[{"status":"public","page":"186–194","publisher":"Association for Computing Machinery","_id":"48861","user_id":"102979","citation":{"mla":"Bossek, Jakob, and Frank Neumann. “Exploring the Feature Space of TSP Instances Using Quality Diversity.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2022, pp. 186–194, doi:<a href=\"https://doi.org/10.1145/3512290.3528851\">10.1145/3512290.3528851</a>.","ama":"Bossek J, Neumann F. Exploring the Feature Space of TSP Instances Using Quality Diversity. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO ’22. Association for Computing Machinery; 2022:186–194. doi:<a href=\"https://doi.org/10.1145/3512290.3528851\">10.1145/3512290.3528851</a>","bibtex":"@inproceedings{Bossek_Neumann_2022, place={New York, NY, USA}, series={GECCO ’22}, title={Exploring the Feature Space of TSP Instances Using Quality Diversity}, DOI={<a href=\"https://doi.org/10.1145/3512290.3528851\">10.1145/3512290.3528851</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Neumann, Frank}, year={2022}, pages={186–194}, collection={GECCO ’22} }","apa":"Bossek, J., &#38; Neumann, F. (2022). Exploring the Feature Space of TSP Instances Using Quality Diversity. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 186–194. <a href=\"https://doi.org/10.1145/3512290.3528851\">https://doi.org/10.1145/3512290.3528851</a>","ieee":"J. Bossek and F. Neumann, “Exploring the Feature Space of TSP Instances Using Quality Diversity,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2022, pp. 186–194, doi: <a href=\"https://doi.org/10.1145/3512290.3528851\">10.1145/3512290.3528851</a>.","short":"J. Bossek, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2022, pp. 186–194.","chicago":"Bossek, Jakob, and Frank Neumann. “Exploring the Feature Space of TSP Instances Using Quality Diversity.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 186–194. GECCO ’22. New York, NY, USA: Association for Computing Machinery, 2022. <a href=\"https://doi.org/10.1145/3512290.3528851\">https://doi.org/10.1145/3512290.3528851</a>."},"place":"New York, NY, USA","title":"Exploring the Feature Space of TSP Instances Using Quality Diversity","year":"2022","author":[{"full_name":"Bossek, Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","first_name":"Jakob","id":"102979"},{"full_name":"Neumann, Frank","first_name":"Frank","last_name":"Neumann"}],"publication_identifier":{"isbn":["978-1-4503-9237-2"]},"date_updated":"2023-12-13T10:45:56Z","publication_status":"published","series_title":"GECCO ’22","language":[{"iso":"eng"}],"doi":"10.1145/3512290.3528851","publication":"Proceedings of the Genetic and Evolutionary Computation Conference","abstract":[{"text":"Generating instances of different properties is key to algorithm selection methods that differentiate between the performance of different solvers for a given combinatorial optimization problem. A wide range of methods using evolutionary computation techniques has been introduced in recent years. With this paper, we contribute to this area of research by providing a new approach based on quality diversity (QD) that is able to explore the whole feature space. QD algorithms allow to create solutions of high quality within a given feature space by splitting it up into boxes and improving solution quality within each box. We use our QD approach for the generation of TSP instances to visualize and analyze the variety of instances differentiating various TSP solvers and compare it to instances generated by established approaches from the literature.","lang":"eng"}],"extern":"1","date_created":"2023-11-14T15:58:55Z","keyword":["instance features","instance generation","quality diversity","TSP"],"type":"conference","department":[{"_id":"819"}]},{"user_id":"102979","page":"824–842","_id":"48868","publisher":"Association for Computing Machinery","status":"public","place":"New York, NY, USA","citation":{"short":"J. Bossek, A. Neumann, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, New York, NY, USA, 2022, pp. 824–842.","ama":"Bossek J, Neumann A, Neumann F. Evolutionary Diversity Optimization for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>. GECCO’22. Association for Computing Machinery; 2022:824–842. doi:<a href=\"https://doi.org/10.1145/3520304.3533626\">10.1145/3520304.3533626</a>","chicago":"Bossek, Jakob, Aneta Neumann, and Frank Neumann. “Evolutionary Diversity Optimization for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, 824–842. GECCO’22. New York, NY, USA: Association for Computing Machinery, 2022. <a href=\"https://doi.org/10.1145/3520304.3533626\">https://doi.org/10.1145/3520304.3533626</a>.","bibtex":"@inproceedings{Bossek_Neumann_Neumann_2022, place={New York, NY, USA}, series={GECCO’22}, title={Evolutionary Diversity Optimization for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA}, DOI={<a href=\"https://doi.org/10.1145/3520304.3533626\">10.1145/3520304.3533626</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference Companion}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Neumann, Aneta and Neumann, Frank}, year={2022}, pages={824–842}, collection={GECCO’22} }","apa":"Bossek, J., Neumann, A., &#38; Neumann, F. (2022). Evolutionary Diversity Optimization for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA. <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, 824–842. <a href=\"https://doi.org/10.1145/3520304.3533626\">https://doi.org/10.1145/3520304.3533626</a>","mla":"Bossek, Jakob, et al. “Evolutionary Diversity Optimization for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA.” <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, Association for Computing Machinery, 2022, pp. 824–842, doi:<a href=\"https://doi.org/10.1145/3520304.3533626\">10.1145/3520304.3533626</a>.","ieee":"J. Bossek, A. Neumann, and F. Neumann, “Evolutionary Diversity Optimization for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, 2022, pp. 824–842, doi: <a href=\"https://doi.org/10.1145/3520304.3533626\">10.1145/3520304.3533626</a>."},"doi":"10.1145/3520304.3533626","series_title":"GECCO’22","language":[{"iso":"eng"}],"publication_status":"published","date_updated":"2023-12-13T10:46:19Z","year":"2022","title":"Evolutionary Diversity Optimization for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA","publication_identifier":{"isbn":["978-1-4503-9268-6"]},"author":[{"first_name":"Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","full_name":"Bossek, Jakob","id":"102979"},{"full_name":"Neumann, Aneta","first_name":"Aneta","last_name":"Neumann"},{"full_name":"Neumann, Frank","first_name":"Frank","last_name":"Neumann"}],"type":"conference","department":[{"_id":"819"}],"date_created":"2023-11-14T15:58:56Z","extern":"1","publication":"Proceedings of the Genetic and Evolutionary Computation Conference Companion"},{"_id":"48882","publisher":"Springer International Publishing","page":"192–206","editor":[{"first_name":"Günter","last_name":"Rudolph","full_name":"Rudolph, Günter"},{"full_name":"Kononova, Anna V.","last_name":"Kononova","first_name":"Anna V."},{"full_name":"Aguirre, Hernán","first_name":"Hernán","last_name":"Aguirre"},{"full_name":"Kerschke, Pascal","first_name":"Pascal","last_name":"Kerschke"},{"full_name":"Ochoa, Gabriela","last_name":"Ochoa","first_name":"Gabriela"},{"last_name":"Tusar","first_name":"Tea","full_name":"Tusar, Tea"}],"user_id":"102979","status":"public","place":"Cham","citation":{"bibtex":"@inproceedings{Heins_Rook_Schäpermeier_Kerschke_Bossek_Trautmann_2022, place={Cham}, series={Lecture Notes in Computer Science}, title={BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems}, DOI={<a href=\"https://doi.org/10.1007/978-3-031-14714-2_14\">10.1007/978-3-031-14714-2_14</a>}, booktitle={Parallel Problem Solving from Nature (PPSN XVII)}, publisher={Springer International Publishing}, author={Heins, Jonathan and Rook, Jeroen and Schäpermeier, Lennart and Kerschke, Pascal and Bossek, Jakob and Trautmann, Heike}, editor={Rudolph, Günter and Kononova, Anna V. and Aguirre, Hernán and Kerschke, Pascal and Ochoa, Gabriela and Tusar, Tea}, year={2022}, pages={192–206}, collection={Lecture Notes in Computer Science} }","ama":"Heins J, Rook J, Schäpermeier L, Kerschke P, Bossek J, Trautmann H. BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems. In: Rudolph G, Kononova AV, Aguirre H, Kerschke P, Ochoa G, Tusar T, eds. <i>Parallel Problem Solving from Nature (PPSN XVII)</i>. Lecture Notes in Computer Science. Springer International Publishing; 2022:192–206. doi:<a href=\"https://doi.org/10.1007/978-3-031-14714-2_14\">10.1007/978-3-031-14714-2_14</a>","mla":"Heins, Jonathan, et al. “BBE: Basin-Based Evaluation of Multimodal Multi-Objective Optimization Problems.” <i>Parallel Problem Solving from Nature (PPSN XVII)</i>, edited by Günter Rudolph et al., Springer International Publishing, 2022, pp. 192–206, doi:<a href=\"https://doi.org/10.1007/978-3-031-14714-2_14\">10.1007/978-3-031-14714-2_14</a>.","short":"J. Heins, J. Rook, L. Schäpermeier, P. Kerschke, J. Bossek, H. Trautmann, in: G. Rudolph, A.V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, T. Tusar (Eds.), Parallel Problem Solving from Nature (PPSN XVII), Springer International Publishing, Cham, 2022, pp. 192–206.","chicago":"Heins, Jonathan, Jeroen Rook, Lennart Schäpermeier, Pascal Kerschke, Jakob Bossek, and Heike Trautmann. “BBE: Basin-Based Evaluation of Multimodal Multi-Objective Optimization Problems.” In <i>Parallel Problem Solving from Nature (PPSN XVII)</i>, edited by Günter Rudolph, Anna V. Kononova, Hernán Aguirre, Pascal Kerschke, Gabriela Ochoa, and Tea Tusar, 192–206. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2022. <a href=\"https://doi.org/10.1007/978-3-031-14714-2_14\">https://doi.org/10.1007/978-3-031-14714-2_14</a>.","ieee":"J. Heins, J. Rook, L. Schäpermeier, P. Kerschke, J. Bossek, and H. Trautmann, “BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems,” in <i>Parallel Problem Solving from Nature (PPSN XVII)</i>, 2022, pp. 192–206, doi: <a href=\"https://doi.org/10.1007/978-3-031-14714-2_14\">10.1007/978-3-031-14714-2_14</a>.","apa":"Heins, J., Rook, J., Schäpermeier, L., Kerschke, P., Bossek, J., &#38; Trautmann, H. (2022). BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems. In G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, &#38; T. Tusar (Eds.), <i>Parallel Problem Solving from Nature (PPSN XVII)</i> (pp. 192–206). Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-031-14714-2_14\">https://doi.org/10.1007/978-3-031-14714-2_14</a>"},"language":[{"iso":"eng"}],"series_title":"Lecture Notes in Computer Science","doi":"10.1007/978-3-031-14714-2_14","author":[{"full_name":"Heins, Jonathan","last_name":"Heins","first_name":"Jonathan"},{"first_name":"Jeroen","last_name":"Rook","full_name":"Rook, Jeroen"},{"first_name":"Lennart","last_name":"Schäpermeier","full_name":"Schäpermeier, Lennart"},{"last_name":"Kerschke","first_name":"Pascal","full_name":"Kerschke, Pascal"},{"id":"102979","last_name":"Bossek","first_name":"Jakob","orcid":"0000-0002-4121-4668","full_name":"Bossek, Jakob"},{"first_name":"Heike","last_name":"Trautmann","full_name":"Trautmann, Heike"}],"publication_identifier":{"isbn":["978-3-031-14714-2"]},"title":"BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems","year":"2022","date_updated":"2023-12-13T10:47:50Z","date_created":"2023-11-14T15:58:58Z","department":[{"_id":"819"}],"type":"conference","keyword":["Anytime behavior","Benchmarking","Continuous optimization","Multi-objective optimization","Multimodality","Performance metric"],"publication":"Parallel Problem Solving from Nature (PPSN XVII)","abstract":[{"lang":"eng","text":"In multimodal multi-objective optimization (MMMOO), the focus is not solely on convergence in objective space, but rather also on explicitly ensuring diversity in decision space. We illustrate why commonly used diversity measures are not entirely appropriate for this task and propose a sophisticated basin-based evaluation (BBE) method. Also, BBE variants are developed, capturing the anytime behavior of algorithms. The set of BBE measures is tested by means of an algorithm configuration study. We show that these new measures also transfer properties of the well-established hypervolume (HV) indicator to the domain of MMMOO, thus also accounting for objective space convergence. Moreover, we advance MMMOO research by providing insights into the multimodal performance of the considered algorithms. Specifically, algorithms exploiting local structures are shown to outperform classical evolutionary multi-objective optimizers regarding the BBE variants and respective trade-off with HV."}],"extern":"1"},{"date_created":"2023-11-14T15:59:00Z","keyword":["Co-evolutionary algorithms","Evolutionary diversity optimisation","Quality diversity","Traveling thief problem"],"type":"conference","department":[{"_id":"819"}],"publication":"Parallel Problem Solving from Nature (PPSN XVII)","abstract":[{"text":"Recently different evolutionary computation approaches have been developed that generate sets of high quality diverse solutions for a given optimisation problem. Many studies have considered diversity 1) as a mean to explore niches in behavioural space (quality diversity) or 2) to increase the structural differences of solutions (evolutionary diversity optimisation). In this study, we introduce a co-evolutionary algorithm to simultaneously explore the two spaces for the multi-component traveling thief problem. The results show the capability of the co-evolutionary algorithm to achieve significantly higher diversity compared to the baseline evolutionary diversity algorithms from the literature.","lang":"eng"}],"extern":"1","language":[{"iso":"eng"}],"series_title":"Lecture Notes in Computer Science","doi":"10.1007/978-3-031-14714-2_17","year":"2022","title":"Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem","publication_identifier":{"isbn":["978-3-031-14714-2"]},"author":[{"last_name":"Nikfarjam","first_name":"Adel","full_name":"Nikfarjam, Adel"},{"full_name":"Neumann, Aneta","last_name":"Neumann","first_name":"Aneta"},{"id":"102979","last_name":"Bossek","first_name":"Jakob","orcid":"0000-0002-4121-4668","full_name":"Bossek, Jakob"},{"full_name":"Neumann, Frank","last_name":"Neumann","first_name":"Frank"}],"date_updated":"2023-12-13T10:49:51Z","publication_status":"published","place":"Cham","citation":{"mla":"Nikfarjam, Adel, et al. “Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem.” <i>Parallel Problem Solving from Nature (PPSN XVII)</i>, edited by Günter Rudolph et al., Springer International Publishing, 2022, pp. 237–249, doi:<a href=\"https://doi.org/10.1007/978-3-031-14714-2_17\">10.1007/978-3-031-14714-2_17</a>.","ama":"Nikfarjam A, Neumann A, Bossek J, Neumann F. Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem. In: Rudolph G, Kononova AV, Aguirre H, Kerschke P, Ochoa G, Tu\\v sar T, eds. <i>Parallel Problem Solving from Nature (PPSN XVII)</i>. Lecture Notes in Computer Science. Springer International Publishing; 2022:237–249. doi:<a href=\"https://doi.org/10.1007/978-3-031-14714-2_17\">10.1007/978-3-031-14714-2_17</a>","bibtex":"@inproceedings{Nikfarjam_Neumann_Bossek_Neumann_2022, place={Cham}, series={Lecture Notes in Computer Science}, title={Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem}, DOI={<a href=\"https://doi.org/10.1007/978-3-031-14714-2_17\">10.1007/978-3-031-14714-2_17</a>}, booktitle={Parallel Problem Solving from Nature (PPSN XVII)}, publisher={Springer International Publishing}, author={Nikfarjam, Adel and Neumann, Aneta and Bossek, Jakob and Neumann, Frank}, editor={Rudolph, Günter and Kononova, Anna V. and Aguirre, Hernán and Kerschke, Pascal and Ochoa, Gabriela and Tu\\v sar, Tea}, year={2022}, pages={237–249}, collection={Lecture Notes in Computer Science} }","apa":"Nikfarjam, A., Neumann, A., Bossek, J., &#38; Neumann, F. (2022). Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem. In G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, &#38; T. Tu\\v sar (Eds.), <i>Parallel Problem Solving from Nature (PPSN XVII)</i> (pp. 237–249). Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-031-14714-2_17\">https://doi.org/10.1007/978-3-031-14714-2_17</a>","ieee":"A. Nikfarjam, A. Neumann, J. Bossek, and F. Neumann, “Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem,” in <i>Parallel Problem Solving from Nature (PPSN XVII)</i>, 2022, pp. 237–249, doi: <a href=\"https://doi.org/10.1007/978-3-031-14714-2_17\">10.1007/978-3-031-14714-2_17</a>.","short":"A. Nikfarjam, A. Neumann, J. Bossek, F. Neumann, in: G. Rudolph, A.V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, T. Tu\\v sar (Eds.), Parallel Problem Solving from Nature (PPSN XVII), Springer International Publishing, Cham, 2022, pp. 237–249.","chicago":"Nikfarjam, Adel, Aneta Neumann, Jakob Bossek, and Frank Neumann. “Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem.” In <i>Parallel Problem Solving from Nature (PPSN XVII)</i>, edited by Günter Rudolph, Anna V. Kononova, Hernán Aguirre, Pascal Kerschke, Gabriela Ochoa, and Tea Tu\\v sar, 237–249. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2022. <a href=\"https://doi.org/10.1007/978-3-031-14714-2_17\">https://doi.org/10.1007/978-3-031-14714-2_17</a>."},"page":"237–249","publisher":"Springer International Publishing","_id":"48894","user_id":"102979","editor":[{"full_name":"Rudolph, Günter","first_name":"Günter","last_name":"Rudolph"},{"full_name":"Kononova, Anna V.","first_name":"Anna V.","last_name":"Kononova"},{"first_name":"Hernán","last_name":"Aguirre","full_name":"Aguirre, Hernán"},{"full_name":"Kerschke, Pascal","first_name":"Pascal","last_name":"Kerschke"},{"full_name":"Ochoa, Gabriela","first_name":"Gabriela","last_name":"Ochoa"},{"full_name":"Tu\\v sar, Tea","first_name":"Tea","last_name":"Tu\\v sar"}],"status":"public"},{"intvolume":"        12","date_updated":"2023-12-13T10:50:56Z","publication_identifier":{"issn":["2076-3417"]},"author":[{"first_name":"Lena","last_name":"Clever","full_name":"Clever, Lena"},{"full_name":"Pohl, Janina Susanne","first_name":"Janina Susanne","last_name":"Pohl"},{"id":"102979","last_name":"Bossek","orcid":"0000-0002-4121-4668","first_name":"Jakob","full_name":"Bossek, Jakob"},{"full_name":"Kerschke, Pascal","first_name":"Pascal","last_name":"Kerschke"},{"full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike"}],"title":"Process-Oriented Stream Classification Pipeline: A Literature Review","year":"2022","doi":"10.3390/app12189094","language":[{"iso":"eng"}],"abstract":[{"text":"Due to the rise of continuous data-generating applications, analyzing data streams has gained increasing attention over the past decades. A core research area in stream data is stream classification, which categorizes or detects data points within an evolving stream of observations. Areas of stream classification are diverse\\textemdash ranging, e.g., from monitoring sensor data to analyzing a wide range of (social) media applications. Research in stream classification is related to developing methods that adapt to the changing and potentially volatile data stream. It focuses on individual aspects of the stream classification pipeline, e.g., designing suitable algorithm architectures, an efficient train and test procedure, or detecting so-called concept drifts. As a result of the many different research questions and strands, the field is challenging to grasp, especially for beginners. This survey explores, summarizes, and categorizes work within the domain of stream classification and identifies core research threads over the past few years. It is structured based on the stream classification process to facilitate coordination within this complex topic, including common application scenarios and benchmarking data sets. Thus, both newcomers to the field and experts who want to widen their scope can gain (additional) insight into this research area and find starting points and pointers to more in-depth literature on specific issues and research directions in the field.","lang":"eng"}],"publication":"Applied Sciences","issue":"18","department":[{"_id":"819"}],"type":"journal_article","keyword":["big data","data mining","data stream analysis","machine learning","stream classification","supervised learning"],"date_created":"2023-11-14T15:58:57Z","status":"public","volume":12,"user_id":"102979","_id":"48878","publisher":"{Multidisciplinary Digital Publishing Institute}","page":"9094","citation":{"apa":"Clever, L., Pohl, J. S., Bossek, J., Kerschke, P., &#38; Trautmann, H. (2022). Process-Oriented Stream Classification Pipeline: A Literature Review. <i>Applied Sciences</i>, <i>12</i>(18), 9094. <a href=\"https://doi.org/10.3390/app12189094\">https://doi.org/10.3390/app12189094</a>","ieee":"L. Clever, J. S. Pohl, J. Bossek, P. Kerschke, and H. Trautmann, “Process-Oriented Stream Classification Pipeline: A Literature Review,” <i>Applied Sciences</i>, vol. 12, no. 18, p. 9094, 2022, doi: <a href=\"https://doi.org/10.3390/app12189094\">10.3390/app12189094</a>.","short":"L. Clever, J.S. Pohl, J. Bossek, P. Kerschke, H. Trautmann, Applied Sciences 12 (2022) 9094.","chicago":"Clever, Lena, Janina Susanne Pohl, Jakob Bossek, Pascal Kerschke, and Heike Trautmann. “Process-Oriented Stream Classification Pipeline: A Literature Review.” <i>Applied Sciences</i> 12, no. 18 (2022): 9094. <a href=\"https://doi.org/10.3390/app12189094\">https://doi.org/10.3390/app12189094</a>.","mla":"Clever, Lena, et al. “Process-Oriented Stream Classification Pipeline: A Literature Review.” <i>Applied Sciences</i>, vol. 12, no. 18, {Multidisciplinary Digital Publishing Institute}, 2022, p. 9094, doi:<a href=\"https://doi.org/10.3390/app12189094\">10.3390/app12189094</a>.","ama":"Clever L, Pohl JS, Bossek J, Kerschke P, Trautmann H. Process-Oriented Stream Classification Pipeline: A Literature Review. <i>Applied Sciences</i>. 2022;12(18):9094. doi:<a href=\"https://doi.org/10.3390/app12189094\">10.3390/app12189094</a>","bibtex":"@article{Clever_Pohl_Bossek_Kerschke_Trautmann_2022, title={Process-Oriented Stream Classification Pipeline: A Literature Review}, volume={12}, DOI={<a href=\"https://doi.org/10.3390/app12189094\">10.3390/app12189094</a>}, number={18}, journal={Applied Sciences}, publisher={{Multidisciplinary Digital Publishing Institute}}, author={Clever, Lena and Pohl, Janina Susanne and Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}, year={2022}, pages={9094} }"}},{"page":"356–359","_id":"48896","publisher":"Association for Computing Machinery","user_id":"102979","status":"public","place":"New York, NY, USA","citation":{"mla":"Rook, Jeroen, et al. “On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems.” <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, Association for Computing Machinery, 2022, pp. 356–359, doi:<a href=\"https://doi.org/10.1145/3520304.3528998\">10.1145/3520304.3528998</a>.","ama":"Rook J, Trautmann H, Bossek J, Grimme C. On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>. GECCO’22. Association for Computing Machinery; 2022:356–359. doi:<a href=\"https://doi.org/10.1145/3520304.3528998\">10.1145/3520304.3528998</a>","bibtex":"@inproceedings{Rook_Trautmann_Bossek_Grimme_2022, place={New York, NY, USA}, series={GECCO’22}, title={On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems}, DOI={<a href=\"https://doi.org/10.1145/3520304.3528998\">10.1145/3520304.3528998</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference Companion}, publisher={Association for Computing Machinery}, author={Rook, Jeroen and Trautmann, Heike and Bossek, Jakob and Grimme, Christian}, year={2022}, pages={356–359}, collection={GECCO’22} }","apa":"Rook, J., Trautmann, H., Bossek, J., &#38; Grimme, C. (2022). 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