[{"language":[{"iso":"eng"}],"series_title":"Lecture Notes in Computer Science","doi":"10.1007/978-3-031-14714-2_14","year":"2022","title":"BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems","publication_identifier":{"isbn":["978-3-031-14714-2"]},"author":[{"first_name":"Jonathan","last_name":"Heins","full_name":"Heins, Jonathan"},{"last_name":"Rook","first_name":"Jeroen","full_name":"Rook, Jeroen"},{"full_name":"Schäpermeier, Lennart","first_name":"Lennart","last_name":"Schäpermeier"},{"full_name":"Kerschke, Pascal","first_name":"Pascal","last_name":"Kerschke"},{"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"}],"date_updated":"2023-12-13T10:47:50Z","date_created":"2023-11-14T15:58:58Z","type":"conference","keyword":["Anytime behavior","Benchmarking","Continuous optimization","Multi-objective optimization","Multimodality","Performance metric"],"department":[{"_id":"819"}],"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","page":"192–206","_id":"48882","publisher":"Springer International Publishing","user_id":"102979","editor":[{"last_name":"Rudolph","first_name":"Günter","full_name":"Rudolph, Günter"},{"first_name":"Anna V.","last_name":"Kononova","full_name":"Kononova, Anna V."},{"full_name":"Aguirre, Hernán","last_name":"Aguirre","first_name":"Hernán"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"full_name":"Ochoa, Gabriela","first_name":"Gabriela","last_name":"Ochoa"},{"full_name":"Tusar, Tea","last_name":"Tusar","first_name":"Tea"}],"status":"public","place":"Cham","citation":{"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>","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>."}},{"date_created":"2023-11-14T15:59:00Z","department":[{"_id":"819"}],"type":"conference","keyword":["Co-evolutionary algorithms","Evolutionary diversity optimisation","Quality diversity","Traveling thief problem"],"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","publication_identifier":{"isbn":["978-3-031-14714-2"]},"author":[{"first_name":"Adel","last_name":"Nikfarjam","full_name":"Nikfarjam, Adel"},{"full_name":"Neumann, Aneta","last_name":"Neumann","first_name":"Aneta"},{"full_name":"Bossek, Jakob","first_name":"Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","id":"102979"},{"full_name":"Neumann, Frank","first_name":"Frank","last_name":"Neumann"}],"title":"Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem","year":"2022","date_updated":"2023-12-13T10:49:51Z","publication_status":"published","place":"Cham","citation":{"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>.","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} }"},"_id":"48894","publisher":"Springer International Publishing","page":"237–249","editor":[{"first_name":"Günter","last_name":"Rudolph","full_name":"Rudolph, Günter"},{"first_name":"Anna V.","last_name":"Kononova","full_name":"Kononova, Anna V."},{"last_name":"Aguirre","first_name":"Hernán","full_name":"Aguirre, Hernán"},{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"},{"last_name":"Ochoa","first_name":"Gabriela","full_name":"Ochoa, Gabriela"},{"full_name":"Tu\\v sar, Tea","first_name":"Tea","last_name":"Tu\\v sar"}],"user_id":"102979","status":"public"},{"citation":{"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} }","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>."},"status":"public","user_id":"102979","volume":12,"page":"9094","publisher":"{Multidisciplinary Digital Publishing Institute}","_id":"48878","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"}],"issue":"18","publication":"Applied Sciences","type":"journal_article","keyword":["big data","data mining","data stream analysis","machine learning","stream classification","supervised learning"],"department":[{"_id":"819"}],"date_created":"2023-11-14T15:58:57Z","date_updated":"2023-12-13T10:50:56Z","intvolume":"        12","title":"Process-Oriented Stream Classification Pipeline: A Literature Review","year":"2022","author":[{"last_name":"Clever","first_name":"Lena","full_name":"Clever, Lena"},{"last_name":"Pohl","first_name":"Janina Susanne","full_name":"Pohl, Janina Susanne"},{"id":"102979","full_name":"Bossek, Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","first_name":"Jakob"},{"full_name":"Kerschke, Pascal","first_name":"Pascal","last_name":"Kerschke"},{"first_name":"Heike","last_name":"Trautmann","full_name":"Trautmann, Heike"}],"publication_identifier":{"issn":["2076-3417"]},"doi":"10.3390/app12189094","language":[{"iso":"eng"}]},{"citation":{"short":"J. Rook, H. Trautmann, J. Bossek, C. Grimme, in: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, New York, NY, USA, 2022, pp. 356–359.","chicago":"Rook, Jeroen, Heike Trautmann, Jakob Bossek, and Christian Grimme. “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>, 356–359. GECCO’22. New York, NY, USA: Association for Computing Machinery, 2022. <a href=\"https://doi.org/10.1145/3520304.3528998\">https://doi.org/10.1145/3520304.3528998</a>.","ieee":"J. Rook, H. Trautmann, J. Bossek, and C. Grimme, “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>, 2022, pp. 356–359, doi: <a href=\"https://doi.org/10.1145/3520304.3528998\">10.1145/3520304.3528998</a>.","apa":"Rook, J., Trautmann, H., Bossek, J., &#38; Grimme, C. (2022). 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>, 356–359. <a href=\"https://doi.org/10.1145/3520304.3528998\">https://doi.org/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} }","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>","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>."},"place":"New York, NY, USA","status":"public","user_id":"102979","page":"356–359","_id":"48896","publisher":"Association for Computing Machinery","extern":"1","abstract":[{"lang":"eng","text":"Hardness of Multi-Objective (MO) continuous optimization problems results from an interplay of various problem characteristics, e. g. the degree of multi-modality. We present a benchmark study of classical and diversity focused optimizers on multi-modal MO problems based on automated algorithm configuration. We show the large effect of the latter and investigate the trade-off between convergence in objective space and diversity in decision space."}],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","keyword":["configuration","multi-modality","multi-objective optimization"],"type":"conference","department":[{"_id":"819"}],"date_created":"2023-11-14T15:59:00Z","date_updated":"2023-12-13T10:50:24Z","year":"2022","title":"On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems","publication_identifier":{"isbn":["978-1-4503-9268-6"]},"author":[{"full_name":"Rook, Jeroen","first_name":"Jeroen","last_name":"Rook"},{"full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike"},{"id":"102979","first_name":"Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","full_name":"Bossek, Jakob"},{"first_name":"Christian","last_name":"Grimme","full_name":"Grimme, Christian"}],"doi":"10.1145/3520304.3528998","series_title":"GECCO’22","language":[{"iso":"eng"}]},{"date_created":"2024-03-13T09:59:21Z","department":[{"_id":"819"}],"type":"journal_article","citation":{"apa":"Rodrigues, A. S., Kerschke, P., Pereira, C. A. D. B., Trautmann, H., Wagner, C., Hellingrath, B., &#38; Polpo, A. (2022). Estimation of component reliability from superposed renewal processes by means of latent variables. <i>Comput. Stat.</i>, <i>37</i>(1), 355–379. <a href=\"https://doi.org/10.1007/S00180-021-01124-0\">https://doi.org/10.1007/S00180-021-01124-0</a>","ieee":"A. S. Rodrigues <i>et al.</i>, “Estimation of component reliability from superposed renewal processes by means of latent variables,” <i>Comput. Stat.</i>, vol. 37, no. 1, pp. 355–379, 2022, doi: <a href=\"https://doi.org/10.1007/S00180-021-01124-0\">10.1007/S00180-021-01124-0</a>.","chicago":"Rodrigues, Agatha S., Pascal Kerschke, Carlos Alberto De Bragança Pereira, Heike Trautmann, Carolin Wagner, Bernd Hellingrath, and Adriano Polpo. “Estimation of Component Reliability from Superposed Renewal Processes by Means of Latent Variables.” <i>Comput. Stat.</i> 37, no. 1 (2022): 355–379. <a href=\"https://doi.org/10.1007/S00180-021-01124-0\">https://doi.org/10.1007/S00180-021-01124-0</a>.","short":"A.S. Rodrigues, P. Kerschke, C.A.D.B. Pereira, H. Trautmann, C. Wagner, B. Hellingrath, A. Polpo, Comput. Stat. 37 (2022) 355–379.","mla":"Rodrigues, Agatha S., et al. “Estimation of Component Reliability from Superposed Renewal Processes by Means of Latent Variables.” <i>Comput. Stat.</i>, vol. 37, no. 1, 2022, pp. 355–379, doi:<a href=\"https://doi.org/10.1007/S00180-021-01124-0\">10.1007/S00180-021-01124-0</a>.","ama":"Rodrigues AS, Kerschke P, Pereira CADB, et al. Estimation of component reliability from superposed renewal processes by means of latent variables. <i>Comput Stat</i>. 2022;37(1):355–379. doi:<a href=\"https://doi.org/10.1007/S00180-021-01124-0\">10.1007/S00180-021-01124-0</a>","bibtex":"@article{Rodrigues_Kerschke_Pereira_Trautmann_Wagner_Hellingrath_Polpo_2022, title={Estimation of component reliability from superposed renewal processes by means of latent variables}, volume={37}, DOI={<a href=\"https://doi.org/10.1007/S00180-021-01124-0\">10.1007/S00180-021-01124-0</a>}, number={1}, journal={Comput. Stat.}, author={Rodrigues, Agatha S. and Kerschke, Pascal and Pereira, Carlos Alberto De Bragança and Trautmann, Heike and Wagner, Carolin and Hellingrath, Bernd and Polpo, Adriano}, year={2022}, pages={355–379} }"},"issue":"1","publication":"Comput. Stat.","language":[{"iso":"eng"}],"_id":"52532","page":"355–379","volume":37,"user_id":"15504","doi":"10.1007/S00180-021-01124-0","author":[{"full_name":"Rodrigues, Agatha S.","first_name":"Agatha S.","last_name":"Rodrigues"},{"full_name":"Kerschke, Pascal","first_name":"Pascal","last_name":"Kerschke"},{"full_name":"Pereira, Carlos Alberto De Bragança","first_name":"Carlos Alberto De Bragança","last_name":"Pereira"},{"last_name":"Trautmann","orcid":"0000-0002-9788-8282","first_name":"Heike","full_name":"Trautmann, Heike","id":"100740"},{"full_name":"Wagner, Carolin","first_name":"Carolin","last_name":"Wagner"},{"full_name":"Hellingrath, Bernd","last_name":"Hellingrath","first_name":"Bernd"},{"full_name":"Polpo, Adriano","last_name":"Polpo","first_name":"Adriano"}],"status":"public","year":"2022","title":"Estimation of component reliability from superposed renewal processes by means of latent variables","intvolume":"        37","date_updated":"2024-03-13T10:28:01Z"},{"place":"New York, NY, USA","date_created":"2023-08-04T07:15:59Z","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","citation":{"ieee":"M. Seiler, R. P. Prager, P. Kerschke, and H. Trautmann, “A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2022, pp. 657–665, doi: <a href=\"https://doi.org/10.1145/3512290.3528834\">10.1145/3512290.3528834</a>.","apa":"Seiler, M., Prager, R. P., Kerschke, P., &#38; Trautmann, H. (2022). A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 657–665. <a href=\"https://doi.org/10.1145/3512290.3528834\">https://doi.org/10.1145/3512290.3528834</a>","chicago":"Seiler, Moritz, Raphael Patrick Prager, Pascal Kerschke, and Heike Trautmann. “A Collection of Deep Learning-Based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 657–665. New York, NY, USA: Association for Computing Machinery, 2022. <a href=\"https://doi.org/10.1145/3512290.3528834\">https://doi.org/10.1145/3512290.3528834</a>.","short":"M. Seiler, R.P. Prager, P. Kerschke, H. Trautmann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2022, pp. 657–665.","mla":"Seiler, Moritz, et al. “A Collection of Deep Learning-Based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2022, pp. 657–665, doi:<a href=\"https://doi.org/10.1145/3512290.3528834\">10.1145/3512290.3528834</a>.","bibtex":"@inproceedings{Seiler_Prager_Kerschke_Trautmann_2022, place={New York, NY, USA}, title={A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes}, DOI={<a href=\"https://doi.org/10.1145/3512290.3528834\">10.1145/3512290.3528834</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Seiler, Moritz and Prager, Raphael Patrick and Kerschke, Pascal and Trautmann, Heike}, year={2022}, pages={657–665} }","ama":"Seiler M, Prager RP, Kerschke P, Trautmann H. A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. Association for Computing Machinery; 2022:657–665. doi:<a href=\"https://doi.org/10.1145/3512290.3528834\">10.1145/3512290.3528834</a>"},"publication":"Proceedings of the Genetic and Evolutionary Computation Conference","abstract":[{"text":"Exploratory Landscape Analysis is a powerful technique for numerically characterizing landscapes of single-objective continuous optimization problems. Landscape insights are crucial both for problem understanding as well as for assessing benchmark set diversity and composition. Despite the irrefutable usefulness of these features, they suffer from their own ailments and downsides. Hence, in this work we provide a collection of different approaches to characterize optimization landscapes. Similar to conventional landscape features, we require a small initial sample. However, instead of computing features based on that sample, we develop alternative representations of the original sample. These range from point clouds to 2D images and, therefore, are entirely feature-free. We demonstrate and validate our devised methods on the BBOB testbed and predict, with the help of Deep Learning, the high-level, expert-based landscape properties such as the degree of multimodality and the existence of funnel structures. The quality of our approaches is on par with methods relying on the traditional landscape features. Thereby, we provide an exciting new perspective on every research area which utilizes problem information such as problem understanding and algorithm design as well as automated algorithm configuration and selection.","lang":"eng"}],"_id":"46307","language":[{"iso":"eng"}],"publisher":"Association for Computing Machinery","page":"657–665","doi":"10.1145/3512290.3528834","user_id":"15504","author":[{"full_name":"Seiler, Moritz","first_name":"Moritz","last_name":"Seiler","id":"105520"},{"full_name":"Prager, Raphael Patrick","last_name":"Prager","first_name":"Raphael Patrick"},{"last_name":"Kerschke","first_name":"Pascal","full_name":"Kerschke, Pascal"},{"id":"100740","full_name":"Trautmann, Heike","orcid":"0000-0002-9788-8282","first_name":"Heike","last_name":"Trautmann"}],"publication_identifier":{"isbn":["9781450392372"]},"title":"A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes","status":"public","year":"2022","date_updated":"2024-06-07T07:13:23Z"},{"date_updated":"2024-06-07T07:13:47Z","publication_identifier":{"isbn":["978-3-031-14714-2"]},"author":[{"last_name":"Prager","first_name":"Raphael Patrick","full_name":"Prager, Raphael Patrick"},{"id":"105520","full_name":"Seiler, Moritz","last_name":"Seiler","first_name":"Moritz"},{"id":"100740","orcid":"0000-0002-9788-8282","first_name":"Heike","last_name":"Trautmann","full_name":"Trautmann, Heike"},{"last_name":"Kerschke","first_name":"Pascal","full_name":"Kerschke, Pascal"}],"title":"Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods","year":"2022","doi":"10.1007/978-3-031-14714-2_1","language":[{"iso":"eng"}],"abstract":[{"lang":"eng","text":"In recent years, feature-based automated algorithm selection using exploratory landscape analysis has demonstrated its great potential in single-objective continuous black-box optimization. However, feature computation is problem-specific and can be costly in terms of computational resources. This paper investigates feature-free approaches that rely on state-of-the-art deep learning techniques operating on either images or point clouds. We show that point-cloud-based strategies, in particular, are highly competitive and also substantially reduce the size of the required solver portfolio. Moreover, we highlight the effect and importance of cost-sensitive learning in automated algorithm selection models."}],"publication":"Parallel Problem Solving from Nature — PPSN XVII","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","date_created":"2023-08-04T07:12:33Z","status":"public","editor":[{"full_name":"Rudolph, Günter","first_name":"Günter","last_name":"Rudolph"},{"last_name":"Kononova","first_name":"Anna V.","full_name":"Kononova, Anna V."},{"full_name":"Aguirre, Hernán","first_name":"Hernán","last_name":"Aguirre"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"last_name":"Ochoa","first_name":"Gabriela","full_name":"Ochoa, Gabriela"},{"full_name":"Tušar, Tea","last_name":"Tušar","first_name":"Tea"}],"user_id":"15504","_id":"46304","publisher":"Springer International Publishing","page":"3–17","citation":{"mla":"Prager, Raphael Patrick, et al. “Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods.” <i>Parallel Problem Solving from Nature — PPSN XVII</i>, edited by Günter Rudolph et al., Springer International Publishing, 2022, pp. 3–17, doi:<a href=\"https://doi.org/10.1007/978-3-031-14714-2_1\">10.1007/978-3-031-14714-2_1</a>.","bibtex":"@inproceedings{Prager_Seiler_Trautmann_Kerschke_2022, place={Cham}, title={Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods}, DOI={<a href=\"https://doi.org/10.1007/978-3-031-14714-2_1\">10.1007/978-3-031-14714-2_1</a>}, booktitle={Parallel Problem Solving from Nature — PPSN XVII}, publisher={Springer International Publishing}, author={Prager, Raphael Patrick and Seiler, Moritz and Trautmann, Heike and Kerschke, Pascal}, editor={Rudolph, Günter and Kononova, Anna V. and Aguirre, Hernán and Kerschke, Pascal and Ochoa, Gabriela and Tušar, Tea}, year={2022}, pages={3–17} }","ama":"Prager RP, Seiler M, Trautmann H, Kerschke P. Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods. In: Rudolph G, Kononova AV, Aguirre H, Kerschke P, Ochoa G, Tušar T, eds. <i>Parallel Problem Solving from Nature — PPSN XVII</i>. Springer International Publishing; 2022:3–17. doi:<a href=\"https://doi.org/10.1007/978-3-031-14714-2_1\">10.1007/978-3-031-14714-2_1</a>","ieee":"R. P. Prager, M. Seiler, H. Trautmann, and P. Kerschke, “Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods,” in <i>Parallel Problem Solving from Nature — PPSN XVII</i>, 2022, pp. 3–17, doi: <a href=\"https://doi.org/10.1007/978-3-031-14714-2_1\">10.1007/978-3-031-14714-2_1</a>.","apa":"Prager, R. P., Seiler, M., Trautmann, H., &#38; Kerschke, P. (2022). Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods. In G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, &#38; T. Tušar (Eds.), <i>Parallel Problem Solving from Nature — PPSN XVII</i> (pp. 3–17). Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-031-14714-2_1\">https://doi.org/10.1007/978-3-031-14714-2_1</a>","chicago":"Prager, Raphael Patrick, Moritz Seiler, Heike Trautmann, and Pascal Kerschke. “Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods.” 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 Tušar, 3–17. Cham: Springer International Publishing, 2022. <a href=\"https://doi.org/10.1007/978-3-031-14714-2_1\">https://doi.org/10.1007/978-3-031-14714-2_1</a>.","short":"R.P. Prager, M. Seiler, H. Trautmann, P. Kerschke, in: G. Rudolph, A.V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, T. Tušar (Eds.), Parallel Problem Solving from Nature — PPSN XVII, Springer International Publishing, Cham, 2022, pp. 3–17."},"place":"Cham"},{"abstract":[{"lang":"eng","text":"Social media platforms are essential for information sharing and, thus, prone to coordinated dis- and misinformation campaigns. Nevertheless, research in this area is hampered by strict data sharing regulations imposed by the platforms, resulting in a lack of benchmark data. Previous work focused on circumventing these rules by either pseudonymizing the data or sharing fragments. In this work, we will address the benchmarking crisis by presenting a methodology that can be used to create artificial campaigns out of original campaign building blocks. We conduct a proof-of-concept study using the freely available generative language model GPT-Neo in this context and demonstrate that the campaign patterns can flexibly be adapted to an underlying social media stream and evade state-of-the-art campaign detection approaches based on stream clustering. Thus, we not only provide a framework for artificial benchmark generation but also demonstrate the possible adversarial nature of such benchmarks for challenging and advancing current campaign detection methods."}],"citation":{"ieee":"J. S. Pohl, D. Assenmacher, M. Seiler, H. Trautmann, and C. Grimme, “Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches,” in <i>Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM)</i>, 2022, pp. 1–10, doi: <a href=\"https://doi.org/10.36190/2022.91\">10.36190/2022.91</a>.","apa":"Pohl, J. S., Assenmacher, D., Seiler, M., Trautmann, H., &#38; Grimme, C. (2022). Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches. In  for the Advancement of Artificial Intelligence (AAAI) Association (Ed.), <i>Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM)</i> (pp. 1–10). AAAI Press. <a href=\"https://doi.org/10.36190/2022.91\">https://doi.org/10.36190/2022.91</a>","chicago":"Pohl, Janina Susanne, Dennis Assenmacher, Moritz Seiler, Heike Trautmann, and Christian Grimme. “Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches.” In <i>Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM)</i>, edited by for the Advancement of Artificial Intelligence (AAAI) Association, 1–10. Palo Alto, CA, USA: AAAI Press, 2022. <a href=\"https://doi.org/10.36190/2022.91\">https://doi.org/10.36190/2022.91</a>.","short":"J.S. Pohl, D. Assenmacher, M. Seiler, H. Trautmann, C. Grimme, in:  for the Advancement of Artificial Intelligence (AAAI) Association (Ed.), Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM), AAAI Press, Palo Alto, CA, USA, 2022, pp. 1–10.","mla":"Pohl, Janina Susanne, et al. “Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches.” <i>Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM)</i>, edited by for the Advancement of Artificial Intelligence (AAAI) Association, AAAI Press, 2022, pp. 1–10, doi:<a href=\"https://doi.org/10.36190/2022.91\">10.36190/2022.91</a>.","bibtex":"@inproceedings{Pohl_Assenmacher_Seiler_Trautmann_Grimme_2022, place={Palo Alto, CA, USA}, title={Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches}, DOI={<a href=\"https://doi.org/10.36190/2022.91\">10.36190/2022.91</a>}, booktitle={Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM)}, publisher={AAAI Press}, author={Pohl, Janina Susanne and Assenmacher, Dennis and Seiler, Moritz and Trautmann, Heike and Grimme, Christian}, editor={the Advancement of Artificial Intelligence (AAAI) Association, for}, year={2022}, pages={1–10} }","ama":"Pohl JS, Assenmacher D, Seiler M, Trautmann H, Grimme C. Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches. In: the Advancement of Artificial Intelligence (AAAI) Association  for, ed. <i>Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM)</i>. AAAI Press; 2022:1–10. doi:<a href=\"https://doi.org/10.36190/2022.91\">10.36190/2022.91</a>"},"publication":"Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM)","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","place":"Palo Alto, CA, USA","date_created":"2023-08-04T07:11:34Z","date_updated":"2024-06-07T07:13:35Z","author":[{"last_name":"Pohl","first_name":"Janina Susanne","full_name":"Pohl, Janina Susanne"},{"last_name":"Assenmacher","first_name":"Dennis","full_name":"Assenmacher, Dennis"},{"id":"105520","last_name":"Seiler","first_name":"Moritz","full_name":"Seiler, Moritz"},{"id":"100740","full_name":"Trautmann, Heike","orcid":"0000-0002-9788-8282","first_name":"Heike","last_name":"Trautmann"},{"full_name":"Grimme, Christian","first_name":"Christian","last_name":"Grimme"}],"status":"public","year":"2022","title":"Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches","editor":[{"full_name":"the Advancement of Artificial Intelligence (AAAI) Association, for","last_name":"the Advancement of Artificial Intelligence (AAAI) Association","first_name":"for"}],"doi":"10.36190/2022.91","user_id":"15504","_id":"46303","language":[{"iso":"eng"}],"publisher":"AAAI Press","page":"1–10"},{"abstract":[{"lang":"eng","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—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."}],"citation":{"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(8):1–44. 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={8}, journal={Applied Sciences}, author={Clever, Lena and Pohl, Janina Susanne and Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}, year={2022}, pages={1–44} }","mla":"Clever, Lena, et al. “Process-Oriented Stream Classification Pipeline: A Literature Review.” <i>Applied Sciences</i>, vol. 12, no. 8, 2022, pp. 1–44, doi:<a href=\"https://doi.org/10.3390/app12189094\">10.3390/app12189094</a>.","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. 8 (2022): 1–44. <a href=\"https://doi.org/10.3390/app12189094\">https://doi.org/10.3390/app12189094</a>.","short":"L. Clever, J.S. Pohl, J. Bossek, P. Kerschke, H. Trautmann, Applied Sciences 12 (2022) 1–44.","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>(8), 1–44. <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. 8, pp. 1–44, 2022, doi: <a href=\"https://doi.org/10.3390/app12189094\">10.3390/app12189094</a>."},"publication":"Applied Sciences","issue":"8","department":[{"_id":"34"},{"_id":"819"}],"type":"journal_article","date_created":"2023-08-04T07:17:23Z","intvolume":"        12","date_updated":"2024-06-10T12:02:17Z","author":[{"full_name":"Clever, Lena","last_name":"Clever","first_name":"Lena"},{"full_name":"Pohl, Janina Susanne","first_name":"Janina Susanne","last_name":"Pohl"},{"full_name":"Bossek, Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob","id":"102979"},{"full_name":"Kerschke, Pascal","first_name":"Pascal","last_name":"Kerschke"},{"first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike","id":"100740"}],"year":"2022","title":"Process-Oriented Stream Classification Pipeline: A Literature Review","status":"public","volume":12,"user_id":"15504","doi":"10.3390/app12189094","_id":"46309","language":[{"iso":"eng"}],"page":"1–44"},{"publication":"Parallel Problem Solving from Nature — PPSN XVII","citation":{"chicago":"Heins, J, J Rook, L Schäpermeier, P 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 G Rudolph, AV Kononova, H Aguirre, P Kerschke, G Ochoa, and T Tušar, 192–206. Cham: Springer International Publishing, 2022.","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 A, Aguirre H, Kerschke P, Ochoa G, Tušar T, eds. <i>Parallel Problem Solving from Nature — PPSN XVII</i>. Springer International Publishing; 2022:192–206.","short":"J. Heins, J. Rook, L. Schäpermeier, P. Kerschke, J. Bossek, H. Trautmann, in: G. Rudolph, A. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, T. Tušar (Eds.), Parallel Problem Solving from Nature — PPSN XVII, Springer International Publishing, Cham, 2022, pp. 192–206.","bibtex":"@inproceedings{Heins_Rook_Schäpermeier_Kerschke_Bossek_Trautmann_2022, place={Cham}, title={BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems}, booktitle={Parallel Problem Solving from Nature — PPSN XVII}, publisher={Springer International Publishing}, author={Heins, J and Rook, J and Schäpermeier, L and Kerschke, P and Bossek, Jakob and Trautmann, Heike}, editor={Rudolph, G and Kononova, AV and Aguirre, H and Kerschke, P and Ochoa, G and Tušar, T}, year={2022}, pages={192–206} }","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. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, &#38; T. Tušar (Eds.), <i>Parallel Problem Solving from Nature — PPSN XVII</i> (pp. 192–206). Springer International Publishing.","mla":"Heins, J., et al. “BBE: Basin-Based Evaluation of Multimodal Multi-Objective Optimization Problems.” <i>Parallel Problem Solving from Nature — PPSN XVII</i>, edited by G Rudolph et al., Springer International Publishing, 2022, pp. 192–206.","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."},"type":"conference","department":[{"_id":"34"},{"_id":"819"}],"place":"Cham","date_created":"2023-08-04T07:10:52Z","date_updated":"2024-06-10T12:02:35Z","status":"public","title":"BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems","year":"2022","author":[{"full_name":"Heins, J","first_name":"J","last_name":"Heins"},{"last_name":"Rook","first_name":"J","full_name":"Rook, J"},{"full_name":"Schäpermeier, L","first_name":"L","last_name":"Schäpermeier"},{"first_name":"P","last_name":"Kerschke","full_name":"Kerschke, P"},{"first_name":"Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","full_name":"Bossek, Jakob","id":"102979"},{"id":"100740","last_name":"Trautmann","first_name":"Heike","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike"}],"publication_identifier":{"isbn":["978-3-031-14714-2"]},"user_id":"15504","editor":[{"first_name":"G","last_name":"Rudolph","full_name":"Rudolph, G"},{"full_name":"Kononova, AV","first_name":"AV","last_name":"Kononova"},{"full_name":"Aguirre, H","last_name":"Aguirre","first_name":"H"},{"full_name":"Kerschke, P","first_name":"P","last_name":"Kerschke"},{"first_name":"G","last_name":"Ochoa","full_name":"Ochoa, G"},{"full_name":"Tušar, T","last_name":"Tušar","first_name":"T"}],"page":"192–206","language":[{"iso":"eng"}],"_id":"46302","publisher":"Springer International Publishing"},{"publication_identifier":{"isbn":["9781450392686"]},"author":[{"last_name":"Rook","first_name":"J","full_name":"Rook, J"},{"orcid":"0000-0002-9788-8282","first_name":"Heike","last_name":"Trautmann","full_name":"Trautmann, Heike","id":"100740"},{"id":"102979","full_name":"Bossek, Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob"},{"full_name":"Grimme, C","last_name":"Grimme","first_name":"C"}],"title":"On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems","year":"2022","date_updated":"2026-02-19T15:12:35Z","series_title":"GECCO ’22","language":[{"iso":"eng"}],"doi":"10.1145/3520304.3528998","publication":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","abstract":[{"text":"Hardness of Multi-Objective (MO) continuous optimization problems results from an interplay of various problem characteristics, e. g. the degree of multi-modality. We present a benchmark study of classical and diversity focused optimizers on multi-modal MO problems based on automated algorithm configuration. We show the large effect of the latter and investigate the trade-off between convergence in objective space and diversity in decision space.","lang":"eng"}],"date_created":"2023-08-04T07:14:24Z","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","status":"public","_id":"46305","publisher":"Association for Computing Machinery","page":"356–359","editor":[{"full_name":"Fieldsend, J","last_name":"Fieldsend","first_name":"J"},{"last_name":"Wagner","first_name":"M.","full_name":"Wagner, M."}],"user_id":"14972","citation":{"ieee":"J. Rook, H. Trautmann, J. Bossek, and C. Grimme, “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>, 2022, pp. 356–359, doi: <a href=\"https://doi.org/10.1145/3520304.3528998\">10.1145/3520304.3528998</a>.","apa":"Rook, J., Trautmann, H., Bossek, J., &#38; Grimme, C. (2022). On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems. In J. Fieldsend &#38; M. Wagner (Eds.), <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i> (pp. 356–359). Association for Computing Machinery. <a href=\"https://doi.org/10.1145/3520304.3528998\">https://doi.org/10.1145/3520304.3528998</a>","mla":"Rook, J., 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>, edited by J Fieldsend and M. Wagner, Association for Computing Machinery, 2022, pp. 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, J and Trautmann, Heike and Bossek, Jakob and Grimme, C}, editor={Fieldsend, J and Wagner, M.}, year={2022}, pages={356–359}, collection={GECCO ’22} }","chicago":"Rook, J, Heike Trautmann, Jakob Bossek, and C Grimme. “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>, edited by J Fieldsend and M. Wagner, 356–359. GECCO ’22. New York, NY, USA: Association for Computing Machinery, 2022. <a href=\"https://doi.org/10.1145/3520304.3528998\">https://doi.org/10.1145/3520304.3528998</a>.","short":"J. Rook, H. Trautmann, J. Bossek, C. Grimme, in: J. Fieldsend, M. Wagner (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, New York, NY, USA, 2022, pp. 356–359.","ama":"Rook J, Trautmann H, Bossek J, Grimme C. On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems. In: Fieldsend J, Wagner M, eds. <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>"},"place":"New York, NY, USA"},{"publication":"Computers & Operations Research","citation":{"mla":"Grimme, Christian, et al. “Peeking beyond Peaks: Challenges and Research Potentials of Continuous Multimodal Multi-Objective Optimization.” <i>Computers &#38; Operations Research</i>, vol. 136, 2021, p. 105489, doi:<a href=\"https://doi.org/10.1016/j.cor.2021.105489\">https://doi.org/10.1016/j.cor.2021.105489</a>.","bibtex":"@article{Grimme_Kerschke_Aspar_Trautmann_Preuss_Deutz_Wang_Emmerich_2021, title={Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization}, volume={136}, DOI={<a href=\"https://doi.org/10.1016/j.cor.2021.105489\">https://doi.org/10.1016/j.cor.2021.105489</a>}, journal={Computers &#38; Operations Research}, author={Grimme, Christian and Kerschke, Pascal and Aspar, Pelin and Trautmann, Heike and Preuss, Mike and Deutz, André H. and Wang, Hao and Emmerich, Michael}, year={2021}, pages={105489} }","ama":"Grimme C, Kerschke P, Aspar P, et al. Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization. <i>Computers &#38; Operations Research</i>. 2021;136:105489. doi:<a href=\"https://doi.org/10.1016/j.cor.2021.105489\">https://doi.org/10.1016/j.cor.2021.105489</a>","ieee":"C. Grimme <i>et al.</i>, “Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization,” <i>Computers &#38; Operations Research</i>, vol. 136, p. 105489, 2021, doi: <a href=\"https://doi.org/10.1016/j.cor.2021.105489\">https://doi.org/10.1016/j.cor.2021.105489</a>.","apa":"Grimme, C., Kerschke, P., Aspar, P., Trautmann, H., Preuss, M., Deutz, A. H., Wang, H., &#38; Emmerich, M. (2021). Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization. <i>Computers &#38; Operations Research</i>, <i>136</i>, 105489. <a href=\"https://doi.org/10.1016/j.cor.2021.105489\">https://doi.org/10.1016/j.cor.2021.105489</a>","chicago":"Grimme, Christian, Pascal Kerschke, Pelin Aspar, Heike Trautmann, Mike Preuss, André H. Deutz, Hao Wang, and Michael Emmerich. “Peeking beyond Peaks: Challenges and Research Potentials of Continuous Multimodal Multi-Objective Optimization.” <i>Computers &#38; Operations Research</i> 136 (2021): 105489. <a href=\"https://doi.org/10.1016/j.cor.2021.105489\">https://doi.org/10.1016/j.cor.2021.105489</a>.","short":"C. Grimme, P. Kerschke, P. Aspar, H. Trautmann, M. Preuss, A.H. Deutz, H. Wang, M. Emmerich, Computers &#38; Operations Research 136 (2021) 105489."},"abstract":[{"text":"Multi-objective (MO) optimization, i.e., the simultaneous optimization of multiple conflicting objectives, is gaining more and more attention in various research areas, such as evolutionary computation, machine learning (e.g., (hyper-)parameter optimization), or logistics (e.g., vehicle routing). Many works in this domain mention the structural problem property of multimodality as a challenge from two classical perspectives: (1) finding all globally optimal solution sets, and (2) avoiding to get trapped in local optima. Interestingly, these streams seem to transfer many traditional concepts of single-objective (SO) optimization into claims, assumptions, or even terminology regarding the MO domain, but mostly neglect the understanding of the structural properties as well as the algorithmic search behavior on a problem’s landscape. However, some recent works counteract this trend, by investigating the fundamentals and characteristics of MO problems using new visualization techniques and gaining surprising insights. Using these visual insights, this work proposes a step towards a unified terminology to capture multimodality and locality in a broader way than it is usually done. This enables us to investigate current research activities in multimodal continuous MO optimization and to highlight new implications and promising research directions for the design of benchmark suites, the discovery of MO landscape features, the development of new MO (or even SO) optimization algorithms, and performance indicators. For all these topics, we provide a review of ideas and methods but also an outlook on future challenges, research potential and perspectives that result from recent developments.","lang":"eng"}],"date_created":"2023-08-04T07:28:34Z","keyword":["Multimodal optimization","Multi-objective continuous optimization","Landscape analysis","Visualization","Benchmarking","Theory","Algorithms"],"type":"journal_article","department":[{"_id":"34"},{"_id":"819"}],"year":"2021","status":"public","title":"Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization","publication_identifier":{"issn":["0305-0548"]},"author":[{"first_name":"Christian","last_name":"Grimme","full_name":"Grimme, Christian"},{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"},{"first_name":"Pelin","last_name":"Aspar","full_name":"Aspar, Pelin"},{"full_name":"Trautmann, Heike","first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","id":"100740"},{"full_name":"Preuss, Mike","last_name":"Preuss","first_name":"Mike"},{"full_name":"Deutz, André H.","first_name":"André H.","last_name":"Deutz"},{"full_name":"Wang, Hao","first_name":"Hao","last_name":"Wang"},{"full_name":"Emmerich, Michael","first_name":"Michael","last_name":"Emmerich"}],"date_updated":"2023-10-16T12:58:42Z","intvolume":"       136","page":"105489","language":[{"iso":"eng"}],"_id":"46318","user_id":"15504","doi":"https://doi.org/10.1016/j.cor.2021.105489","volume":136},{"place":"Heidelberg, Berlin","date_created":"2023-08-04T07:21:17Z","type":"conference","department":[{"_id":"34"},{"_id":"819"}],"publication":"Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings","citation":{"mla":"Aspar, Pelin, et al. “Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective Space by Means of Multiobjectivization.” <i>Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings</i>, edited by H. et al. Ishibuchi, Springer, 2021, pp. 311–322, doi:<a href=\"https://doi.org/10.1007/978-3-030-72062-9_25\">10.1007/978-3-030-72062-9_25</a>.","bibtex":"@inproceedings{Aspar_Kerschke_Steinhoff_Trautmann_Grimme_2021, place={Heidelberg, Berlin}, title={Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective Space by Means of Multiobjectivization}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-72062-9_25\">10.1007/978-3-030-72062-9_25</a>}, booktitle={Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings}, publisher={Springer}, author={Aspar, Pelin and Kerschke, Pascal and Steinhoff, Vera and Trautmann, Heike and Grimme, Christian}, editor={et al. Ishibuchi, H.}, year={2021}, pages={311–322} }","ama":"Aspar P, Kerschke P, Steinhoff V, Trautmann H, Grimme C. Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective Space by Means of Multiobjectivization. In: et al. Ishibuchi H, ed. <i>Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings</i>. Springer; 2021:311–322. doi:<a href=\"https://doi.org/10.1007/978-3-030-72062-9_25\">10.1007/978-3-030-72062-9_25</a>","ieee":"P. Aspar, P. Kerschke, V. Steinhoff, H. Trautmann, and C. Grimme, “Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective Space by Means of Multiobjectivization,” in <i>Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings</i>, 2021, pp. 311–322, doi: <a href=\"https://doi.org/10.1007/978-3-030-72062-9_25\">10.1007/978-3-030-72062-9_25</a>.","apa":"Aspar, P., Kerschke, P., Steinhoff, V., Trautmann, H., &#38; Grimme, C. (2021). Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective Space by Means of Multiobjectivization. In H. et al. Ishibuchi (Ed.), <i>Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings</i> (pp. 311–322). Springer. <a href=\"https://doi.org/10.1007/978-3-030-72062-9_25\">https://doi.org/10.1007/978-3-030-72062-9_25</a>","short":"P. Aspar, P. Kerschke, V. Steinhoff, H. Trautmann, C. Grimme, in: H. et al. Ishibuchi (Ed.), Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings, Springer, Heidelberg, Berlin, 2021, pp. 311–322.","chicago":"Aspar, Pelin, Pascal Kerschke, Vera Steinhoff, Heike Trautmann, and Christian Grimme. “Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective Space by Means of Multiobjectivization.” In <i>Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings</i>, edited by H. et al. Ishibuchi, 311–322. Heidelberg, Berlin: Springer, 2021. <a href=\"https://doi.org/10.1007/978-3-030-72062-9_25\">https://doi.org/10.1007/978-3-030-72062-9_25</a>."},"abstract":[{"text":"In this work we examine the inner mechanisms of the recently developed sophisticated local search procedure SOMOGSA. This method solves multimodal single-objective continuous optimization problems by first expanding the problem with an additional objective (e.g., a sphere function) to the bi-objective space, and subsequently exploiting local structures and ridges of the resulting landscapes. Our study particularly focusses on the sensitivity of this multiobjectivization approach w.r.t. (i) the parametrization of the artificial second objective, as well as (ii) the position of the initial starting points in the search space.\r\n\r\nAs SOMOGSA is a modular framework for encapsulating local search, we integrate Gradient and Nelder-Mead local search (as optimizers in the respective module) and compare the performance of the resulting hybrid local search to their original single-objective counterparts. We show that the SOMOGSA framework can significantly boost local search by multiobjectivization. Combined with more sophisticated local search and metaheuristics this may help in solving highly multimodal optimization problems in future.","lang":"eng"}],"page":"311–322","_id":"46311","language":[{"iso":"eng"}],"publisher":"Springer","doi":"10.1007/978-3-030-72062-9_25","user_id":"15504","editor":[{"first_name":"H.","last_name":"et al. Ishibuchi","full_name":"et al. Ishibuchi, H."}],"status":"public","year":"2021","title":"Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective Space by Means of Multiobjectivization","author":[{"full_name":"Aspar, Pelin","last_name":"Aspar","first_name":"Pelin"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"full_name":"Steinhoff, Vera","first_name":"Vera","last_name":"Steinhoff"},{"id":"100740","first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike"},{"full_name":"Grimme, Christian","first_name":"Christian","last_name":"Grimme"}],"date_updated":"2023-10-16T12:54:29Z"},{"doi":"10.1016/j.ijinfomgt.2021.102311","user_id":"15504","volume":58,"language":[{"iso":"eng"}],"_id":"46317","date_updated":"2023-10-16T12:58:02Z","intvolume":"        58","title":"What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective","year":"2021","status":"public","author":[{"last_name":"Coombs","first_name":"Crispin","full_name":"Coombs, Crispin"},{"first_name":"Patrick","last_name":"Stacey","full_name":"Stacey, Patrick"},{"full_name":"Kawalek, Peter","first_name":"Peter","last_name":"Kawalek"},{"first_name":"Boyka","last_name":"Simeonova","full_name":"Simeonova, Boyka"},{"last_name":"Becker","first_name":"Jörg","full_name":"Becker, Jörg"},{"first_name":"Katrin","last_name":"Bergener","full_name":"Bergener, Katrin"},{"last_name":"Carvalho","first_name":"João Álvaro","full_name":"Carvalho, João Álvaro"},{"full_name":"Fantinato, Marcelo","last_name":"Fantinato","first_name":"Marcelo"},{"full_name":"Garmann-Johnsen, Niels F.","first_name":"Niels F.","last_name":"Garmann-Johnsen"},{"full_name":"Grimme, Christian","first_name":"Christian","last_name":"Grimme"},{"full_name":"Stein, Armin","first_name":"Armin","last_name":"Stein"},{"id":"100740","full_name":"Trautmann, Heike","first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann"}],"type":"journal_article","department":[{"_id":"34"},{"_id":"819"}],"date_created":"2023-08-04T07:27:14Z","abstract":[{"text":"One of the most significant recent technological developments concerns the development and implementation of ‘intelligent machines’ that draw on recent advances in artificial intelligence (AI) and robotics. However, there are growing tensions between human freedoms and machine controls. This article reports the findings of a workshop that investigated the application of the principles of human freedom throughout intelligent machine development and use. Forty IS researchers from ten different countries discussed four contemporary AI and humanity issues and the most relevant IS domain challenges. This article summarizes their experiences and opinions regarding four AI and humanity themes: Crime & conflict, Jobs, Attention, and Wellbeing. The outcomes of the workshop discussions identify three attributes of humanity that need preservation: a critique of the design and application of AI, and the intelligent machines it can create; human involvement in the loop of intelligent machine decision-making processes; and the ability to interpret and explain intelligent machine decision-making processes. The article provides an agenda for future AI and humanity research.","lang":"eng"}],"publication":"International Journal of Information Management","citation":{"ieee":"C. Coombs <i>et al.</i>, “What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective,” <i>International Journal of Information Management</i>, vol. 58, 2021, doi: <a href=\"https://doi.org/10.1016/j.ijinfomgt.2021.102311\">10.1016/j.ijinfomgt.2021.102311</a>.","apa":"Coombs, C., Stacey, P., Kawalek, P., Simeonova, B., Becker, J., Bergener, K., Carvalho, J. Á., Fantinato, M., Garmann-Johnsen, N. F., Grimme, C., Stein, A., &#38; Trautmann, H. (2021). What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective. <i>International Journal of Information Management</i>, <i>58</i>. <a href=\"https://doi.org/10.1016/j.ijinfomgt.2021.102311\">https://doi.org/10.1016/j.ijinfomgt.2021.102311</a>","chicago":"Coombs, Crispin, Patrick Stacey, Peter Kawalek, Boyka Simeonova, Jörg Becker, Katrin Bergener, João Álvaro Carvalho, et al. “What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective.” <i>International Journal of Information Management</i> 58 (2021). <a href=\"https://doi.org/10.1016/j.ijinfomgt.2021.102311\">https://doi.org/10.1016/j.ijinfomgt.2021.102311</a>.","short":"C. Coombs, P. Stacey, P. Kawalek, B. Simeonova, J. Becker, K. Bergener, J.Á. Carvalho, M. Fantinato, N.F. Garmann-Johnsen, C. Grimme, A. Stein, H. Trautmann, International Journal of Information Management 58 (2021).","mla":"Coombs, Crispin, et al. “What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective.” <i>International Journal of Information Management</i>, vol. 58, 2021, doi:<a href=\"https://doi.org/10.1016/j.ijinfomgt.2021.102311\">10.1016/j.ijinfomgt.2021.102311</a>.","bibtex":"@article{Coombs_Stacey_Kawalek_Simeonova_Becker_Bergener_Carvalho_Fantinato_Garmann-Johnsen_Grimme_et al._2021, title={What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective}, volume={58}, DOI={<a href=\"https://doi.org/10.1016/j.ijinfomgt.2021.102311\">10.1016/j.ijinfomgt.2021.102311</a>}, journal={International Journal of Information Management}, author={Coombs, Crispin and Stacey, Patrick and Kawalek, Peter and Simeonova, Boyka and Becker, Jörg and Bergener, Katrin and Carvalho, João Álvaro and Fantinato, Marcelo and Garmann-Johnsen, Niels F. and Grimme, Christian and et al.}, year={2021} }","ama":"Coombs C, Stacey P, Kawalek P, et al. What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective. <i>International Journal of Information Management</i>. 2021;58. doi:<a href=\"https://doi.org/10.1016/j.ijinfomgt.2021.102311\">10.1016/j.ijinfomgt.2021.102311</a>"}},{"status":"public","page":"556–564","publisher":"Association for Computing Machinery","_id":"48853","user_id":"102979","citation":{"bibtex":"@inproceedings{Bossek_Neumann_Neumann_2021, place={New York, NY, USA}, series={GECCO ’21}, title={Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms}, DOI={<a href=\"https://doi.org/10.1145/3449639.3459364\">10.1145/3449639.3459364</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Neumann, Aneta and Neumann, Frank}, year={2021}, pages={556–564}, collection={GECCO ’21} }","ama":"Bossek J, Neumann A, Neumann F. Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO ’21. Association for Computing Machinery; 2021:556–564. doi:<a href=\"https://doi.org/10.1145/3449639.3459364\">10.1145/3449639.3459364</a>","mla":"Bossek, Jakob, et al. “Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2021, pp. 556–564, doi:<a href=\"https://doi.org/10.1145/3449639.3459364\">10.1145/3449639.3459364</a>.","chicago":"Bossek, Jakob, Aneta Neumann, and Frank Neumann. “Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 556–564. GECCO ’21. New York, NY, USA: Association for Computing Machinery, 2021. <a href=\"https://doi.org/10.1145/3449639.3459364\">https://doi.org/10.1145/3449639.3459364</a>.","short":"J. Bossek, A. Neumann, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2021, pp. 556–564.","ieee":"J. Bossek, A. Neumann, and F. Neumann, “Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2021, pp. 556–564, doi: <a href=\"https://doi.org/10.1145/3449639.3459364\">10.1145/3449639.3459364</a>.","apa":"Bossek, J., Neumann, A., &#38; Neumann, F. (2021). Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 556–564. <a href=\"https://doi.org/10.1145/3449639.3459364\">https://doi.org/10.1145/3449639.3459364</a>"},"place":"New York, NY, USA","year":"2021","title":"Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms","publication_identifier":{"isbn":["978-1-4503-8350-9"]},"author":[{"id":"102979","full_name":"Bossek, Jakob","first_name":"Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668"},{"last_name":"Neumann","first_name":"Aneta","full_name":"Neumann, Aneta"},{"full_name":"Neumann, Frank","last_name":"Neumann","first_name":"Frank"}],"date_updated":"2023-12-13T10:45:22Z","publication_status":"published","language":[{"iso":"eng"}],"series_title":"GECCO ’21","doi":"10.1145/3449639.3459364","publication":"Proceedings of the Genetic and Evolutionary Computation Conference","abstract":[{"lang":"eng","text":"In practise, it is often desirable to provide the decision-maker with a rich set of diverse solutions of decent quality instead of just a single solution. In this paper we study evolutionary diversity optimization for the knapsack problem (KP). Our goal is to evolve a population of solutions that all have a profit of at least (1 - {$ϵ$}) {$\\cdot$} OPT, where OPT is the value of an optimal solution. Furthermore, they should differ in structure with respect to an entropy-based diversity measure. To this end we propose a simple ({$\\mu$} + 1)-EA with initial approximate solutions calculated by a well-known FPTAS for the KP. We investigate the effect of different standard mutation operators and introduce biased mutation and crossover which puts strong probability on flipping bits of low and/or high frequency within the population. An experimental study on different instances and settings shows that the proposed mutation operators in most cases perform slightly inferior in the long term, but show strong benefits if the number of function evaluations is severely limited."}],"extern":"1","date_created":"2023-11-14T15:58:54Z","type":"conference","keyword":["evolutionary algorithms","evolutionary diversity optimization","knapsack problem","tailored operators"],"department":[{"_id":"819"}]},{"publication_status":"published","date_updated":"2023-12-13T10:45:14Z","author":[{"orcid":"0000-0002-4121-4668","first_name":"Jakob","last_name":"Bossek","full_name":"Bossek, Jakob","id":"102979"},{"full_name":"Neumann, Aneta","first_name":"Aneta","last_name":"Neumann"},{"first_name":"Frank","last_name":"Neumann","full_name":"Neumann, Frank"}],"publication_identifier":{"isbn":["978-3-030-92120-0"]},"title":"Exact Counting and~Sampling of Optima for the Knapsack Problem","year":"2021","doi":"10.1007/978-3-030-92121-7_4","language":[{"iso":"eng"}],"extern":"1","abstract":[{"text":"Computing sets of high quality solutions has gained increasing interest in recent years. In this paper, we investigate how to obtain sets of optimal solutions for the classical knapsack problem. We present an algorithm to count exactly the number of optima to a zero-one knapsack problem instance. In addition, we show how to efficiently sample uniformly at random from the set of all global optima. In our experimental study, we investigate how the number of optima develops for classical random benchmark instances dependent on their generator parameters. We find that the number of global optima can increase exponentially for practically relevant classes of instances with correlated weights and profits which poses a justification for the considered exact counting problem.","lang":"eng"}],"publication":"Learning and Intelligent Optimization","department":[{"_id":"819"}],"type":"conference","keyword":["Dynamic programming","Exact counting","Sampling","Zero-one knapsack problem"],"date_created":"2023-11-14T15:58:54Z","status":"public","user_id":"102979","publisher":"Springer-Verlag","_id":"48855","page":"40–54","citation":{"short":"J. Bossek, A. Neumann, F. Neumann, in: Learning and Intelligent Optimization, Springer-Verlag, Berlin, Heidelberg, 2021, pp. 40–54.","chicago":"Bossek, Jakob, Aneta Neumann, and Frank Neumann. “Exact Counting And~Sampling of Optima for the Knapsack Problem.” In <i>Learning and Intelligent Optimization</i>, 40–54. Berlin, Heidelberg: Springer-Verlag, 2021. <a href=\"https://doi.org/10.1007/978-3-030-92121-7_4\">https://doi.org/10.1007/978-3-030-92121-7_4</a>.","apa":"Bossek, J., Neumann, A., &#38; Neumann, F. (2021). Exact Counting and~Sampling of Optima for the Knapsack Problem. <i>Learning and Intelligent Optimization</i>, 40–54. <a href=\"https://doi.org/10.1007/978-3-030-92121-7_4\">https://doi.org/10.1007/978-3-030-92121-7_4</a>","ieee":"J. Bossek, A. Neumann, and F. Neumann, “Exact Counting and~Sampling of Optima for the Knapsack Problem,” in <i>Learning and Intelligent Optimization</i>, 2021, pp. 40–54, doi: <a href=\"https://doi.org/10.1007/978-3-030-92121-7_4\">10.1007/978-3-030-92121-7_4</a>.","ama":"Bossek J, Neumann A, Neumann F. Exact Counting and~Sampling of Optima for the Knapsack Problem. In: <i>Learning and Intelligent Optimization</i>. Springer-Verlag; 2021:40–54. doi:<a href=\"https://doi.org/10.1007/978-3-030-92121-7_4\">10.1007/978-3-030-92121-7_4</a>","bibtex":"@inproceedings{Bossek_Neumann_Neumann_2021, place={Berlin, Heidelberg}, title={Exact Counting and~Sampling of Optima for the Knapsack Problem}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-92121-7_4\">10.1007/978-3-030-92121-7_4</a>}, booktitle={Learning and Intelligent Optimization}, publisher={Springer-Verlag}, author={Bossek, Jakob and Neumann, Aneta and Neumann, Frank}, year={2021}, pages={40–54} }","mla":"Bossek, Jakob, et al. “Exact Counting And~Sampling of Optima for the Knapsack Problem.” <i>Learning and Intelligent Optimization</i>, Springer-Verlag, 2021, pp. 40–54, doi:<a href=\"https://doi.org/10.1007/978-3-030-92121-7_4\">10.1007/978-3-030-92121-7_4</a>."},"place":"Berlin, Heidelberg"},{"publication":"Proceedings of the Genetic and Evolutionary Computation Conference","abstract":[{"lang":"eng","text":"In the area of evolutionary computation the calculation of diverse sets of high-quality solutions to a given optimization problem has gained momentum in recent years under the term evolutionary diversity optimization. Theoretical insights into the working principles of baseline evolutionary algorithms for diversity optimization are still rare. In this paper we study the well-known Minimum Spanning Tree problem (MST) in the context of diversity optimization where population diversity is measured by the sum of pairwise edge overlaps. Theoretical results provide insights into the fitness landscape of the MST diversity optimization problem pointing out that even for a population of {$\\mu$} = 2 fitness plateaus (of constant length) can be reached, but nevertheless diverse sets can be calculated in polynomial time. We supplement our theoretical results with a series of experiments for the unconstrained and constraint case where all solutions need to fulfill a minimal quality threshold. Our results show that a simple ({$\\mu$} + 1)-EA can effectively compute a diversified population of spanning trees of high quality."}],"extern":"1","date_created":"2023-11-14T15:58:55Z","department":[{"_id":"819"}],"type":"conference","keyword":["evolutionary algorithms","evolutionary diversity optimization","minimum spanning tree","runtime analysis"],"publication_identifier":{"isbn":["978-1-4503-8350-9"]},"author":[{"full_name":"Bossek, Jakob","first_name":"Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","id":"102979"},{"full_name":"Neumann, Frank","last_name":"Neumann","first_name":"Frank"}],"title":"Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem","year":"2021","date_updated":"2023-12-13T10:45:37Z","publication_status":"published","series_title":"GECCO ’21","language":[{"iso":"eng"}],"doi":"10.1145/3449639.3459363","citation":{"bibtex":"@inproceedings{Bossek_Neumann_2021, place={New York, NY, USA}, series={GECCO ’21}, title={Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem}, DOI={<a href=\"https://doi.org/10.1145/3449639.3459363\">10.1145/3449639.3459363</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Neumann, Frank}, year={2021}, pages={198–206}, collection={GECCO ’21} }","ama":"Bossek J, Neumann F. Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO ’21. Association for Computing Machinery; 2021:198–206. doi:<a href=\"https://doi.org/10.1145/3449639.3459363\">10.1145/3449639.3459363</a>","mla":"Bossek, Jakob, and Frank Neumann. “Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2021, pp. 198–206, doi:<a href=\"https://doi.org/10.1145/3449639.3459363\">10.1145/3449639.3459363</a>.","chicago":"Bossek, Jakob, and Frank Neumann. “Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 198–206. GECCO ’21. New York, NY, USA: Association for Computing Machinery, 2021. <a href=\"https://doi.org/10.1145/3449639.3459363\">https://doi.org/10.1145/3449639.3459363</a>.","short":"J. Bossek, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2021, pp. 198–206.","ieee":"J. Bossek and F. Neumann, “Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2021, pp. 198–206, doi: <a href=\"https://doi.org/10.1145/3449639.3459363\">10.1145/3449639.3459363</a>.","apa":"Bossek, J., &#38; Neumann, F. (2021). Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 198–206. <a href=\"https://doi.org/10.1145/3449639.3459363\">https://doi.org/10.1145/3449639.3459363</a>"},"place":"New York, NY, USA","status":"public","publisher":"Association for Computing Machinery","_id":"48860","page":"198–206","user_id":"102979"},{"title":"Do Additional Optima Speed up Evolutionary Algorithms?","year":"2021","author":[{"id":"102979","full_name":"Bossek, Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob"},{"full_name":"Sudholt, Dirk","first_name":"Dirk","last_name":"Sudholt"}],"publication_identifier":{"isbn":["978-1-4503-8352-3"]},"date_updated":"2023-12-13T10:45:31Z","publication_status":"published","language":[{"iso":"eng"}],"publication":"Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms","abstract":[{"lang":"eng","text":"Most runtime analyses of randomised search heuristics focus on the expected number of function evaluations to find a unique global optimum. We ask a fundamental question: if additional search points are declared optimal, or declared as desirable target points, do these additional optima speed up evolutionary algorithms? More formally, we analyse the expected hitting time of a target set OPT {$\\cup$} S where S is a set of non-optimal search points and OPT is the set of optima and compare it to the expected hitting time of OPT. We show that the answer to our question depends on the number and placement of search points in S. For all black-box algorithms and all fitness functions we show that, if additional optima are placed randomly, even an exponential number of optima has a negligible effect on the expected optimisation time. Considering Hamming balls around all global optima gives an easier target for some algorithms and functions and can shift the phase transition with respect to offspring population sizes in the (1,{$\\lambda$}) EA on One-Max. Finally, on functions where search trajectories typically join in a single search point, turning one search point into an optimum drastically reduces the expected optimisation time."}],"extern":"1","date_created":"2023-11-14T15:58:55Z","keyword":["evolutionary algorithms","pseudo-boolean functions","runtime analysis","theory"],"type":"book_chapter","department":[{"_id":"819"}],"status":"public","page":"1–11","_id":"48862","publisher":"Association for Computing Machinery","user_id":"102979","citation":{"apa":"Bossek, J., &#38; Sudholt, D. (2021). Do Additional Optima Speed up Evolutionary Algorithms? In <i>Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i> (pp. 1–11). Association for Computing Machinery.","ieee":"J. Bossek and D. Sudholt, “Do Additional Optima Speed up Evolutionary Algorithms?,” in <i>Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, New York, NY, USA: Association for Computing Machinery, 2021, pp. 1–11.","short":"J. Bossek, D. Sudholt, in: Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, Association for Computing Machinery, New York, NY, USA, 2021, pp. 1–11.","chicago":"Bossek, Jakob, and Dirk Sudholt. “Do Additional Optima Speed up Evolutionary Algorithms?” In <i>Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, 1–11. New York, NY, USA: Association for Computing Machinery, 2021.","mla":"Bossek, Jakob, and Dirk Sudholt. “Do Additional Optima Speed up Evolutionary Algorithms?” <i>Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, Association for Computing Machinery, 2021, pp. 1–11.","ama":"Bossek J, Sudholt D. Do Additional Optima Speed up Evolutionary Algorithms? In: <i>Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>. Association for Computing Machinery; 2021:1–11.","bibtex":"@inbook{Bossek_Sudholt_2021, place={New York, NY, USA}, title={Do Additional Optima Speed up Evolutionary Algorithms?}, booktitle={Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Sudholt, Dirk}, year={2021}, pages={1–11} }"},"place":"New York, NY, USA"},{"date_updated":"2023-12-13T10:47:23Z","publication_identifier":{"isbn":["978-1-4503-8352-3"]},"author":[{"full_name":"Heins, Jonathan","last_name":"Heins","first_name":"Jonathan"},{"id":"102979","full_name":"Bossek, Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","first_name":"Jakob"},{"last_name":"Pohl","first_name":"Janina","full_name":"Pohl, Janina"},{"last_name":"Seiler","first_name":"Moritz","full_name":"Seiler, Moritz"},{"full_name":"Trautmann, Heike","first_name":"Heike","last_name":"Trautmann"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"}],"status":"public","title":"On the Potential of Normalized TSP Features for Automated Algorithm Selection","year":"2021","user_id":"102979","_id":"48881","publisher":"Association for Computing Machinery","language":[{"iso":"eng"}],"page":"1–15","abstract":[{"text":"Classic automated algorithm selection (AS) for (combinatorial) optimization problems heavily relies on so-called instance features, i.e., numerical characteristics of the problem at hand ideally extracted with computationally low-demanding routines. For the traveling salesperson problem (TSP) a plethora of features have been suggested. Most of these features are, if at all, only normalized imprecisely raising the issue of feature values being strongly affected by the instance size. Such artifacts may have detrimental effects on algorithm selection models. We propose a normalization for two feature groups which stood out in multiple AS studies on the TSP: (a) features based on a minimum spanning tree (MST) and (b) a k-nearest neighbor graph (NNG) transformation of the input instance. To this end we theoretically derive minimum and maximum values for properties of MSTs and k-NNGs of Euclidean graphs. We analyze the differences in feature space between normalized versions of these features and their unnormalized counterparts. Our empirical investigations on various TSP benchmark sets point out that the feature scaling succeeds in eliminating the effect of the instance size. Eventually, a proof-of-concept AS-study shows promising results: models trained with normalized features tend to outperform those trained with the respective vanilla features.","lang":"eng"}],"extern":"1","citation":{"mla":"Heins, Jonathan, et al. “On the Potential of Normalized TSP Features for Automated Algorithm Selection.” <i>Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, Association for Computing Machinery, 2021, pp. 1–15.","ama":"Heins J, Bossek J, Pohl J, Seiler M, Trautmann H, Kerschke P. On the Potential of Normalized TSP Features for Automated Algorithm Selection. In: <i>Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>. Association for Computing Machinery; 2021:1–15.","bibtex":"@inbook{Heins_Bossek_Pohl_Seiler_Trautmann_Kerschke_2021, place={New York, NY, USA}, title={On the Potential of Normalized TSP Features for Automated Algorithm Selection}, booktitle={Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms}, publisher={Association for Computing Machinery}, author={Heins, Jonathan and Bossek, Jakob and Pohl, Janina and Seiler, Moritz and Trautmann, Heike and Kerschke, Pascal}, year={2021}, pages={1–15} }","apa":"Heins, J., Bossek, J., Pohl, J., Seiler, M., Trautmann, H., &#38; Kerschke, P. (2021). On the Potential of Normalized TSP Features for Automated Algorithm Selection. In <i>Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i> (pp. 1–15). Association for Computing Machinery.","ieee":"J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, and P. Kerschke, “On the Potential of Normalized TSP Features for Automated Algorithm Selection,” in <i>Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, New York, NY, USA: Association for Computing Machinery, 2021, pp. 1–15.","short":"J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, P. Kerschke, in: Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, Association for Computing Machinery, New York, NY, USA, 2021, pp. 1–15.","chicago":"Heins, Jonathan, Jakob Bossek, Janina Pohl, Moritz Seiler, Heike Trautmann, and Pascal Kerschke. “On the Potential of Normalized TSP Features for Automated Algorithm Selection.” In <i>Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, 1–15. New York, NY, USA: Association for Computing Machinery, 2021."},"publication":"Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms","department":[{"_id":"819"}],"type":"book_chapter","keyword":["automated algorithm selection","graph theory","instance features","normalization","traveling salesperson problem (TSP)"],"place":"New York, NY, USA","date_created":"2023-11-14T15:58:58Z"},{"page":"1423–1432","publisher":"Association for Computing Machinery","_id":"48876","user_id":"102979","status":"public","place":"New York, NY, USA","citation":{"ama":"Bossek J, Wagner M. Generating Instances with Performance Differences for More than Just Two Algorithms. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>. GECCO’21. Association for Computing Machinery; 2021:1423–1432. doi:<a href=\"https://doi.org/10.1145/3449726.3463165\">10.1145/3449726.3463165</a>","short":"J. Bossek, M. Wagner, in: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, New York, NY, USA, 2021, pp. 1423–1432.","chicago":"Bossek, Jakob, and Markus Wagner. “Generating Instances with Performance Differences for More than Just Two Algorithms.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, 1423–1432. GECCO’21. New York, NY, USA: Association for Computing Machinery, 2021. <a href=\"https://doi.org/10.1145/3449726.3463165\">https://doi.org/10.1145/3449726.3463165</a>.","bibtex":"@inproceedings{Bossek_Wagner_2021, place={New York, NY, USA}, series={GECCO’21}, title={Generating Instances with Performance Differences for More than Just Two Algorithms}, DOI={<a href=\"https://doi.org/10.1145/3449726.3463165\">10.1145/3449726.3463165</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference Companion}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Wagner, Markus}, year={2021}, pages={1423–1432}, collection={GECCO’21} }","apa":"Bossek, J., &#38; Wagner, M. (2021). Generating Instances with Performance Differences for More than Just Two Algorithms. <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, 1423–1432. <a href=\"https://doi.org/10.1145/3449726.3463165\">https://doi.org/10.1145/3449726.3463165</a>","mla":"Bossek, Jakob, and Markus Wagner. “Generating Instances with Performance Differences for More than Just Two Algorithms.” <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, Association for Computing Machinery, 2021, pp. 1423–1432, doi:<a href=\"https://doi.org/10.1145/3449726.3463165\">10.1145/3449726.3463165</a>.","ieee":"J. Bossek and M. Wagner, “Generating Instances with Performance Differences for More than Just Two Algorithms,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, 2021, pp. 1423–1432, doi: <a href=\"https://doi.org/10.1145/3449726.3463165\">10.1145/3449726.3463165</a>."},"series_title":"GECCO’21","language":[{"iso":"eng"}],"doi":"10.1145/3449726.3463165","year":"2021","title":"Generating Instances with Performance Differences for More than Just Two Algorithms","author":[{"full_name":"Bossek, Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob","id":"102979"},{"full_name":"Wagner, Markus","first_name":"Markus","last_name":"Wagner"}],"publication_identifier":{"isbn":["978-1-4503-8351-6"]},"date_updated":"2023-12-13T10:47:41Z","date_created":"2023-11-14T15:58:57Z","type":"conference","keyword":["evolutionary algorithms","evolving instances","fitness function","instance hardness","traveling thief problem (TTP)"],"department":[{"_id":"819"}],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","extern":"1","abstract":[{"lang":"eng","text":"In recent years, Evolutionary Algorithms (EAs) have frequently been adopted to evolve instances for optimization problems that pose difficulties for one algorithm while being rather easy for a competitor and vice versa. Typically, this is achieved by either minimizing or maximizing the performance difference or ratio which serves as the fitness function. Repeating this process is useful to gain insights into strengths/weaknesses of certain algorithms or to build a set of instances with strong performance differences as a foundation for automatic per-instance algorithm selection or configuration. We contribute to this branch of research by proposing fitness-functions to evolve instances that show large performance differences for more than just two algorithms simultaneously. As a proof-of-principle, we evolve instances of the multi-component Traveling Thief Problem (TTP) for three incomplete TTP-solvers. Our results point out that our strategies are promising, but unsurprisingly their success strongly relies on the algorithms’ performance complementarity."}]}]