T13:32:18Z","intvolume":"        27","title":"Search Dynamics on Multimodal Multi-Objective Problems","year":"2019","status":"public","author":[{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"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","first_name":"Christian","last_name":"Grimme"},{"full_name":"Deutz, André","last_name":"Deutz","first_name":"André"},{"first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","full_name":"Trautmann, Heike","id":"100740"},{"first_name":"Michael","last_name":"Emmerich","full_name":"Emmerich, Michael"}],"doi":"10.1162/evco_a_00234","user_id":"15504","volume":27,"page":"577–609","language":[{"iso":"eng"}],"_id":"46347","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"}],"issue":"4","publication":"Evolutionary Computation (ECJ)","citation":{"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.","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>.","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>","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} }","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>","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>."},"type":"journal_article","department":[{"_id":"34"},{"_id":"819"}],"date_created":"2023-08-04T07:52:06Z"},{"year":"2019","title":"Bi-Objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm","author":[{"first_name":"Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","full_name":"Bossek, Jakob","id":"102979"},{"last_name":"Grimme","first_name":"Christian","full_name":"Grimme, Christian"},{"full_name":"Meisel, Stephan","first_name":"Stephan","last_name":"Meisel"},{"full_name":"Rudolph, Günter","first_name":"Günter","last_name":"Rudolph"},{"full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike"}],"publication_identifier":{"isbn":["978-3-030-12598-1"]},"date_updated":"2023-12-13T10:43:07Z","publication_status":"published","language":[{"iso":"eng"}],"series_title":"Lecture Notes in Computer Science","doi":"10.1007/978-3-030-12598-1_41","publication":"Evolutionary Multi-Criterion Optimization (EMO)","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."}],"extern":"1","date_created":"2023-11-14T15:58:52Z","type":"conference","keyword":["Combinatorial optimization","Dynamic optimization","Metaheuristics","Multi-objective optimization","Vehicle routing"],"department":[{"_id":"819"}],"status":"public","page":"516–528","_id":"48841","publisher":"Springer International Publishing","user_id":"102979","editor":[{"last_name":"Deb","first_name":"Kalyanmoy","full_name":"Deb, Kalyanmoy"},{"last_name":"Goodman","first_name":"Erik","full_name":"Goodman, Erik"},{"full_name":"Coello Coello, Carlos A.","last_name":"Coello Coello","first_name":"Carlos A."},{"last_name":"Klamroth","first_name":"Kathrin","full_name":"Klamroth, Kathrin"},{"full_name":"Miettinen, Kaisa","last_name":"Miettinen","first_name":"Kaisa"},{"full_name":"Mostaghim, Sanaz","last_name":"Mostaghim","first_name":"Sanaz"},{"full_name":"Reed, Patrick","first_name":"Patrick","last_name":"Reed"}],"citation":{"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>.","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>.","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.","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>"},"place":"Cham"},{"_id":"48842","publisher":"Association for Computing Machinery","page":"58–71","user_id":"102979","status":"public","place":"New York, NY, USA","citation":{"mla":"Bossek, Jakob, et al. “Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators.” <i>Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, Association for Computing Machinery, 2019, pp. 58–71, doi:<a href=\"https://doi.org/10.1145/3299904.3340307\">10.1145/3299904.3340307</a>.","bibtex":"@inproceedings{Bossek_Kerschke_Neumann_Wagner_Neumann_Trautmann_2019, place={New York, NY, USA}, series={FOGA ’19}, title={Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators}, DOI={<a href=\"https://doi.org/10.1145/3299904.3340307\">10.1145/3299904.3340307</a>}, booktitle={Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Kerschke, Pascal and Neumann, Aneta and Wagner, Markus and Neumann, Frank and Trautmann, Heike}, year={2019}, pages={58–71}, collection={FOGA ’19} }","ama":"Bossek J, Kerschke P, Neumann A, Wagner M, Neumann F, Trautmann H. Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators. In: <i>Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>. FOGA ’19. Association for Computing Machinery; 2019:58–71. doi:<a href=\"https://doi.org/10.1145/3299904.3340307\">10.1145/3299904.3340307</a>","ieee":"J. Bossek, P. Kerschke, A. Neumann, M. Wagner, F. Neumann, and H. Trautmann, “Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators,” in <i>Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, 2019, pp. 58–71, doi: <a href=\"https://doi.org/10.1145/3299904.3340307\">10.1145/3299904.3340307</a>.","apa":"Bossek, J., Kerschke, P., Neumann, A., Wagner, M., Neumann, F., &#38; Trautmann, H. (2019). Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators. <i>Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, 58–71. <a href=\"https://doi.org/10.1145/3299904.3340307\">https://doi.org/10.1145/3299904.3340307</a>","short":"J. Bossek, P. Kerschke, A. Neumann, M. Wagner, F. Neumann, H. Trautmann, in: Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, Association for Computing Machinery, New York, NY, USA, 2019, pp. 58–71.","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>."},"series_title":"FOGA ’19","language":[{"iso":"eng"}],"doi":"10.1145/3299904.3340307","publication_identifier":{"isbn":["978-1-4503-6254-2"]},"author":[{"id":"102979","full_name":"Bossek, Jakob","first_name":"Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668"},{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"},{"first_name":"Aneta","last_name":"Neumann","full_name":"Neumann, Aneta"},{"full_name":"Wagner, Markus","last_name":"Wagner","first_name":"Markus"},{"last_name":"Neumann","first_name":"Frank","full_name":"Neumann, Frank"},{"full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike"}],"year":"2019","title":"Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators","date_updated":"2023-12-13T10:42:57Z","publication_status":"published","date_created":"2023-11-14T15:58:52Z","department":[{"_id":"819"}],"keyword":["benchmarking","instance features","optimization","problem generation","traveling salesperson problem"],"type":"conference","publication":"Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms","abstract":[{"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.","lang":"eng"}],"extern":"1"},{"citation":{"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>.","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} }","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>.","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.","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>."},"place":"New York, NY, USA","status":"public","user_id":"102979","page":"1443–1451","_id":"48843","publisher":"Association for Computing Machinery","extern":"1","abstract":[{"lang":"eng","text":"We contribute to the theoretical understanding of randomized search heuristics for dynamic problems. We consider the classical graph coloring problem and investigate the dynamic setting where edges are added to the current graph. We then analyze the expected time for randomized search heuristics to recompute high quality solutions. This includes the (1+1) EA and RLS in a setting where the number of colors is bounded and we are minimizing the number of conflicts as well as iterated local search algorithms that use an unbounded color palette and aim to use the smallest colors and - as a consequence - the smallest number of colors. We identify classes of bipartite graphs where reoptimization is as hard as or even harder than optimization from scratch, i. e. starting with a random initialization. Even adding a single edge can lead to hard symmetry problems. However, graph classes that are hard for one algorithm turn out to be easy for others. In most cases our bounds show that reoptimization is faster than optimizing from scratch. Furthermore, we show how to speed up computations by using problem specific operators concentrating on parts of the graph where changes have occurred."}],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference","keyword":["dynamic optimization","evolutionary algorithms","running time analysis","theory"],"type":"conference","department":[{"_id":"819"}],"date_created":"2023-11-14T15:58:52Z","publication_status":"published","date_updated":"2023-12-13T10:42:37Z","year":"2019","title":"Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring","author":[{"first_name":"Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","full_name":"Bossek, Jakob","id":"102979"},{"full_name":"Neumann, Frank","last_name":"Neumann","first_name":"Frank"},{"full_name":"Peng, Pan","last_name":"Peng","first_name":"Pan"},{"first_name":"Dirk","last_name":"Sudholt","full_name":"Sudholt, Dirk"}],"publication_identifier":{"isbn":["978-1-4503-6111-8"]},"doi":"10.1145/3321707.3321792","series_title":"GECCO ’19","language":[{"iso":"eng"}]},{"status":"public","user_id":"102979","_id":"48840","publisher":"Association for Computing Machinery","page":"516–523","citation":{"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>.","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} }","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>","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>.","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>","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>."},"place":"New York, NY, USA","date_updated":"2023-12-13T10:42:24Z","publication_status":"published","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"},{"full_name":"Neumann, Frank","last_name":"Neumann","first_name":"Frank"}],"publication_identifier":{"isbn":["978-1-4503-6111-8"]},"title":"On the Benefits of Biased Edge-Exchange Mutation for the Multi-Criteria Spanning Tree Problem","year":"2019","doi":"10.1145/3321707.3321818","language":[{"iso":"eng"}],"series_title":"GECCO ’19","abstract":[{"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.","lang":"eng"}],"extern":"1","publication":"Proceedings of the Genetic and Evolutionary Computation Conference","department":[{"_id":"819"}],"type":"conference","keyword":["biased mutation","combinatorial optimization","minimum spanning tree","multi-objective optimization"],"date_created":"2023-11-14T15:58:52Z"},{"date_created":"2023-11-14T15:58:54Z","department":[{"_id":"819"}],"type":"conference","publication":"Learning and Intelligent Optimization","abstract":[{"text":"The $$\\textbackslash mathcal NP$$-hard multi-criteria shortest path problem (mcSPP) is of utmost practical relevance, e.~g., in navigation system design and logistics. We address the problem of approximating the Pareto-front of the mcSPP with sum objectives. We do so by proposing a new mutation operator for multi-objective evolutionary algorithms that solves single-objective versions of the shortest path problem on subgraphs. A rigorous empirical benchmark on a diverse set of problem instances shows the effectiveness of the approach in comparison to a well-known mutation operator in terms of convergence speed and approximation quality. In addition, we glance at the neighbourhood structure and similarity of obtained Pareto-optimal solutions and derive promising directions for future work.","lang":"eng"}],"extern":"1","language":[{"iso":"eng"}],"series_title":"Lecture Notes in Computer Science","doi":"10.1007/978-3-030-05348-2_17","publication_identifier":{"isbn":["978-3-030-05348-2"]},"author":[{"id":"102979","last_name":"Bossek","orcid":"0000-0002-4121-4668","first_name":"Jakob","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","date_updated":"2023-12-13T10:44:44Z","publication_status":"published","place":"Cham","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. <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>","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} }","mla":"Bossek, Jakob, and Christian Grimme. “Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-Criteria Shortest Path Problems.” <i>Learning and Intelligent Optimization</i>, edited by Roberto Battiti et al., Springer International Publishing, 2019, pp. 184–198, doi:<a href=\"https://doi.org/10.1007/978-3-030-05348-2_17\">10.1007/978-3-030-05348-2_17</a>.","short":"J. Bossek, C. Grimme, in: R. Battiti, M. Brunato, I. Kotsireas, P.M. Pardalos (Eds.), Learning and Intelligent Optimization, Springer International Publishing, Cham, 2019, pp. 184–198.","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>.","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>","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>."},"_id":"48858","publisher":"Springer International Publishing","page":"184–198","editor":[{"first_name":"Roberto","last_name":"Battiti","full_name":"Battiti, Roberto"},{"last_name":"Brunato","first_name":"Mauro","full_name":"Brunato, Mauro"},{"first_name":"Ilias","last_name":"Kotsireas","full_name":"Kotsireas, Ilias"},{"full_name":"Pardalos, Panos M.","last_name":"Pardalos","first_name":"Panos M."}],"user_id":"102979","status":"public"},{"user_id":"102979","publisher":"Association for Computing Machinery","_id":"48870","page":"102–115","status":"public","place":"New York, NY, USA","citation":{"ama":"Bossek J, Sudholt D. Time Complexity Analysis of RLS and (1 + 1) EA for the Edge Coloring Problem. In: <i>Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>. FOGA ’19. Association for Computing Machinery; 2019:102–115. doi:<a href=\"https://doi.org/10.1145/3299904.3340311\">10.1145/3299904.3340311</a>","bibtex":"@inproceedings{Bossek_Sudholt_2019, place={New York, NY, USA}, series={FOGA ’19}, title={Time Complexity Analysis of RLS and (1 + 1) EA for the Edge Coloring Problem}, DOI={<a href=\"https://doi.org/10.1145/3299904.3340311\">10.1145/3299904.3340311</a>}, booktitle={Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Sudholt, Dirk}, year={2019}, pages={102–115}, collection={FOGA ’19} }","mla":"Bossek, Jakob, and Dirk Sudholt. “Time Complexity Analysis of RLS and (1 + 1) EA for the Edge Coloring Problem.” <i>Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, Association for Computing Machinery, 2019, pp. 102–115, doi:<a href=\"https://doi.org/10.1145/3299904.3340311\">10.1145/3299904.3340311</a>.","short":"J. Bossek, D. Sudholt, in: Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, Association for Computing Machinery, New York, NY, USA, 2019, pp. 102–115.","chicago":"Bossek, Jakob, and Dirk Sudholt. “Time Complexity Analysis of RLS and (1 + 1) EA for the Edge Coloring Problem.” In <i>Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, 102–115. FOGA ’19. New York, NY, USA: Association for Computing Machinery, 2019. <a href=\"https://doi.org/10.1145/3299904.3340311\">https://doi.org/10.1145/3299904.3340311</a>.","apa":"Bossek, J., &#38; Sudholt, D. (2019). Time Complexity Analysis of RLS and (1 + 1) EA for the Edge Coloring Problem. <i>Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, 102–115. <a href=\"https://doi.org/10.1145/3299904.3340311\">https://doi.org/10.1145/3299904.3340311</a>","ieee":"J. Bossek and D. Sudholt, “Time Complexity Analysis of RLS and (1 + 1) EA for the Edge Coloring Problem,” in <i>Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, 2019, pp. 102–115, doi: <a href=\"https://doi.org/10.1145/3299904.3340311\">10.1145/3299904.3340311</a>."},"doi":"10.1145/3299904.3340311","language":[{"iso":"eng"}],"series_title":"FOGA ’19","publication_status":"published","date_updated":"2023-12-13T10:46:12Z","author":[{"id":"102979","first_name":"Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","full_name":"Bossek, Jakob"},{"last_name":"Sudholt","first_name":"Dirk","full_name":"Sudholt, Dirk"}],"publication_identifier":{"isbn":["978-1-4503-6254-2"]},"year":"2019","title":"Time Complexity Analysis of RLS and (1 + 1) EA for the Edge Coloring Problem","department":[{"_id":"819"}],"keyword":["edge coloring problem","runtime analysis"],"type":"conference","date_created":"2023-11-14T15:58:56Z","extern":"1","abstract":[{"text":"The edge coloring problem asks for an assignment of colors to edges of a graph such that no two incident edges share the same color and the number of colors is minimized. It is known that all graphs with maximum degree {$\\Delta$} can be colored with {$\\Delta$} or {$\\Delta$} + 1 colors, but it is NP-hard to determine whether {$\\Delta$} colors are sufficient. We present the first runtime analysis of evolutionary algorithms (EAs) for the edge coloring problem. Simple EAs such as RLS and (1+1) EA efficiently find (2{$\\Delta$} - 1)-colorings on arbitrary graphs and optimal colorings for even and odd cycles, paths, star graphs and arbitrary trees. A partial analysis for toroids also suggests efficient runtimes in bipartite graphs with many cycles. Experiments support these findings and investigate additional graph classes such as hypercubes, complete graphs and complete bipartite graphs. Theoretical and experimental results suggest that simple EAs find optimal colorings for all these graph classes in expected time O({$\\Delta\\mathscrl$}2m log m), where m is the number of edges and {$\\mathscrl$} is the length of the longest simple path in the graph.","lang":"eng"}],"publication":"Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms"},{"doi":"10.1007/978-3-030-05348-2_19","series_title":"Lecture Notes in Computer Science","language":[{"iso":"eng"}],"date_updated":"2023-12-13T10:47:32Z","author":[{"id":"102979","full_name":"Bossek, Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","first_name":"Jakob"},{"full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike"}],"publication_identifier":{"isbn":["978-3-030-05348-2"]},"title":"Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time","year":"2019","department":[{"_id":"819"}],"type":"conference","keyword":["Algorithm selection","Performance measurement"],"date_created":"2023-11-14T15:58:57Z","extern":"1","abstract":[{"text":"A multiobjective perspective onto common performance measures such as the PAR10 score or the expected runtime of single-objective stochastic solvers is presented by directly investigating the tradeoff between the fraction of failed runs and the average runtime. Multi-objective indicators operating in the bi-objective space allow for an overall performance comparison on a set of instances paving the way for instance-based automated algorithm selection techniques.","lang":"eng"}],"publication":"Learning and Intelligent Optimization","editor":[{"full_name":"Battiti, Roberto","last_name":"Battiti","first_name":"Roberto"},{"full_name":"Brunato, Mauro","first_name":"Mauro","last_name":"Brunato"},{"full_name":"Kotsireas, Ilias","last_name":"Kotsireas","first_name":"Ilias"},{"first_name":"Panos M.","last_name":"Pardalos","full_name":"Pardalos, Panos M."}],"user_id":"102979","publisher":"Springer International Publishing","_id":"48875","page":"215–219","status":"public","place":"Cham","citation":{"apa":"Bossek, J., &#38; Trautmann, H. (2019). Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time. In R. Battiti, M. Brunato, I. Kotsireas, &#38; P. M. Pardalos (Eds.), <i>Learning and Intelligent Optimization</i> (pp. 215–219). Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-030-05348-2_19\">https://doi.org/10.1007/978-3-030-05348-2_19</a>","mla":"Bossek, Jakob, and Heike Trautmann. “Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time.” <i>Learning and Intelligent Optimization</i>, edited by Roberto Battiti et al., Springer International Publishing, 2019, pp. 215–219, doi:<a href=\"https://doi.org/10.1007/978-3-030-05348-2_19\">10.1007/978-3-030-05348-2_19</a>.","ieee":"J. Bossek and H. Trautmann, “Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time,” in <i>Learning and Intelligent Optimization</i>, 2019, pp. 215–219, doi: <a href=\"https://doi.org/10.1007/978-3-030-05348-2_19\">10.1007/978-3-030-05348-2_19</a>.","chicago":"Bossek, Jakob, and Heike Trautmann. “Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time.” In <i>Learning and Intelligent Optimization</i>, edited by Roberto Battiti, Mauro Brunato, Ilias Kotsireas, and Panos M. Pardalos, 215–219. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2019. <a href=\"https://doi.org/10.1007/978-3-030-05348-2_19\">https://doi.org/10.1007/978-3-030-05348-2_19</a>.","ama":"Bossek J, Trautmann H. Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time. In: Battiti R, Brunato M, Kotsireas I, Pardalos PM, eds. <i>Learning and Intelligent Optimization</i>. Lecture Notes in Computer Science. Springer International Publishing; 2019:215–219. doi:<a href=\"https://doi.org/10.1007/978-3-030-05348-2_19\">10.1007/978-3-030-05348-2_19</a>","short":"J. Bossek, H. Trautmann, in: R. Battiti, M. Brunato, I. Kotsireas, P.M. Pardalos (Eds.), Learning and Intelligent Optimization, Springer International Publishing, Cham, 2019, pp. 215–219.","bibtex":"@inproceedings{Bossek_Trautmann_2019, place={Cham}, series={Lecture Notes in Computer Science}, title={Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-05348-2_19\">10.1007/978-3-030-05348-2_19</a>}, booktitle={Learning and Intelligent Optimization}, publisher={Springer International Publishing}, author={Bossek, Jakob and Trautmann, Heike}, editor={Battiti, Roberto and Brunato, Mauro and Kotsireas, Ilias and Pardalos, Panos M.}, year={2019}, pages={215–219}, collection={Lecture Notes in Computer Science} }"}},{"language":[{"iso":"eng"}],"doi":"10.1007/s00180-017-0742-2","year":"2019","title":"OpenML: An R Package to Connect to the Machine Learning Platform OpenML","author":[{"full_name":"Casalicchio, Giuseppe","first_name":"Giuseppe","last_name":"Casalicchio"},{"orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob","full_name":"Bossek, Jakob","id":"102979"},{"full_name":"Lang, Michel","first_name":"Michel","last_name":"Lang"},{"full_name":"Kirchhoff, Dominik","last_name":"Kirchhoff","first_name":"Dominik"},{"last_name":"Kerschke","first_name":"Pascal","full_name":"Kerschke, Pascal"},{"full_name":"Hofner, Benjamin","last_name":"Hofner","first_name":"Benjamin"},{"full_name":"Seibold, Heidi","first_name":"Heidi","last_name":"Seibold"},{"full_name":"Vanschoren, Joaquin","first_name":"Joaquin","last_name":"Vanschoren"},{"first_name":"Bernd","last_name":"Bischl","full_name":"Bischl, Bernd"}],"publication_identifier":{"issn":["0943-4062"]},"date_updated":"2023-12-13T10:51:17Z","intvolume":"        34","date_created":"2023-11-14T15:58:57Z","type":"journal_article","keyword":["Databases","Machine learning","R","Reproducible research"],"department":[{"_id":"819"}],"publication":"Computational Statistics","issue":"3","abstract":[{"text":"OpenML is an online machine learning platform where researchers can easily share data, machine learning tasks and experiments as well as organize them online to work and collaborate more efficiently. In this paper, we present an R package to interface with the OpenML platform and illustrate its usage in combination with the machine learning R package mlr (Bischl et al. J Mach Learn Res 17(170):1—5, 2016). We show how the OpenML package allows R users to easily search, download and upload data sets and machine learning tasks. Furthermore, we also show how to upload results of experiments, share them with others and download results from other users. Beyond ensuring reproducibility of results, the OpenML platform automates much of the drudge work, speeds up research, facilitates collaboration and increases the users’ visibility online.","lang":"eng"}],"page":"977–991","_id":"48877","user_id":"102979","volume":34,"status":"public","citation":{"short":"G. Casalicchio, J. Bossek, M. Lang, D. Kirchhoff, P. Kerschke, B. Hofner, H. Seibold, J. Vanschoren, B. Bischl, Computational Statistics 34 (2019) 977–991.","chicago":"Casalicchio, Giuseppe, Jakob Bossek, Michel Lang, Dominik Kirchhoff, Pascal Kerschke, Benjamin Hofner, Heidi Seibold, Joaquin Vanschoren, and Bernd Bischl. “OpenML: An R Package to Connect to the Machine Learning Platform OpenML.” <i>Computational Statistics</i> 34, no. 3 (2019): 977–991. <a href=\"https://doi.org/10.1007/s00180-017-0742-2\">https://doi.org/10.1007/s00180-017-0742-2</a>.","apa":"Casalicchio, G., Bossek, J., Lang, M., Kirchhoff, D., Kerschke, P., Hofner, B., Seibold, H., Vanschoren, J., &#38; Bischl, B. (2019). OpenML: An R Package to Connect to the Machine Learning Platform OpenML. <i>Computational Statistics</i>, <i>34</i>(3), 977–991. <a href=\"https://doi.org/10.1007/s00180-017-0742-2\">https://doi.org/10.1007/s00180-017-0742-2</a>","ieee":"G. Casalicchio <i>et al.</i>, “OpenML: An R Package to Connect to the Machine Learning Platform OpenML,” <i>Computational Statistics</i>, vol. 34, no. 3, pp. 977–991, 2019, doi: <a href=\"https://doi.org/10.1007/s00180-017-0742-2\">10.1007/s00180-017-0742-2</a>.","ama":"Casalicchio G, Bossek J, Lang M, et al. OpenML: An R Package to Connect to the Machine Learning Platform OpenML. <i>Computational Statistics</i>. 2019;34(3):977–991. doi:<a href=\"https://doi.org/10.1007/s00180-017-0742-2\">10.1007/s00180-017-0742-2</a>","bibtex":"@article{Casalicchio_Bossek_Lang_Kirchhoff_Kerschke_Hofner_Seibold_Vanschoren_Bischl_2019, title={OpenML: An R Package to Connect to the Machine Learning Platform OpenML}, volume={34}, DOI={<a href=\"https://doi.org/10.1007/s00180-017-0742-2\">10.1007/s00180-017-0742-2</a>}, number={3}, journal={Computational Statistics}, author={Casalicchio, Giuseppe and Bossek, Jakob and Lang, Michel and Kirchhoff, Dominik and Kerschke, Pascal and Hofner, Benjamin and Seibold, Heidi and Vanschoren, Joaquin and Bischl, Bernd}, year={2019}, pages={977–991} }","mla":"Casalicchio, Giuseppe, et al. “OpenML: An R Package to Connect to the Machine Learning Platform OpenML.” <i>Computational Statistics</i>, vol. 34, no. 3, 2019, pp. 977–991, doi:<a href=\"https://doi.org/10.1007/s00180-017-0742-2\">10.1007/s00180-017-0742-2</a>."}},{"abstract":[{"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.","lang":"eng"}],"citation":{"mla":"Bossek, Jakob, et al. “Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators.” <i>Proceedings of the 15$^th$ ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA XV)</i>, edited by Tobias Friedrich et al., 2019, pp. 58–71, doi:<a href=\"https://doi.org/10.1145/3299904.3340307\">10.1145/3299904.3340307</a>.","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: Friedrich T, Doerr C, Arnold D, eds. <i>Proceedings of the 15$^th$ ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA XV)</i>. ; 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={Potsdam, Germany}, 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 15$^th$ ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA XV)}, author={Bossek, Jakob and Kerschke, Pascal and Neumann, Aneta and Wagner, Markus and Neumann, Frank and Trautmann, Heike}, editor={Friedrich, Tobias and Doerr, Carola and Arnold, Dirk}, year={2019}, pages={58–71} }","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. In T. Friedrich, C. Doerr, &#38; D. Arnold (Eds.), <i>Proceedings of the 15$^th$ ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA XV)</i> (pp. 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 15$^th$ ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA XV)</i>, 2019, pp. 58–71, doi: <a href=\"https://doi.org/10.1145/3299904.3340307\">10.1145/3299904.3340307</a>.","chicago":"Bossek, Jakob, Pascal Kerschke, Aneta Neumann, Markus Wagner, Frank Neumann, and Heike Trautmann. “Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators.” In <i>Proceedings of the 15$^th$ ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA XV)</i>, edited by Tobias Friedrich, Carola Doerr, and Dirk Arnold, 58–71. Potsdam, Germany, 2019. <a href=\"https://doi.org/10.1145/3299904.3340307\">https://doi.org/10.1145/3299904.3340307</a>.","short":"J. Bossek, P. Kerschke, A. Neumann, M. Wagner, F. Neumann, H. Trautmann, in: T. Friedrich, C. Doerr, D. Arnold (Eds.), Proceedings of the 15$^th$ ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA XV), Potsdam, Germany, 2019, pp. 58–71."},"publication":"Proceedings of the 15$^th$ ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA XV)","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","place":"Potsdam, Germany","date_created":"2023-08-04T07:45:39Z","date_updated":"2024-06-10T11:59:26Z","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"},{"full_name":"Neumann, Aneta","first_name":"Aneta","last_name":"Neumann"},{"full_name":"Wagner, Markus","last_name":"Wagner","first_name":"Markus"},{"first_name":"Frank","last_name":"Neumann","full_name":"Neumann, Frank"},{"last_name":"Trautmann","first_name":"Heike","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike","id":"100740"}],"year":"2019","title":"Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators","status":"public","editor":[{"last_name":"Friedrich","first_name":"Tobias","full_name":"Friedrich, Tobias"},{"last_name":"Doerr","first_name":"Carola","full_name":"Doerr, Carola"},{"last_name":"Arnold","first_name":"Dirk","full_name":"Arnold, Dirk"}],"doi":"10.1145/3299904.3340307","user_id":"15504","_id":"46339","language":[{"iso":"eng"}],"page":"58–71"},{"place":"East Lansing, Michigan, USA","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 CCA, et al., eds. <i>Evolutionary Multi-Criterion Optimization (EMO)</i>. Vol 11411. 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>","short":"J. Bossek, C. Grimme, S. Meisel, G. Rudolph, H. Trautmann, in: K. Deb, E. Goodman, C.C.A. Coello, K. Klamroth, K. Miettinen, S. Mostaghim, P. Reed (Eds.), Evolutionary Multi-Criterion Optimization (EMO), Springer International Publishing, East Lansing, Michigan, USA, 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, Coello Carlos A. Coello, Kathrin Klamroth, Kaisa Miettinen, Sanaz Mostaghim, and Patrick Reed, 11411:516–528. Lecture Notes in Computer Science. East Lansing, Michigan, USA: 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>.","bibtex":"@inproceedings{Bossek_Grimme_Meisel_Rudolph_Trautmann_2019, place={East Lansing, Michigan, USA}, series={Lecture Notes in Computer Science}, title={Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm}, volume={11411}, 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} }","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., vol. 11411, 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>.","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. C. A. Coello, K. Klamroth, K. Miettinen, S. Mostaghim, &#38; P. Reed (Eds.), <i>Evolutionary Multi-Criterion Optimization (EMO)</i> (Vol. 11411, 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>","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, vol. 11411, 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>."},"user_id":"15504","volume":11411,"editor":[{"full_name":"Deb, Kalyanmoy","last_name":"Deb","first_name":"Kalyanmoy"},{"last_name":"Goodman","first_name":"Erik","full_name":"Goodman, Erik"},{"full_name":"Coello, Coello Carlos A.","first_name":"Coello Carlos A.","last_name":"Coello"},{"last_name":"Klamroth","first_name":"Kathrin","full_name":"Klamroth, Kathrin"},{"full_name":"Miettinen, Kaisa","last_name":"Miettinen","first_name":"Kaisa"},{"last_name":"Mostaghim","first_name":"Sanaz","full_name":"Mostaghim, Sanaz"},{"full_name":"Reed, Patrick","first_name":"Patrick","last_name":"Reed"}],"page":"516–528","_id":"46338","publisher":"Springer International Publishing","status":"public","type":"conference","department":[{"_id":"34"},{"_id":"819"}],"date_created":"2023-08-04T07:44:59Z","abstract":[{"lang":"eng","text":"We tackle a bi-objective dynamic orienteering problem where customer requests arise as time passes by. The goal is to minimize the tour length traveled by a single delivery vehicle while simultaneously keeping the number of dismissed dynamic customers to a minimum. We propose a dynamic Evolutionary Multi-Objective Algorithm which is grounded on insights gained from a previous series of work on an a-posteriori version of the problem, where all request times are known in advance. In our experiments, we simulate different decision maker strategies and evaluate the development of the Pareto-front approximations on exemplary problem instances. It turns out, that despite severely reduced computational budget and no oracle-knowledge of request times the dynamic EMOA is capable of producing approximations which partially dominate the results of the a-posteriori EMOA and dynamic integer linear programming strategies."}],"publication":"Evolutionary Multi-Criterion Optimization (EMO)","doi":"10.1007/978-3-030-12598-1_41","series_title":"Lecture Notes in Computer Science","language":[{"iso":"eng"}],"date_updated":"2024-06-10T12:00:05Z","intvolume":"     11411","title":"Bi-Objective Orienteering: Towards a Dynamic Multi-Objective Evolutionary Algorithm","year":"2019","publication_identifier":{"isbn":["978-3-030-12597-4"]},"author":[{"last_name":"Bossek","first_name":"Jakob","orcid":"0000-0002-4121-4668","full_name":"Bossek, Jakob","id":"102979"},{"first_name":"Christian","last_name":"Grimme","full_name":"Grimme, Christian"},{"full_name":"Meisel, Stephan","first_name":"Stephan","last_name":"Meisel"},{"first_name":"Günter","last_name":"Rudolph","full_name":"Rudolph, Günter"},{"full_name":"Trautmann, Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","first_name":"Heike","id":"100740"}]},{"publication":"Learning and Intelligent Optimization","abstract":[{"lang":"eng","text":"A multiobjective perspective onto common performance measures such as the PAR10 score or the expected runtime of single-objective stochastic solvers is presented by directly investigating the tradeoff between the fraction of failed runs and the average runtime. Multi-objective indicators operating in the bi-objective space allow for an overall performance comparison on a set of instances paving the way for instance-based automated algorithm selection techniques."}],"date_created":"2023-08-04T07:44:10Z","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","publication_identifier":{"isbn":["978-3-030-05347-5"]},"author":[{"full_name":"Bossek, Jakob","orcid":"0000-0002-4121-4668","first_name":"Jakob","last_name":"Bossek","id":"102979"},{"full_name":"Trautmann, Heike","first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","id":"100740"}],"title":"Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time","year":"2019","intvolume":"     11353","date_updated":"2024-06-10T12:00:23Z","series_title":"Lecture Notes in Computer Science","language":[{"iso":"eng"}],"citation":{"bibtex":"@inproceedings{Bossek_Trautmann_2019, place={Cham}, series={Lecture Notes in Computer Science}, title={Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time}, volume={11353}, booktitle={Learning and Intelligent Optimization}, publisher={Springer}, author={Bossek, Jakob and Trautmann, Heike}, editor={Battiti, R and Brunato, M and Kotsireas, I and Pardalos, P}, year={2019}, pages={215–219}, collection={Lecture Notes in Computer Science} }","chicago":"Bossek, Jakob, and Heike Trautmann. “Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time.” In <i>Learning and Intelligent Optimization</i>, edited by R Battiti, M Brunato, I Kotsireas, and P Pardalos, 11353:215–219. Lecture Notes in Computer Science. Cham: Springer, 2019.","short":"J. Bossek, H. Trautmann, in: R. Battiti, M. Brunato, I. Kotsireas, P. Pardalos (Eds.), Learning and Intelligent Optimization, Springer, Cham, 2019, pp. 215–219.","ama":"Bossek J, Trautmann H. Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time. In: Battiti R, Brunato M, Kotsireas I, Pardalos P, eds. <i>Learning and Intelligent Optimization</i>. Vol 11353. Lecture Notes in Computer Science. Springer; 2019:215–219.","ieee":"J. Bossek and H. Trautmann, “Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time,” in <i>Learning and Intelligent Optimization</i>, 2019, vol. 11353, pp. 215–219.","mla":"Bossek, Jakob, and Heike Trautmann. “Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time.” <i>Learning and Intelligent Optimization</i>, edited by R Battiti et al., vol. 11353, Springer, 2019, pp. 215–219.","apa":"Bossek, J., &#38; Trautmann, H. (2019). Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time. In R. Battiti, M. Brunato, I. Kotsireas, &#38; P. Pardalos (Eds.), <i>Learning and Intelligent Optimization</i> (Vol. 11353, pp. 215–219). Springer."},"place":"Cham","status":"public","_id":"46337","publisher":"Springer","page":"215–219","editor":[{"last_name":"Battiti","first_name":"R","full_name":"Battiti, R"},{"full_name":"Brunato, M","first_name":"M","last_name":"Brunato"},{"first_name":"I","last_name":"Kotsireas","full_name":"Kotsireas, I"},{"last_name":"Pardalos","first_name":"P","full_name":"Pardalos, P"}],"volume":11353,"user_id":"15504"},{"place":"Dublin, Ireland","date_created":"2023-08-04T07:54:43Z","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","citation":{"apa":"van Engelen, J. E., van Lier, J. J., Takes, F. W., &#38; Trautmann, H. (2018). Accurate WiFi based indoor positioning with continuous location sampling. <i>Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (ECML/PKDD)</i>, 524–540.","ieee":"J. E. van Engelen, J. J. van Lier, F. W. Takes, and H. Trautmann, “Accurate WiFi based indoor positioning with continuous location sampling,” in <i>Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (ECML/PKDD)</i>, 2018, pp. 524–540.","chicago":"Engelen, J.E. van, J.J. van Lier, F.W. Takes, and Heike Trautmann. “Accurate WiFi Based Indoor Positioning with Continuous Location Sampling.” In <i>Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (ECML/PKDD)</i>, 524–540. Dublin, Ireland: Springer, 2018.","short":"J.E. van Engelen, J.J. van Lier, F.W. Takes, H. Trautmann, in: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (ECML/PKDD), Springer, Dublin, Ireland, 2018, pp. 524–540.","mla":"van Engelen, J. E., et al. “Accurate WiFi Based Indoor Positioning with Continuous Location Sampling.” <i>Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (ECML/PKDD)</i>, Springer, 2018, pp. 524–540.","ama":"van Engelen JE, van Lier JJ, Takes FW, Trautmann H. Accurate WiFi based indoor positioning with continuous location sampling. In: <i>Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (ECML/PKDD)</i>. Springer; 2018:524–540.","bibtex":"@inproceedings{van Engelen_van Lier_Takes_Trautmann_2018, place={Dublin, Ireland}, title={Accurate WiFi based indoor positioning with continuous location sampling}, booktitle={Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (ECML/PKDD)}, publisher={Springer}, author={van Engelen, J.E. and van Lier, J.J. and Takes, F.W. and Trautmann, Heike}, year={2018}, pages={524–540} }"},"publication":"Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (ECML/PKDD)","abstract":[{"lang":"eng","text":"The ubiquity of WiFi access points and the sharp increase in WiFi-enabled devices carried by humans have paved the way for WiFi-based indoor positioning and location analysis. Locating people in indoor environments has numerous applications in robotics, crowd control, indoor facility optimization, and automated environment mapping. However, existing WiFi-based positioning systems suffer from two major problems: (1) their accuracy and precision is limited due to inherent noise induced by indoor obstacles, and (2) they only occasionally provide location estimates, namely when a WiFi-equipped device emits a signal. To mitigate these two issues, we propose a novel Gaussian process (GP) model for WiFi signal strength measurements. It allows for simultaneous smoothing (increasing accuracy and precision of estimators) and interpolation (enabling continuous sampling of location estimates). Furthermore, simple and efficient smoothing methods for location estimates are introduced to improve localization performance in real-time settings. Experiments are conducted on two data sets from a large real-world commercial indoor retail environment. Results demonstrate that our approach provides significant improvements in terms of precision and accuracy with respect to unfiltered data. Ultimately, the GP model realizes continuous location sampling with consistently high quality location estimates."}],"_id":"46350","publisher":"Springer","language":[{"iso":"eng"}],"page":"524–540","user_id":"15504","author":[{"full_name":"van Engelen, J.E.","last_name":"van Engelen","first_name":"J.E."},{"last_name":"van Lier","first_name":"J.J.","full_name":"van Lier, J.J."},{"full_name":"Takes, F.W.","first_name":"F.W.","last_name":"Takes"},{"full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike","orcid":"0000-0002-9788-8282","id":"100740"}],"title":"Accurate WiFi based indoor positioning with continuous location sampling","year":"2018","status":"public","date_updated":"2023-10-16T13:33:18Z"},{"type":"journal_article","department":[{"_id":"34"},{"_id":"819"}],"date_created":"2023-08-04T07:55:33Z","abstract":[{"text":"Clustering is an important field in data mining that aims to reveal hidden patterns in data sets. It is widely popular in marketing or medical applications and used to identify groups of similar objects. Clustering possibly unbounded and evolving data streams is of particular interest due to the widespread deployment of large and fast data sources such as sensors. The vast majority of stream clustering algorithms employ a two-phase approach where the stream is first summarized in an online phase. Upon request, an offline phase reclusters the aggregations into the final clusters. In this setup, the online component will idle and wait for the next observation in times where the stream is slow. This paper proposes a new stream clustering algorithm called evoStream which performs evolutionary optimization in the idle times of the online phase to incrementally build and refine the final clusters. Since the online phase would idle otherwise, our approach does not reduce the processing speed while effectively removing the computational overhead of the offline phase. In extensive experiments on real data streams we show that the proposed algorithm allows to output clusters of high quality at any time within the stream without the need for additional computational resources.","lang":"eng"}],"publication":"Big Data Research","citation":{"mla":"Carnein, Matthias, and Heike Trautmann. “EvoStream — Evolutionary Stream Clustering Utilizing Idle Times.” <i>Big Data Research</i>, vol. 14, 2018, pp. 101–111, doi:<a href=\"https://doi.org/10.1016/j.bdr.2018.05.005\">10.1016/j.bdr.2018.05.005</a>.","bibtex":"@article{Carnein_Trautmann_2018, title={evoStream — Evolutionary Stream Clustering Utilizing Idle Times}, volume={14}, DOI={<a href=\"https://doi.org/10.1016/j.bdr.2018.05.005\">10.1016/j.bdr.2018.05.005</a>}, journal={Big Data Research}, author={Carnein, Matthias and Trautmann, Heike}, year={2018}, pages={101–111} }","ama":"Carnein M, Trautmann H. evoStream — Evolutionary Stream Clustering Utilizing Idle Times. <i>Big Data Research</i>. 2018;14:101–111. doi:<a href=\"https://doi.org/10.1016/j.bdr.2018.05.005\">10.1016/j.bdr.2018.05.005</a>","ieee":"M. Carnein and H. Trautmann, “evoStream — Evolutionary Stream Clustering Utilizing Idle Times,” <i>Big Data Research</i>, vol. 14, pp. 101–111, 2018, doi: <a href=\"https://doi.org/10.1016/j.bdr.2018.05.005\">10.1016/j.bdr.2018.05.005</a>.","apa":"Carnein, M., &#38; Trautmann, H. (2018). evoStream — Evolutionary Stream Clustering Utilizing Idle Times. <i>Big Data Research</i>, <i>14</i>, 101–111. <a href=\"https://doi.org/10.1016/j.bdr.2018.05.005\">https://doi.org/10.1016/j.bdr.2018.05.005</a>","chicago":"Carnein, Matthias, and Heike Trautmann. “EvoStream — Evolutionary Stream Clustering Utilizing Idle Times.” <i>Big Data Research</i> 14 (2018): 101–111. <a href=\"https://doi.org/10.1016/j.bdr.2018.05.005\">https://doi.org/10.1016/j.bdr.2018.05.005</a>.","short":"M. Carnein, H. Trautmann, Big Data Research 14 (2018) 101–111."},"doi":"10.1016/j.bdr.2018.05.005","user_id":"15504","volume":14,"page":"101–111","language":[{"iso":"eng"}],"_id":"46351","date_updated":"2023-10-16T13:33:43Z","intvolume":"        14","status":"public","title":"evoStream — Evolutionary Stream Clustering Utilizing Idle Times","year":"2018","author":[{"full_name":"Carnein, Matthias","first_name":"Matthias","last_name":"Carnein"},{"id":"100740","full_name":"Trautmann, Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike"}]},{"page":"196–215","_id":"46353","language":[{"iso":"eng"}],"user_id":"15504","doi":"10.1016/j.swevo.2018.02.006","volume":40,"title":"Multiobjective evolutionary algorithms based on target region preferences","year":"2018","status":"public","author":[{"first_name":"L","last_name":"Li","full_name":"Li, L"},{"full_name":"Wang, Y","first_name":"Y","last_name":"Wang"},{"full_name":"Trautmann, Heike","first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","id":"100740"},{"full_name":"Jing, N","first_name":"N","last_name":"Jing"},{"last_name":"Emmerich","first_name":"M","full_name":"Emmerich, M"}],"date_updated":"2023-10-16T13:34:21Z","intvolume":"        40","date_created":"2023-08-04T07:56:57Z","type":"journal_article","department":[{"_id":"34"},{"_id":"819"}],"publication":"Swarm and Evolutionary Computation","citation":{"bibtex":"@article{Li_Wang_Trautmann_Jing_Emmerich_2018, title={Multiobjective evolutionary algorithms based on target region preferences}, volume={40}, DOI={<a href=\"https://doi.org/10.1016/j.swevo.2018.02.006\">10.1016/j.swevo.2018.02.006</a>}, journal={Swarm and Evolutionary Computation}, author={Li, L and Wang, Y and Trautmann, Heike and Jing, N and Emmerich, M}, year={2018}, pages={196–215} }","ama":"Li L, Wang Y, Trautmann H, Jing N, Emmerich M. Multiobjective evolutionary algorithms based on target region preferences. <i>Swarm and Evolutionary Computation</i>. 2018;40:196–215. doi:<a href=\"https://doi.org/10.1016/j.swevo.2018.02.006\">10.1016/j.swevo.2018.02.006</a>","mla":"Li, L., et al. “Multiobjective Evolutionary Algorithms Based on Target Region Preferences.” <i>Swarm and Evolutionary Computation</i>, vol. 40, 2018, pp. 196–215, doi:<a href=\"https://doi.org/10.1016/j.swevo.2018.02.006\">10.1016/j.swevo.2018.02.006</a>.","short":"L. Li, Y. Wang, H. Trautmann, N. Jing, M. Emmerich, Swarm and Evolutionary Computation 40 (2018) 196–215.","chicago":"Li, L, Y Wang, Heike Trautmann, N Jing, and M Emmerich. “Multiobjective Evolutionary Algorithms Based on Target Region Preferences.” <i>Swarm and Evolutionary Computation</i> 40 (2018): 196–215. <a href=\"https://doi.org/10.1016/j.swevo.2018.02.006\">https://doi.org/10.1016/j.swevo.2018.02.006</a>.","ieee":"L. Li, Y. Wang, H. Trautmann, N. Jing, and M. Emmerich, “Multiobjective evolutionary algorithms based on target region preferences,” <i>Swarm and Evolutionary Computation</i>, vol. 40, pp. 196–215, 2018, doi: <a href=\"https://doi.org/10.1016/j.swevo.2018.02.006\">10.1016/j.swevo.2018.02.006</a>.","apa":"Li, L., Wang, Y., Trautmann, H., Jing, N., &#38; Emmerich, M. (2018). Multiobjective evolutionary algorithms based on target region preferences. <i>Swarm and Evolutionary Computation</i>, <i>40</i>, 196–215. <a href=\"https://doi.org/10.1016/j.swevo.2018.02.006\">https://doi.org/10.1016/j.swevo.2018.02.006</a>"},"abstract":[{"text":"Incorporating decision makers' preferences is of great significance in multiobjective optimization. Target region-based multiobjective evolutionary algorithms (TMOEAs), aiming at a well-distributed subset of Pareto optimal solutions within the user-provided region(s), are extensively investigated in this paper. An empirical comparison is performed among three TMOEA instantiations: T-NSGA-II, T-SMS-EMOA and T-R2-EMOA. Experimental results show that T-SMS-EMOA has the best overall performance regarding the hypervolume indicator within the target region, while T-NSGA-II is the fastest algorithm. We also compare TMOEAs with other state-of-the-art preference-based approaches, i.e., DF-SMS-EMOA, RVEA, AS-EMOA and R-NSGA-II to show the advantages of TMOEAs. A case study in the mission planning of earth observation satellite is carried out to verify the capabilities of TMOEAs in the real-world application. Experimental results indicate that preferences can improve the searching ability of MOEAs, and TMOEAs can successfully find nondominated solutions preferred by the decision maker.","lang":"eng"}]}]