--- _id: '48861' abstract: - lang: eng text: Generating instances of different properties is key to algorithm selection methods that differentiate between the performance of different solvers for a given combinatorial optimization problem. A wide range of methods using evolutionary computation techniques has been introduced in recent years. With this paper, we contribute to this area of research by providing a new approach based on quality diversity (QD) that is able to explore the whole feature space. QD algorithms allow to create solutions of high quality within a given feature space by splitting it up into boxes and improving solution quality within each box. We use our QD approach for the generation of TSP instances to visualize and analyze the variety of instances differentiating various TSP solvers and compare it to instances generated by established approaches from the literature. author: - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Frank full_name: Neumann, Frank last_name: Neumann citation: ama: 'Bossek J, Neumann F. Exploring the Feature Space of TSP Instances Using Quality Diversity. In: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO ’22. Association for Computing Machinery; 2022:186–194. doi:10.1145/3512290.3528851' apa: Bossek, J., & Neumann, F. (2022). Exploring the Feature Space of TSP Instances Using Quality Diversity. Proceedings of the Genetic and Evolutionary Computation Conference, 186–194. https://doi.org/10.1145/3512290.3528851 bibtex: '@inproceedings{Bossek_Neumann_2022, place={New York, NY, USA}, series={GECCO ’22}, title={Exploring the Feature Space of TSP Instances Using Quality Diversity}, DOI={10.1145/3512290.3528851}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Neumann, Frank}, year={2022}, pages={186–194}, collection={GECCO ’22} }' chicago: 'Bossek, Jakob, and Frank Neumann. “Exploring the Feature Space of TSP Instances Using Quality Diversity.” In Proceedings of the Genetic and Evolutionary Computation Conference, 186–194. GECCO ’22. New York, NY, USA: Association for Computing Machinery, 2022. https://doi.org/10.1145/3512290.3528851.' ieee: 'J. Bossek and F. Neumann, “Exploring the Feature Space of TSP Instances Using Quality Diversity,” in Proceedings of the Genetic and Evolutionary Computation Conference, 2022, pp. 186–194, doi: 10.1145/3512290.3528851.' mla: Bossek, Jakob, and Frank Neumann. “Exploring the Feature Space of TSP Instances Using Quality Diversity.” Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, 2022, pp. 186–194, doi:10.1145/3512290.3528851. short: 'J. Bossek, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2022, pp. 186–194.' date_created: 2023-11-14T15:58:55Z date_updated: 2023-12-13T10:45:56Z department: - _id: '819' doi: 10.1145/3512290.3528851 extern: '1' keyword: - instance features - instance generation - quality diversity - TSP language: - iso: eng page: 186–194 place: New York, NY, USA publication: Proceedings of the Genetic and Evolutionary Computation Conference publication_identifier: isbn: - 978-1-4503-9237-2 publication_status: published publisher: Association for Computing Machinery series_title: GECCO ’22 status: public title: Exploring the Feature Space of TSP Instances Using Quality Diversity type: conference user_id: '102979' year: '2022' ... --- _id: '48868' author: - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Aneta full_name: Neumann, Aneta last_name: Neumann - first_name: Frank full_name: Neumann, Frank last_name: Neumann citation: ama: 'Bossek J, Neumann A, Neumann F. Evolutionary Diversity Optimization for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion. GECCO’22. Association for Computing Machinery; 2022:824–842. doi:10.1145/3520304.3533626' apa: 'Bossek, J., Neumann, A., & Neumann, F. (2022). Evolutionary Diversity Optimization for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA. Proceedings of the Genetic and Evolutionary Computation Conference Companion, 824–842. https://doi.org/10.1145/3520304.3533626' bibtex: '@inproceedings{Bossek_Neumann_Neumann_2022, place={New York, NY, USA}, series={GECCO’22}, title={Evolutionary Diversity Optimization for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA}, DOI={10.1145/3520304.3533626}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference Companion}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Neumann, Aneta and Neumann, Frank}, year={2022}, pages={824–842}, collection={GECCO’22} }' chicago: 'Bossek, Jakob, Aneta Neumann, and Frank Neumann. “Evolutionary Diversity Optimization for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA.” In Proceedings of the Genetic and Evolutionary Computation Conference Companion, 824–842. GECCO’22. New York, NY, USA: Association for Computing Machinery, 2022. https://doi.org/10.1145/3520304.3533626.' ieee: 'J. Bossek, A. Neumann, and F. Neumann, “Evolutionary Diversity Optimization for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA,” in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2022, pp. 824–842, doi: 10.1145/3520304.3533626.' mla: 'Bossek, Jakob, et al. “Evolutionary Diversity Optimization for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA.” Proceedings of the Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, 2022, pp. 824–842, doi:10.1145/3520304.3533626.' short: 'J. Bossek, A. Neumann, F. Neumann, in: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, New York, NY, USA, 2022, pp. 824–842.' date_created: 2023-11-14T15:58:56Z date_updated: 2023-12-13T10:46:19Z department: - _id: '819' doi: 10.1145/3520304.3533626 extern: '1' language: - iso: eng page: 824–842 place: New York, NY, USA publication: Proceedings of the Genetic and Evolutionary Computation Conference Companion publication_identifier: isbn: - 978-1-4503-9268-6 publication_status: published publisher: Association for Computing Machinery series_title: GECCO’22 status: public title: 'Evolutionary Diversity Optimization for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA' type: conference user_id: '102979' year: '2022' ... --- _id: '48882' 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. author: - first_name: Jonathan full_name: Heins, Jonathan last_name: Heins - first_name: Jeroen full_name: Rook, Jeroen last_name: Rook - first_name: Lennart full_name: Schäpermeier, Lennart last_name: Schäpermeier - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Heike full_name: Trautmann, Heike last_name: Trautmann citation: 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. Parallel Problem Solving from Nature (PPSN XVII). Lecture Notes in Computer Science. Springer International Publishing; 2022:192–206. doi:10.1007/978-3-031-14714-2_14' apa: 'Heins, J., Rook, J., Schäpermeier, L., Kerschke, P., Bossek, J., & 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, & T. Tusar (Eds.), Parallel Problem Solving from Nature (PPSN XVII) (pp. 192–206). Springer International Publishing. https://doi.org/10.1007/978-3-031-14714-2_14' 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={10.1007/978-3-031-14714-2_14}, 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} }' 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 Parallel Problem Solving from Nature (PPSN XVII), 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. https://doi.org/10.1007/978-3-031-14714-2_14.' 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 Parallel Problem Solving from Nature (PPSN XVII), 2022, pp. 192–206, doi: 10.1007/978-3-031-14714-2_14.' mla: 'Heins, Jonathan, et al. “BBE: Basin-Based Evaluation of Multimodal Multi-Objective Optimization Problems.” Parallel Problem Solving from Nature (PPSN XVII), edited by Günter Rudolph et al., Springer International Publishing, 2022, pp. 192–206, doi:10.1007/978-3-031-14714-2_14.' 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.' date_created: 2023-11-14T15:58:58Z date_updated: 2023-12-13T10:47:50Z department: - _id: '819' doi: 10.1007/978-3-031-14714-2_14 editor: - first_name: Günter full_name: Rudolph, Günter last_name: Rudolph - first_name: Anna V. full_name: Kononova, Anna V. last_name: Kononova - first_name: Hernán full_name: Aguirre, Hernán last_name: Aguirre - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke - first_name: Gabriela full_name: Ochoa, Gabriela last_name: Ochoa - first_name: Tea full_name: Tusar, Tea last_name: Tusar extern: '1' keyword: - Anytime behavior - Benchmarking - Continuous optimization - Multi-objective optimization - Multimodality - Performance metric language: - iso: eng page: 192–206 place: Cham publication: Parallel Problem Solving from Nature (PPSN XVII) publication_identifier: isbn: - 978-3-031-14714-2 publisher: Springer International Publishing series_title: Lecture Notes in Computer Science status: public title: 'BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems' type: conference user_id: '102979' year: '2022' ... --- _id: '48894' abstract: - lang: eng 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. author: - first_name: Adel full_name: Nikfarjam, Adel last_name: Nikfarjam - first_name: Aneta full_name: Neumann, Aneta last_name: Neumann - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Frank full_name: Neumann, Frank last_name: Neumann citation: 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. Parallel Problem Solving from Nature (PPSN XVII). Lecture Notes in Computer Science. Springer International Publishing; 2022:237–249. doi:10.1007/978-3-031-14714-2_17' apa: Nikfarjam, A., Neumann, A., Bossek, J., & Neumann, F. (2022). Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem. In G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, & T. Tu\v sar (Eds.), Parallel Problem Solving from Nature (PPSN XVII) (pp. 237–249). Springer International Publishing. https://doi.org/10.1007/978-3-031-14714-2_17 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={10.1007/978-3-031-14714-2_17}, 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} }' chicago: 'Nikfarjam, Adel, Aneta Neumann, Jakob Bossek, and Frank Neumann. “Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem.” In Parallel Problem Solving from Nature (PPSN XVII), 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. https://doi.org/10.1007/978-3-031-14714-2_17.' ieee: 'A. Nikfarjam, A. Neumann, J. Bossek, and F. Neumann, “Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem,” in Parallel Problem Solving from Nature (PPSN XVII), 2022, pp. 237–249, doi: 10.1007/978-3-031-14714-2_17.' mla: Nikfarjam, Adel, et al. “Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem.” Parallel Problem Solving from Nature (PPSN XVII), edited by Günter Rudolph et al., Springer International Publishing, 2022, pp. 237–249, doi:10.1007/978-3-031-14714-2_17. 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.' date_created: 2023-11-14T15:59:00Z date_updated: 2023-12-13T10:49:51Z department: - _id: '819' doi: 10.1007/978-3-031-14714-2_17 editor: - first_name: Günter full_name: Rudolph, Günter last_name: Rudolph - first_name: Anna V. full_name: Kononova, Anna V. last_name: Kononova - first_name: Hernán full_name: Aguirre, Hernán last_name: Aguirre - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke - first_name: Gabriela full_name: Ochoa, Gabriela last_name: Ochoa - first_name: Tea full_name: Tu\v sar, Tea last_name: Tu\v sar extern: '1' keyword: - Co-evolutionary algorithms - Evolutionary diversity optimisation - Quality diversity - Traveling thief problem language: - iso: eng page: 237–249 place: Cham publication: Parallel Problem Solving from Nature (PPSN XVII) publication_identifier: isbn: - 978-3-031-14714-2 publication_status: published publisher: Springer International Publishing series_title: Lecture Notes in Computer Science status: public title: Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem type: conference user_id: '102979' year: '2022' ... --- _id: '48878' 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\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. author: - first_name: Lena full_name: Clever, Lena last_name: Clever - first_name: Janina Susanne full_name: Pohl, Janina Susanne last_name: Pohl - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke - first_name: Heike full_name: Trautmann, Heike last_name: Trautmann citation: ama: 'Clever L, Pohl JS, Bossek J, Kerschke P, Trautmann H. Process-Oriented Stream Classification Pipeline: A Literature Review. Applied Sciences. 2022;12(18):9094. doi:10.3390/app12189094' apa: 'Clever, L., Pohl, J. S., Bossek, J., Kerschke, P., & Trautmann, H. (2022). Process-Oriented Stream Classification Pipeline: A Literature Review. Applied Sciences, 12(18), 9094. https://doi.org/10.3390/app12189094' bibtex: '@article{Clever_Pohl_Bossek_Kerschke_Trautmann_2022, title={Process-Oriented Stream Classification Pipeline: A Literature Review}, volume={12}, DOI={10.3390/app12189094}, 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} }' chicago: 'Clever, Lena, Janina Susanne Pohl, Jakob Bossek, Pascal Kerschke, and Heike Trautmann. “Process-Oriented Stream Classification Pipeline: A Literature Review.” Applied Sciences 12, no. 18 (2022): 9094. https://doi.org/10.3390/app12189094.' ieee: 'L. Clever, J. S. Pohl, J. Bossek, P. Kerschke, and H. Trautmann, “Process-Oriented Stream Classification Pipeline: A Literature Review,” Applied Sciences, vol. 12, no. 18, p. 9094, 2022, doi: 10.3390/app12189094.' mla: 'Clever, Lena, et al. “Process-Oriented Stream Classification Pipeline: A Literature Review.” Applied Sciences, vol. 12, no. 18, {Multidisciplinary Digital Publishing Institute}, 2022, p. 9094, doi:10.3390/app12189094.' short: L. Clever, J.S. Pohl, J. Bossek, P. Kerschke, H. Trautmann, Applied Sciences 12 (2022) 9094. date_created: 2023-11-14T15:58:57Z date_updated: 2023-12-13T10:50:56Z department: - _id: '819' doi: 10.3390/app12189094 intvolume: ' 12' issue: '18' keyword: - big data - data mining - data stream analysis - machine learning - stream classification - supervised learning language: - iso: eng page: '9094' publication: Applied Sciences publication_identifier: issn: - 2076-3417 publisher: '{Multidisciplinary Digital Publishing Institute}' status: public title: 'Process-Oriented Stream Classification Pipeline: A Literature Review' type: journal_article user_id: '102979' volume: 12 year: '2022' ... --- _id: '48896' 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. author: - first_name: Jeroen full_name: Rook, Jeroen last_name: Rook - first_name: Heike full_name: Trautmann, Heike last_name: Trautmann - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Christian full_name: Grimme, Christian last_name: Grimme citation: ama: 'Rook J, Trautmann H, Bossek J, Grimme C. On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion. GECCO’22. Association for Computing Machinery; 2022:356–359. doi:10.1145/3520304.3528998' apa: Rook, J., Trautmann, H., Bossek, J., & Grimme, C. (2022). On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems. Proceedings of the Genetic and Evolutionary Computation Conference Companion, 356–359. https://doi.org/10.1145/3520304.3528998 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={10.1145/3520304.3528998}, 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} }' 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 Proceedings of the Genetic and Evolutionary Computation Conference Companion, 356–359. GECCO’22. New York, NY, USA: Association for Computing Machinery, 2022. https://doi.org/10.1145/3520304.3528998.' 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 Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2022, pp. 356–359, doi: 10.1145/3520304.3528998.' mla: Rook, Jeroen, et al. “On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems.” Proceedings of the Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, 2022, pp. 356–359, doi:10.1145/3520304.3528998. 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.' date_created: 2023-11-14T15:59:00Z date_updated: 2023-12-13T10:50:24Z department: - _id: '819' doi: 10.1145/3520304.3528998 extern: '1' keyword: - configuration - multi-modality - multi-objective optimization language: - iso: eng page: 356–359 place: New York, NY, USA publication: Proceedings of the Genetic and Evolutionary Computation Conference Companion publication_identifier: isbn: - 978-1-4503-9268-6 publisher: Association for Computing Machinery series_title: GECCO’22 status: public title: On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems type: conference user_id: '102979' year: '2022' ... --- _id: '52532' author: - first_name: Agatha S. full_name: Rodrigues, Agatha S. last_name: Rodrigues - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke - first_name: Carlos Alberto De Bragança full_name: Pereira, Carlos Alberto De Bragança last_name: Pereira - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 - first_name: Carolin full_name: Wagner, Carolin last_name: Wagner - first_name: Bernd full_name: Hellingrath, Bernd last_name: Hellingrath - first_name: Adriano full_name: Polpo, Adriano last_name: Polpo citation: ama: Rodrigues AS, Kerschke P, Pereira CADB, et al. Estimation of component reliability from superposed renewal processes by means of latent variables. Comput Stat. 2022;37(1):355–379. doi:10.1007/S00180-021-01124-0 apa: Rodrigues, A. S., Kerschke, P., Pereira, C. A. D. B., Trautmann, H., Wagner, C., Hellingrath, B., & Polpo, A. (2022). Estimation of component reliability from superposed renewal processes by means of latent variables. Comput. Stat., 37(1), 355–379. https://doi.org/10.1007/S00180-021-01124-0 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={10.1007/S00180-021-01124-0}, 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} }' 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.” Comput. Stat. 37, no. 1 (2022): 355–379. https://doi.org/10.1007/S00180-021-01124-0.' ieee: 'A. S. Rodrigues et al., “Estimation of component reliability from superposed renewal processes by means of latent variables,” Comput. Stat., vol. 37, no. 1, pp. 355–379, 2022, doi: 10.1007/S00180-021-01124-0.' mla: Rodrigues, Agatha S., et al. “Estimation of Component Reliability from Superposed Renewal Processes by Means of Latent Variables.” Comput. Stat., vol. 37, no. 1, 2022, pp. 355–379, doi:10.1007/S00180-021-01124-0. 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. date_created: 2024-03-13T09:59:21Z date_updated: 2024-03-13T10:28:01Z department: - _id: '819' doi: 10.1007/S00180-021-01124-0 intvolume: ' 37' issue: '1' language: - iso: eng page: 355–379 publication: Comput. Stat. status: public title: Estimation of component reliability from superposed renewal processes by means of latent variables type: journal_article user_id: '15504' volume: 37 year: '2022' ... --- _id: '46307' abstract: - lang: eng 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. author: - first_name: Moritz full_name: Seiler, Moritz id: '105520' last_name: Seiler - first_name: Raphael Patrick full_name: Prager, Raphael Patrick last_name: Prager - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: 'Seiler M, Prager RP, Kerschke P, Trautmann H. A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes. In: Proceedings of the Genetic and Evolutionary Computation Conference. Association for Computing Machinery; 2022:657–665. doi:10.1145/3512290.3528834' apa: Seiler, M., Prager, R. P., Kerschke, P., & Trautmann, H. (2022). A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes. Proceedings of the Genetic and Evolutionary Computation Conference, 657–665. https://doi.org/10.1145/3512290.3528834 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={10.1145/3512290.3528834}, 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} }' 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 Proceedings of the Genetic and Evolutionary Computation Conference, 657–665. New York, NY, USA: Association for Computing Machinery, 2022. https://doi.org/10.1145/3512290.3528834.' 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 Proceedings of the Genetic and Evolutionary Computation Conference, 2022, pp. 657–665, doi: 10.1145/3512290.3528834.' mla: Seiler, Moritz, et al. “A Collection of Deep Learning-Based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes.” Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, 2022, pp. 657–665, doi:10.1145/3512290.3528834. 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.' date_created: 2023-08-04T07:15:59Z date_updated: 2024-06-07T07:13:23Z department: - _id: '34' - _id: '819' doi: 10.1145/3512290.3528834 language: - iso: eng page: 657–665 place: New York, NY, USA publication: Proceedings of the Genetic and Evolutionary Computation Conference publication_identifier: isbn: - '9781450392372' publisher: Association for Computing Machinery status: public title: A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes type: conference user_id: '15504' year: '2022' ... --- _id: '46304' 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. author: - first_name: Raphael Patrick full_name: Prager, Raphael Patrick last_name: Prager - first_name: Moritz full_name: Seiler, Moritz id: '105520' last_name: Seiler - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke citation: 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. Parallel Problem Solving from Nature — PPSN XVII. Springer International Publishing; 2022:3–17. doi:10.1007/978-3-031-14714-2_1' apa: 'Prager, R. P., Seiler, M., Trautmann, H., & 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, & T. Tušar (Eds.), Parallel Problem Solving from Nature — PPSN XVII (pp. 3–17). Springer International Publishing. https://doi.org/10.1007/978-3-031-14714-2_1' 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={10.1007/978-3-031-14714-2_1}, 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} }' 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 Parallel Problem Solving from Nature — PPSN XVII, edited by Günter Rudolph, Anna V. Kononova, Hernán Aguirre, Pascal Kerschke, Gabriela Ochoa, and Tea Tušar, 3–17. Cham: Springer International Publishing, 2022. https://doi.org/10.1007/978-3-031-14714-2_1.' 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 Parallel Problem Solving from Nature — PPSN XVII, 2022, pp. 3–17, doi: 10.1007/978-3-031-14714-2_1.' mla: 'Prager, Raphael Patrick, et al. “Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods.” Parallel Problem Solving from Nature — PPSN XVII, edited by Günter Rudolph et al., Springer International Publishing, 2022, pp. 3–17, doi:10.1007/978-3-031-14714-2_1.' 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.' date_created: 2023-08-04T07:12:33Z date_updated: 2024-06-07T07:13:47Z department: - _id: '34' - _id: '819' doi: 10.1007/978-3-031-14714-2_1 editor: - first_name: Günter full_name: Rudolph, Günter last_name: Rudolph - first_name: Anna V. full_name: Kononova, Anna V. last_name: Kononova - first_name: Hernán full_name: Aguirre, Hernán last_name: Aguirre - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke - first_name: Gabriela full_name: Ochoa, Gabriela last_name: Ochoa - first_name: Tea full_name: Tušar, Tea last_name: Tušar language: - iso: eng page: 3–17 place: Cham publication: Parallel Problem Solving from Nature — PPSN XVII publication_identifier: isbn: - 978-3-031-14714-2 publisher: Springer International Publishing status: public title: 'Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods' type: conference user_id: '15504' year: '2022' ... --- _id: '46303' 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. author: - first_name: Janina Susanne full_name: Pohl, Janina Susanne last_name: Pohl - first_name: Dennis full_name: Assenmacher, Dennis last_name: Assenmacher - first_name: Moritz full_name: Seiler, Moritz id: '105520' last_name: Seiler - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 - first_name: Christian full_name: Grimme, Christian last_name: Grimme citation: 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. Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM). AAAI Press; 2022:1–10. doi:10.36190/2022.91' apa: Pohl, J. S., Assenmacher, D., Seiler, M., Trautmann, H., & Grimme, C. (2022). Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches. In for the Advancement of Artificial Intelligence (AAAI) Association (Ed.), Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM) (pp. 1–10). AAAI Press. https://doi.org/10.36190/2022.91 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={10.36190/2022.91}, 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} }' 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 Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM), edited by for the Advancement of Artificial Intelligence (AAAI) Association, 1–10. Palo Alto, CA, USA: AAAI Press, 2022. https://doi.org/10.36190/2022.91.' 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 Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM), 2022, pp. 1–10, doi: 10.36190/2022.91.' mla: Pohl, Janina Susanne, et al. “Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches.” Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM), edited by for the Advancement of Artificial Intelligence (AAAI) Association, AAAI Press, 2022, pp. 1–10, doi:10.36190/2022.91. 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.' date_created: 2023-08-04T07:11:34Z date_updated: 2024-06-07T07:13:35Z department: - _id: '34' - _id: '819' doi: 10.36190/2022.91 editor: - first_name: for full_name: the Advancement of Artificial Intelligence (AAAI) Association, for last_name: the Advancement of Artificial Intelligence (AAAI) Association language: - iso: eng page: 1–10 place: Palo Alto, CA, USA publication: Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM) publisher: AAAI Press status: public title: Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches type: conference user_id: '15504' year: '2022' ... --- _id: '46309' 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. author: - first_name: Lena full_name: Clever, Lena last_name: Clever - first_name: Janina Susanne full_name: Pohl, Janina Susanne last_name: Pohl - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: 'Clever L, Pohl JS, Bossek J, Kerschke P, Trautmann H. Process-Oriented Stream Classification Pipeline: A Literature Review. Applied Sciences. 2022;12(8):1–44. doi:10.3390/app12189094' apa: 'Clever, L., Pohl, J. S., Bossek, J., Kerschke, P., & Trautmann, H. (2022). Process-Oriented Stream Classification Pipeline: A Literature Review. Applied Sciences, 12(8), 1–44. https://doi.org/10.3390/app12189094' bibtex: '@article{Clever_Pohl_Bossek_Kerschke_Trautmann_2022, title={Process-Oriented Stream Classification Pipeline: A Literature Review}, volume={12}, DOI={10.3390/app12189094}, 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} }' chicago: 'Clever, Lena, Janina Susanne Pohl, Jakob Bossek, Pascal Kerschke, and Heike Trautmann. “Process-Oriented Stream Classification Pipeline: A Literature Review.” Applied Sciences 12, no. 8 (2022): 1–44. https://doi.org/10.3390/app12189094.' ieee: 'L. Clever, J. S. Pohl, J. Bossek, P. Kerschke, and H. Trautmann, “Process-Oriented Stream Classification Pipeline: A Literature Review,” Applied Sciences, vol. 12, no. 8, pp. 1–44, 2022, doi: 10.3390/app12189094.' mla: 'Clever, Lena, et al. “Process-Oriented Stream Classification Pipeline: A Literature Review.” Applied Sciences, vol. 12, no. 8, 2022, pp. 1–44, doi:10.3390/app12189094.' short: L. Clever, J.S. Pohl, J. Bossek, P. Kerschke, H. Trautmann, Applied Sciences 12 (2022) 1–44. date_created: 2023-08-04T07:17:23Z date_updated: 2024-06-10T12:02:17Z department: - _id: '34' - _id: '819' doi: 10.3390/app12189094 intvolume: ' 12' issue: '8' language: - iso: eng page: 1–44 publication: Applied Sciences status: public title: 'Process-Oriented Stream Classification Pipeline: A Literature Review' type: journal_article user_id: '15504' volume: 12 year: '2022' ... --- _id: '46302' author: - first_name: J full_name: Heins, J last_name: Heins - first_name: J full_name: Rook, J last_name: Rook - first_name: L full_name: Schäpermeier, L last_name: Schäpermeier - first_name: P full_name: Kerschke, P last_name: Kerschke - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: 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. Parallel Problem Solving from Nature — PPSN XVII. Springer International Publishing; 2022:192–206.' apa: 'Heins, J., Rook, J., Schäpermeier, L., Kerschke, P., Bossek, J., & Trautmann, H. (2022). BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems. In G. Rudolph, A. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, & T. Tušar (Eds.), Parallel Problem Solving from Nature — PPSN XVII (pp. 192–206). Springer International Publishing.' 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} }' 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 Parallel Problem Solving from Nature — PPSN XVII, edited by G Rudolph, AV Kononova, H Aguirre, P Kerschke, G Ochoa, and T Tušar, 192–206. Cham: Springer International Publishing, 2022.' 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 Parallel Problem Solving from Nature — PPSN XVII, 2022, pp. 192–206.' mla: 'Heins, J., et al. “BBE: Basin-Based Evaluation of Multimodal Multi-Objective Optimization Problems.” Parallel Problem Solving from Nature — PPSN XVII, edited by G Rudolph et al., Springer International Publishing, 2022, pp. 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.' date_created: 2023-08-04T07:10:52Z date_updated: 2024-06-10T12:02:35Z department: - _id: '34' - _id: '819' editor: - first_name: G full_name: Rudolph, G last_name: Rudolph - first_name: AV full_name: Kononova, AV last_name: Kononova - first_name: H full_name: Aguirre, H last_name: Aguirre - first_name: P full_name: Kerschke, P last_name: Kerschke - first_name: G full_name: Ochoa, G last_name: Ochoa - first_name: T full_name: Tušar, T last_name: Tušar language: - iso: eng page: 192–206 place: Cham publication: Parallel Problem Solving from Nature — PPSN XVII publication_identifier: isbn: - 978-3-031-14714-2 publisher: Springer International Publishing status: public title: 'BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems' type: conference user_id: '15504' year: '2022' ... --- _id: '46305' 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. author: - first_name: J full_name: Rook, J last_name: Rook - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: C full_name: Grimme, C last_name: Grimme citation: 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. Proceedings of the Genetic and Evolutionary Computation Conference Companion. GECCO ’22. Association for Computing Machinery; 2022:356–359. doi:10.1145/3520304.3528998' apa: Rook, J., Trautmann, H., Bossek, J., & Grimme, C. (2022). On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems. In J. Fieldsend & M. Wagner (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 356–359). Association for Computing Machinery. https://doi.org/10.1145/3520304.3528998 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={10.1145/3520304.3528998}, 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 Proceedings of the Genetic and Evolutionary Computation Conference Companion, edited by J Fieldsend and M. Wagner, 356–359. GECCO ’22. New York, NY, USA: Association for Computing Machinery, 2022. https://doi.org/10.1145/3520304.3528998.' 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 Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2022, pp. 356–359, doi: 10.1145/3520304.3528998.' mla: Rook, J., et al. “On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems.” Proceedings of the Genetic and Evolutionary Computation Conference Companion, edited by J Fieldsend and M. Wagner, Association for Computing Machinery, 2022, pp. 356–359, doi:10.1145/3520304.3528998. 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.' date_created: 2023-08-04T07:14:24Z date_updated: 2026-02-19T15:12:35Z department: - _id: '34' - _id: '819' doi: 10.1145/3520304.3528998 editor: - first_name: J full_name: Fieldsend, J last_name: Fieldsend - first_name: M. full_name: Wagner, M. last_name: Wagner language: - iso: eng page: 356–359 place: New York, NY, USA publication: Proceedings of the Genetic and Evolutionary Computation Conference Companion publication_identifier: isbn: - '9781450392686' publisher: Association for Computing Machinery series_title: GECCO ’22 status: public title: On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems type: conference user_id: '14972' year: '2022' ... --- _id: '46318' abstract: - lang: eng 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.' author: - first_name: Christian full_name: Grimme, Christian last_name: Grimme - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke - first_name: Pelin full_name: Aspar, Pelin last_name: Aspar - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 - first_name: Mike full_name: Preuss, Mike last_name: Preuss - first_name: André H. full_name: Deutz, André H. last_name: Deutz - first_name: Hao full_name: Wang, Hao last_name: Wang - first_name: Michael full_name: Emmerich, Michael last_name: Emmerich citation: ama: 'Grimme C, Kerschke P, Aspar P, et al. Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization. Computers & Operations Research. 2021;136:105489. doi:https://doi.org/10.1016/j.cor.2021.105489' apa: 'Grimme, C., Kerschke, P., Aspar, P., Trautmann, H., Preuss, M., Deutz, A. H., Wang, H., & Emmerich, M. (2021). Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization. Computers & Operations Research, 136, 105489. https://doi.org/10.1016/j.cor.2021.105489' 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={https://doi.org/10.1016/j.cor.2021.105489}, journal={Computers & 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} }' 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.” Computers & Operations Research 136 (2021): 105489. https://doi.org/10.1016/j.cor.2021.105489.' ieee: 'C. Grimme et al., “Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization,” Computers & Operations Research, vol. 136, p. 105489, 2021, doi: https://doi.org/10.1016/j.cor.2021.105489.' mla: 'Grimme, Christian, et al. “Peeking beyond Peaks: Challenges and Research Potentials of Continuous Multimodal Multi-Objective Optimization.” Computers & Operations Research, vol. 136, 2021, p. 105489, doi:https://doi.org/10.1016/j.cor.2021.105489.' short: C. Grimme, P. Kerschke, P. Aspar, H. Trautmann, M. Preuss, A.H. Deutz, H. Wang, M. Emmerich, Computers & Operations Research 136 (2021) 105489. date_created: 2023-08-04T07:28:34Z date_updated: 2023-10-16T12:58:42Z department: - _id: '34' - _id: '819' doi: https://doi.org/10.1016/j.cor.2021.105489 intvolume: ' 136' keyword: - Multimodal optimization - Multi-objective continuous optimization - Landscape analysis - Visualization - Benchmarking - Theory - Algorithms language: - iso: eng page: '105489' publication: Computers & Operations Research publication_identifier: issn: - 0305-0548 status: public title: 'Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization' type: journal_article user_id: '15504' volume: 136 year: '2021' ... --- _id: '46311' abstract: - lang: eng 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." author: - first_name: Pelin full_name: Aspar, Pelin last_name: Aspar - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke - first_name: Vera full_name: Steinhoff, Vera last_name: Steinhoff - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 - first_name: Christian full_name: Grimme, Christian last_name: Grimme citation: 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. Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings. Springer; 2021:311–322. doi:10.1007/978-3-030-72062-9_25' apa: 'Aspar, P., Kerschke, P., Steinhoff, V., Trautmann, H., & 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.), Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings (pp. 311–322). Springer. https://doi.org/10.1007/978-3-030-72062-9_25' 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={10.1007/978-3-030-72062-9_25}, 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} }' 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 Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings, edited by H. et al. Ishibuchi, 311–322. Heidelberg, Berlin: Springer, 2021. https://doi.org/10.1007/978-3-030-72062-9_25.' 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 Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings, 2021, pp. 311–322, doi: 10.1007/978-3-030-72062-9_25.' mla: 'Aspar, Pelin, et al. “Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective Space by Means of Multiobjectivization.” Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings, edited by H. et al. Ishibuchi, Springer, 2021, pp. 311–322, doi:10.1007/978-3-030-72062-9_25.' 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.' date_created: 2023-08-04T07:21:17Z date_updated: 2023-10-16T12:54:29Z department: - _id: '34' - _id: '819' doi: 10.1007/978-3-030-72062-9_25 editor: - first_name: H. full_name: et al. Ishibuchi, H. last_name: et al. Ishibuchi language: - iso: eng page: 311–322 place: Heidelberg, Berlin publication: 'Evolutionary Multi-Criterion Optimization: 11$^th$ International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings' publisher: Springer status: public title: 'Multi^3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective Space by Means of Multiobjectivization' type: conference user_id: '15504' year: '2021' ... --- _id: '46317' abstract: - lang: eng 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.' author: - first_name: Crispin full_name: Coombs, Crispin last_name: Coombs - first_name: Patrick full_name: Stacey, Patrick last_name: Stacey - first_name: Peter full_name: Kawalek, Peter last_name: Kawalek - first_name: Boyka full_name: Simeonova, Boyka last_name: Simeonova - first_name: Jörg full_name: Becker, Jörg last_name: Becker - first_name: Katrin full_name: Bergener, Katrin last_name: Bergener - first_name: João Álvaro full_name: Carvalho, João Álvaro last_name: Carvalho - first_name: Marcelo full_name: Fantinato, Marcelo last_name: Fantinato - first_name: Niels F. full_name: Garmann-Johnsen, Niels F. last_name: Garmann-Johnsen - first_name: Christian full_name: Grimme, Christian last_name: Grimme - first_name: Armin full_name: Stein, Armin last_name: Stein - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: 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. International Journal of Information Management. 2021;58. doi:10.1016/j.ijinfomgt.2021.102311 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., & Trautmann, H. (2021). What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective. International Journal of Information Management, 58. https://doi.org/10.1016/j.ijinfomgt.2021.102311 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={10.1016/j.ijinfomgt.2021.102311}, 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} }' 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.” International Journal of Information Management 58 (2021). https://doi.org/10.1016/j.ijinfomgt.2021.102311. ieee: 'C. Coombs et al., “What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective,” International Journal of Information Management, vol. 58, 2021, doi: 10.1016/j.ijinfomgt.2021.102311.' mla: Coombs, Crispin, et al. “What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective.” International Journal of Information Management, vol. 58, 2021, doi:10.1016/j.ijinfomgt.2021.102311. 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). date_created: 2023-08-04T07:27:14Z date_updated: 2023-10-16T12:58:02Z department: - _id: '34' - _id: '819' doi: 10.1016/j.ijinfomgt.2021.102311 intvolume: ' 58' language: - iso: eng publication: International Journal of Information Management status: public title: What Is It About Humanity That We Can’t Give Away To Intelligent Machines? A European Perspective type: journal_article user_id: '15504' volume: 58 year: '2021' ... --- _id: '48853' 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. author: - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Aneta full_name: Neumann, Aneta last_name: Neumann - first_name: Frank full_name: Neumann, Frank last_name: Neumann citation: ama: 'Bossek J, Neumann A, Neumann F. Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms. In: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO ’21. Association for Computing Machinery; 2021:556–564. doi:10.1145/3449639.3459364' apa: Bossek, J., Neumann, A., & Neumann, F. (2021). Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms. Proceedings of the Genetic and Evolutionary Computation Conference, 556–564. https://doi.org/10.1145/3449639.3459364 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={10.1145/3449639.3459364}, 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} }' chicago: 'Bossek, Jakob, Aneta Neumann, and Frank Neumann. “Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms.” In Proceedings of the Genetic and Evolutionary Computation Conference, 556–564. GECCO ’21. New York, NY, USA: Association for Computing Machinery, 2021. https://doi.org/10.1145/3449639.3459364.' ieee: 'J. Bossek, A. Neumann, and F. Neumann, “Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms,” in Proceedings of the Genetic and Evolutionary Computation Conference, 2021, pp. 556–564, doi: 10.1145/3449639.3459364.' mla: Bossek, Jakob, et al. “Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms.” Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, 2021, pp. 556–564, doi:10.1145/3449639.3459364. 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.' date_created: 2023-11-14T15:58:54Z date_updated: 2023-12-13T10:45:22Z department: - _id: '819' doi: 10.1145/3449639.3459364 extern: '1' keyword: - evolutionary algorithms - evolutionary diversity optimization - knapsack problem - tailored operators language: - iso: eng page: 556–564 place: New York, NY, USA publication: Proceedings of the Genetic and Evolutionary Computation Conference publication_identifier: isbn: - 978-1-4503-8350-9 publication_status: published publisher: Association for Computing Machinery series_title: GECCO ’21 status: public title: Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms type: conference user_id: '102979' year: '2021' ... --- _id: '48855' abstract: - lang: eng 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. author: - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Aneta full_name: Neumann, Aneta last_name: Neumann - first_name: Frank full_name: Neumann, Frank last_name: Neumann citation: ama: 'Bossek J, Neumann A, Neumann F. Exact Counting and~Sampling of Optima for the Knapsack Problem. In: Learning and Intelligent Optimization. Springer-Verlag; 2021:40–54. doi:10.1007/978-3-030-92121-7_4' apa: Bossek, J., Neumann, A., & Neumann, F. (2021). Exact Counting and~Sampling of Optima for the Knapsack Problem. Learning and Intelligent Optimization, 40–54. https://doi.org/10.1007/978-3-030-92121-7_4 bibtex: '@inproceedings{Bossek_Neumann_Neumann_2021, place={Berlin, Heidelberg}, title={Exact Counting and~Sampling of Optima for the Knapsack Problem}, DOI={10.1007/978-3-030-92121-7_4}, booktitle={Learning and Intelligent Optimization}, publisher={Springer-Verlag}, author={Bossek, Jakob and Neumann, Aneta and Neumann, Frank}, year={2021}, pages={40–54} }' chicago: 'Bossek, Jakob, Aneta Neumann, and Frank Neumann. “Exact Counting And~Sampling of Optima for the Knapsack Problem.” In Learning and Intelligent Optimization, 40–54. Berlin, Heidelberg: Springer-Verlag, 2021. https://doi.org/10.1007/978-3-030-92121-7_4.' ieee: 'J. Bossek, A. Neumann, and F. Neumann, “Exact Counting and~Sampling of Optima for the Knapsack Problem,” in Learning and Intelligent Optimization, 2021, pp. 40–54, doi: 10.1007/978-3-030-92121-7_4.' mla: Bossek, Jakob, et al. “Exact Counting And~Sampling of Optima for the Knapsack Problem.” Learning and Intelligent Optimization, Springer-Verlag, 2021, pp. 40–54, doi:10.1007/978-3-030-92121-7_4. short: 'J. Bossek, A. Neumann, F. Neumann, in: Learning and Intelligent Optimization, Springer-Verlag, Berlin, Heidelberg, 2021, pp. 40–54.' date_created: 2023-11-14T15:58:54Z date_updated: 2023-12-13T10:45:14Z department: - _id: '819' doi: 10.1007/978-3-030-92121-7_4 extern: '1' keyword: - Dynamic programming - Exact counting - Sampling - Zero-one knapsack problem language: - iso: eng page: 40–54 place: Berlin, Heidelberg publication: Learning and Intelligent Optimization publication_identifier: isbn: - 978-3-030-92120-0 publication_status: published publisher: Springer-Verlag status: public title: Exact Counting and~Sampling of Optima for the Knapsack Problem type: conference user_id: '102979' year: '2021' ... --- _id: '48860' 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. author: - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Frank full_name: Neumann, Frank last_name: Neumann citation: ama: 'Bossek J, Neumann F. Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem. In: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO ’21. Association for Computing Machinery; 2021:198–206. doi:10.1145/3449639.3459363' apa: Bossek, J., & Neumann, F. (2021). Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem. Proceedings of the Genetic and Evolutionary Computation Conference, 198–206. https://doi.org/10.1145/3449639.3459363 bibtex: '@inproceedings{Bossek_Neumann_2021, place={New York, NY, USA}, series={GECCO ’21}, title={Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem}, DOI={10.1145/3449639.3459363}, 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} }' chicago: 'Bossek, Jakob, and Frank Neumann. “Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem.” In Proceedings of the Genetic and Evolutionary Computation Conference, 198–206. GECCO ’21. New York, NY, USA: Association for Computing Machinery, 2021. https://doi.org/10.1145/3449639.3459363.' ieee: 'J. Bossek and F. Neumann, “Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem,” in Proceedings of the Genetic and Evolutionary Computation Conference, 2021, pp. 198–206, doi: 10.1145/3449639.3459363.' mla: Bossek, Jakob, and Frank Neumann. “Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem.” Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, 2021, pp. 198–206, doi:10.1145/3449639.3459363. 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.' date_created: 2023-11-14T15:58:55Z date_updated: 2023-12-13T10:45:37Z department: - _id: '819' doi: 10.1145/3449639.3459363 extern: '1' keyword: - evolutionary algorithms - evolutionary diversity optimization - minimum spanning tree - runtime analysis language: - iso: eng page: 198–206 place: New York, NY, USA publication: Proceedings of the Genetic and Evolutionary Computation Conference publication_identifier: isbn: - 978-1-4503-8350-9 publication_status: published publisher: Association for Computing Machinery series_title: GECCO ’21 status: public title: Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem type: conference user_id: '102979' year: '2021' ... --- _id: '48862' 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.' author: - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Dirk full_name: Sudholt, Dirk last_name: Sudholt citation: ama: 'Bossek J, Sudholt D. Do Additional Optima Speed up Evolutionary Algorithms? In: Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms. Association for Computing Machinery; 2021:1–11.' apa: Bossek, J., & Sudholt, D. (2021). Do Additional Optima Speed up Evolutionary Algorithms? In Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (pp. 1–11). Association for Computing Machinery. 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} }' chicago: 'Bossek, Jakob, and Dirk Sudholt. “Do Additional Optima Speed up Evolutionary Algorithms?” In Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 1–11. New York, NY, USA: Association for Computing Machinery, 2021.' ieee: 'J. Bossek and D. Sudholt, “Do Additional Optima Speed up Evolutionary Algorithms?,” in Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, New York, NY, USA: Association for Computing Machinery, 2021, pp. 1–11.' mla: Bossek, Jakob, and Dirk Sudholt. “Do Additional Optima Speed up Evolutionary Algorithms?” Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 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.' date_created: 2023-11-14T15:58:55Z date_updated: 2023-12-13T10:45:31Z department: - _id: '819' extern: '1' keyword: - evolutionary algorithms - pseudo-boolean functions - runtime analysis - theory language: - iso: eng page: 1–11 place: New York, NY, USA publication: Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms publication_identifier: isbn: - 978-1-4503-8352-3 publication_status: published publisher: Association for Computing Machinery status: public title: Do Additional Optima Speed up Evolutionary Algorithms? type: book_chapter user_id: '102979' year: '2021' ...