--- _id: '60132' author: - first_name: Moritz full_name: Seiler, Moritz id: '105520' last_name: Seiler - first_name: Urban full_name: Skvorc, Urban id: '103764' last_name: Skvorc - first_name: Gjorgjina full_name: Cenikj, Gjorgjina last_name: Cenikj - first_name: Carola full_name: Doerr, Carola last_name: Doerr - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: 'Seiler M, Skvorc U, Cenikj G, Doerr C, Trautmann H. Learned Features vs. Classical ELA on Affine BBOB Functions. In: Affenzeller M, Winkler SM, Kononova AV, et al., eds. Parallel Problem Solving from Nature - PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg, Austria, September 14-18, 2024, Proceedings, Part II. Vol 15149. Lecture Notes in Computer Science. Springer; 2024:137–153. doi:10.1007/978-3-031-70068-2_9' apa: Seiler, M., Skvorc, U., Cenikj, G., Doerr, C., & Trautmann, H. (2024). Learned Features vs. Classical ELA on Affine BBOB Functions. In M. Affenzeller, S. M. Winkler, A. V. Kononova, H. Trautmann, T. Tusar, P. Machado, & T. Bäck (Eds.), Parallel Problem Solving from Nature - PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg, Austria, September 14-18, 2024, Proceedings, Part II (Vol. 15149, pp. 137–153). Springer. https://doi.org/10.1007/978-3-031-70068-2_9 bibtex: '@inproceedings{Seiler_Skvorc_Cenikj_Doerr_Trautmann_2024, series={Lecture Notes in Computer Science}, title={Learned Features vs. Classical ELA on Affine BBOB Functions}, volume={15149}, DOI={10.1007/978-3-031-70068-2_9}, booktitle={Parallel Problem Solving from Nature - PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg, Austria, September 14-18, 2024, Proceedings, Part II}, publisher={Springer}, author={Seiler, Moritz and Skvorc, Urban and Cenikj, Gjorgjina and Doerr, Carola and Trautmann, Heike}, editor={Affenzeller, Michael and Winkler, Stephan M. and Kononova, Anna V. and Trautmann, Heike and Tusar, Tea and Machado, Penousal and Bäck, Thomas}, year={2024}, pages={137–153}, collection={Lecture Notes in Computer Science} }' chicago: Seiler, Moritz, Urban Skvorc, Gjorgjina Cenikj, Carola Doerr, and Heike Trautmann. “Learned Features vs. Classical ELA on Affine BBOB Functions.” In Parallel Problem Solving from Nature - PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg, Austria, September 14-18, 2024, Proceedings, Part II, edited by Michael Affenzeller, Stephan M. Winkler, Anna V. Kononova, Heike Trautmann, Tea Tusar, Penousal Machado, and Thomas Bäck, 15149:137–153. Lecture Notes in Computer Science. Springer, 2024. https://doi.org/10.1007/978-3-031-70068-2_9. ieee: 'M. Seiler, U. Skvorc, G. Cenikj, C. Doerr, and H. Trautmann, “Learned Features vs. Classical ELA on Affine BBOB Functions,” in Parallel Problem Solving from Nature - PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg, Austria, September 14-18, 2024, Proceedings, Part II, 2024, vol. 15149, pp. 137–153, doi: 10.1007/978-3-031-70068-2_9.' mla: Seiler, Moritz, et al. “Learned Features vs. Classical ELA on Affine BBOB Functions.” Parallel Problem Solving from Nature - PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg, Austria, September 14-18, 2024, Proceedings, Part II, edited by Michael Affenzeller et al., vol. 15149, Springer, 2024, pp. 137–153, doi:10.1007/978-3-031-70068-2_9. short: 'M. Seiler, U. Skvorc, G. Cenikj, C. Doerr, H. Trautmann, in: M. Affenzeller, S.M. Winkler, A.V. Kononova, H. Trautmann, T. Tusar, P. Machado, T. Bäck (Eds.), Parallel Problem Solving from Nature - PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg, Austria, September 14-18, 2024, Proceedings, Part II, Springer, 2024, pp. 137–153.' date_created: 2025-06-04T12:48:56Z date_updated: 2025-06-04T12:49:30Z doi: 10.1007/978-3-031-70068-2_9 editor: - first_name: Michael full_name: Affenzeller, Michael last_name: Affenzeller - first_name: Stephan M. full_name: Winkler, Stephan M. last_name: Winkler - first_name: Anna V. full_name: Kononova, Anna V. last_name: Kononova - first_name: Heike full_name: Trautmann, Heike last_name: Trautmann - first_name: Tea full_name: Tusar, Tea last_name: Tusar - first_name: Penousal full_name: Machado, Penousal last_name: Machado - first_name: Thomas full_name: Bäck, Thomas last_name: Bäck intvolume: ' 15149' language: - iso: eng page: 137–153 publication: Parallel Problem Solving from Nature - PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg, Austria, September 14-18, 2024, Proceedings, Part II publisher: Springer series_title: Lecture Notes in Computer Science status: public title: Learned Features vs. Classical ELA on Affine BBOB Functions type: conference user_id: '15504' volume: 15149 year: '2024' ... --- _id: '59283' author: - first_name: Raphael Patrick full_name: Prager, Raphael Patrick last_name: Prager - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: 'Prager RP, Trautmann H. Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python. Evol Comput. 2024;32(3):211–216. doi:10.1162/EVCO_A_00341' apa: 'Prager, R. P., & Trautmann, H. (2024). Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python. Evol. Comput., 32(3), 211–216. https://doi.org/10.1162/EVCO_A_00341' bibtex: '@article{Prager_Trautmann_2024, title={Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python}, volume={32}, DOI={10.1162/EVCO_A_00341}, number={3}, journal={Evol. Comput.}, author={Prager, Raphael Patrick and Trautmann, Heike}, year={2024}, pages={211–216} }' chicago: 'Prager, Raphael Patrick, and Heike Trautmann. “Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python.” Evol. Comput. 32, no. 3 (2024): 211–216. https://doi.org/10.1162/EVCO_A_00341.' ieee: 'R. P. Prager and H. Trautmann, “Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python,” Evol. Comput., vol. 32, no. 3, pp. 211–216, 2024, doi: 10.1162/EVCO_A_00341.' mla: 'Prager, Raphael Patrick, and Heike Trautmann. “Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python.” Evol. Comput., vol. 32, no. 3, 2024, pp. 211–216, doi:10.1162/EVCO_A_00341.' short: R.P. Prager, H. Trautmann, Evol. Comput. 32 (2024) 211–216. date_created: 2025-04-03T05:56:07Z date_updated: 2025-04-03T05:56:33Z doi: 10.1162/EVCO_A_00341 intvolume: ' 32' issue: '3' language: - iso: eng page: 211–216 publication: Evol. Comput. status: public title: 'Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python' type: journal_article user_id: '15504' volume: 32 year: '2024' ... --- _id: '47522' abstract: - lang: eng text: Artificial benchmark functions are commonly used in optimization research because of their ability to rapidly evaluate potential solutions, making them a preferred substitute for real-world problems. However, these benchmark functions have faced criticism for their limited resemblance to real-world problems. In response, recent research has focused on automatically generating new benchmark functions for areas where established test suites are inadequate. These approaches have limitations, such as the difficulty of generating new benchmark functions that exhibit exploratory landscape analysis (ELA) features beyond those of existing benchmarks.The objective of this work is to develop a method for generating benchmark functions for single-objective continuous optimization with user-specified structural properties. Specifically, we aim to demonstrate a proof of concept for a method that uses an ELA feature vector to specify these properties in advance. To achieve this, we begin by generating a random sample of decision space variables and objective values. We then adjust the objective values using CMA-ES until the corresponding features of our new problem match the predefined ELA features within a specified threshold. By iteratively transforming the landscape in this way, we ensure that the resulting function exhibits the desired properties. To create the final function, we use the resulting point cloud as training data for a simple neural network that produces a function exhibiting the target ELA features. We demonstrate the effectiveness of this approach by replicating the existing functions of the well-known BBOB suite and creating new functions with ELA feature values that are not present in BBOB. author: - first_name: Raphael Patrick full_name: Prager, Raphael Patrick last_name: Prager - first_name: Konstantin full_name: Dietrich, Konstantin last_name: Dietrich - first_name: Lennart full_name: Schneider, Lennart last_name: Schneider - first_name: Lennart full_name: Schäpermeier, Lennart last_name: Schäpermeier - first_name: Bernd full_name: Bischl, Bernd last_name: Bischl - 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 - first_name: Olaf full_name: Mersmann, Olaf last_name: Mersmann citation: ama: 'Prager RP, Dietrich K, Schneider L, et al. Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features. In: Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms. FOGA ’23. Association for Computing Machinery; 2023:129–139. doi:10.1145/3594805.3607136' apa: Prager, R. P., Dietrich, K., Schneider, L., Schäpermeier, L., Bischl, B., Kerschke, P., Trautmann, H., & Mersmann, O. (2023). Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features. Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 129–139. https://doi.org/10.1145/3594805.3607136 bibtex: '@inproceedings{Prager_Dietrich_Schneider_Schäpermeier_Bischl_Kerschke_Trautmann_Mersmann_2023, place={New York, NY, USA}, series={FOGA ’23}, title={Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features}, DOI={10.1145/3594805.3607136}, booktitle={Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms}, publisher={Association for Computing Machinery}, author={Prager, Raphael Patrick and Dietrich, Konstantin and Schneider, Lennart and Schäpermeier, Lennart and Bischl, Bernd and Kerschke, Pascal and Trautmann, Heike and Mersmann, Olaf}, year={2023}, pages={129–139}, collection={FOGA ’23} }' chicago: 'Prager, Raphael Patrick, Konstantin Dietrich, Lennart Schneider, Lennart Schäpermeier, Bernd Bischl, Pascal Kerschke, Heike Trautmann, and Olaf Mersmann. “Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features.” In Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 129–139. FOGA ’23. New York, NY, USA: Association for Computing Machinery, 2023. https://doi.org/10.1145/3594805.3607136.' ieee: 'R. P. Prager et al., “Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features,” in Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 2023, pp. 129–139, doi: 10.1145/3594805.3607136.' mla: Prager, Raphael Patrick, et al. “Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features.” Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, Association for Computing Machinery, 2023, pp. 129–139, doi:10.1145/3594805.3607136. short: 'R.P. Prager, K. Dietrich, L. Schneider, L. Schäpermeier, B. Bischl, P. Kerschke, H. Trautmann, O. Mersmann, in: Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, Association for Computing Machinery, New York, NY, USA, 2023, pp. 129–139.' date_created: 2023-09-27T15:43:17Z date_updated: 2023-10-16T12:33:02Z department: - _id: '34' - _id: '819' doi: 10.1145/3594805.3607136 keyword: - Benchmarking - Instance Generator - Black-Box Continuous Optimization - Exploratory Landscape Analysis - Neural Networks language: - iso: eng page: 129–139 place: New York, NY, USA publication: Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms publication_identifier: isbn: - '9798400702020' publisher: Association for Computing Machinery series_title: FOGA ’23 status: public title: Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features type: conference user_id: '15504' year: '2023' ... --- _id: '46297' abstract: - lang: eng text: Exploratory landscape analysis (ELA) in single-objective black-box optimization relies on a comprehensive and large set of numerical features characterizing problem instances. Those foster problem understanding and serve as basis for constructing automated algorithm selection models choosing the best suited algorithm for a problem at hand based on the aforementioned features computed prior to optimization. This work specifically points to the sensitivity of a substantial proportion of these features to absolute objective values, i.e., we observe a lack of shift and scale invariance. We show that this unfortunately induces bias within automated algorithm selection models, an overfitting to specific benchmark problem sets used for training and thereby hinders generalization capabilities to unseen problems. We tackle these issues by presenting an appropriate objective normalization to be used prior to ELA feature computation and empirically illustrate the respective effectiveness focusing on the BBOB benchmark set. author: - first_name: Raphael Patrick full_name: Prager, Raphael Patrick last_name: Prager - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: 'Prager RP, Trautmann H. Nullifying the Inherent Bias of Non-invariant Exploratory Landscape Analysis Features. In: Correia J, Smith S, Qaddoura R, eds. Applications of Evolutionary Computation. Springer Nature Switzerland; 2023:411–425.' apa: Prager, R. P., & Trautmann, H. (2023). Nullifying the Inherent Bias of Non-invariant Exploratory Landscape Analysis Features. In J. Correia, S. Smith, & R. Qaddoura (Eds.), Applications of Evolutionary Computation (pp. 411–425). Springer Nature Switzerland. bibtex: '@inproceedings{Prager_Trautmann_2023, place={Cham}, title={Nullifying the Inherent Bias of Non-invariant Exploratory Landscape Analysis Features}, booktitle={Applications of Evolutionary Computation}, publisher={Springer Nature Switzerland}, author={Prager, Raphael Patrick and Trautmann, Heike}, editor={Correia, João and Smith, Stephen and Qaddoura, Raneem}, year={2023}, pages={411–425} }' chicago: 'Prager, Raphael Patrick, and Heike Trautmann. “Nullifying the Inherent Bias of Non-Invariant Exploratory Landscape Analysis Features.” In Applications of Evolutionary Computation, edited by João Correia, Stephen Smith, and Raneem Qaddoura, 411–425. Cham: Springer Nature Switzerland, 2023.' ieee: R. P. Prager and H. Trautmann, “Nullifying the Inherent Bias of Non-invariant Exploratory Landscape Analysis Features,” in Applications of Evolutionary Computation, 2023, pp. 411–425. mla: Prager, Raphael Patrick, and Heike Trautmann. “Nullifying the Inherent Bias of Non-Invariant Exploratory Landscape Analysis Features.” Applications of Evolutionary Computation, edited by João Correia et al., Springer Nature Switzerland, 2023, pp. 411–425. short: 'R.P. Prager, H. Trautmann, in: J. Correia, S. Smith, R. Qaddoura (Eds.), Applications of Evolutionary Computation, Springer Nature Switzerland, Cham, 2023, pp. 411–425.' date_created: 2023-08-04T06:54:22Z date_updated: 2023-10-16T12:36:45Z department: - _id: '819' - _id: '34' editor: - first_name: João full_name: Correia, João last_name: Correia - first_name: Stephen full_name: Smith, Stephen last_name: Smith - first_name: Raneem full_name: Qaddoura, Raneem last_name: Qaddoura language: - iso: eng page: 411–425 place: Cham publication: Applications of Evolutionary Computation publication_identifier: isbn: - 978-3-031-30229-9 publisher: Springer Nature Switzerland status: public title: Nullifying the Inherent Bias of Non-invariant Exploratory Landscape Analysis Features type: conference user_id: '15504' year: '2023' ... --- _id: '46298' abstract: - lang: eng text: The design and choice of benchmark suites are ongoing topics of discussion in the multi-objective optimization community. Some suites provide a good understanding of their Pareto sets and fronts, such as the well-known DTLZ and ZDT problems. However, they lack diversity in their landscape properties and do not provide a mechanism for creating multiple distinct problem instances. Other suites, like bi-objective BBOB, possess diverse and challenging landscape properties, but their optima are not well understood and can only be approximated empirically without any guarantees. author: - first_name: Lennart full_name: Schäpermeier, Lennart last_name: Schäpermeier - first_name: Pascal full_name: Kerschke, Pascal last_name: Kerschke - first_name: Christian full_name: Grimme, Christian last_name: Grimme - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: 'Schäpermeier L, Kerschke P, Grimme C, Trautmann H. Peak-A-Boo! Generating Multi-objective Multiple Peaks Benchmark Problems with Precise Pareto Sets. In: Emmerich M, Deutz A, Wang H, et al., eds. Evolutionary Multi-Criterion Optimization. Springer Nature Switzerland; 2023:291–304.' apa: Schäpermeier, L., Kerschke, P., Grimme, C., & Trautmann, H. (2023). Peak-A-Boo! Generating Multi-objective Multiple Peaks Benchmark Problems with Precise Pareto Sets. In M. Emmerich, A. Deutz, H. Wang, A. V. Kononova, B. Naujoks, K. Li, K. Miettinen, & I. Yevseyeva (Eds.), Evolutionary Multi-Criterion Optimization (pp. 291–304). Springer Nature Switzerland. bibtex: '@inproceedings{Schäpermeier_Kerschke_Grimme_Trautmann_2023, place={Cham}, title={Peak-A-Boo! Generating Multi-objective Multiple Peaks Benchmark Problems with Precise Pareto Sets}, booktitle={Evolutionary Multi-Criterion Optimization}, publisher={Springer Nature Switzerland}, author={Schäpermeier, Lennart and Kerschke, Pascal and Grimme, Christian and Trautmann, Heike}, editor={Emmerich, Michael and Deutz, André and Wang, Hao and Kononova, Anna V. and Naujoks, Boris and Li, Ke and Miettinen, Kaisa and Yevseyeva, Iryna}, year={2023}, pages={291–304} }' chicago: 'Schäpermeier, Lennart, Pascal Kerschke, Christian Grimme, and Heike Trautmann. “Peak-A-Boo! Generating Multi-Objective Multiple Peaks Benchmark Problems with Precise Pareto Sets.” In Evolutionary Multi-Criterion Optimization, edited by Michael Emmerich, André Deutz, Hao Wang, Anna V. Kononova, Boris Naujoks, Ke Li, Kaisa Miettinen, and Iryna Yevseyeva, 291–304. Cham: Springer Nature Switzerland, 2023.' ieee: L. Schäpermeier, P. Kerschke, C. Grimme, and H. Trautmann, “Peak-A-Boo! Generating Multi-objective Multiple Peaks Benchmark Problems with Precise Pareto Sets,” in Evolutionary Multi-Criterion Optimization, 2023, pp. 291–304. mla: Schäpermeier, Lennart, et al. “Peak-A-Boo! Generating Multi-Objective Multiple Peaks Benchmark Problems with Precise Pareto Sets.” Evolutionary Multi-Criterion Optimization, edited by Michael Emmerich et al., Springer Nature Switzerland, 2023, pp. 291–304. short: 'L. Schäpermeier, P. Kerschke, C. Grimme, H. Trautmann, in: M. Emmerich, A. Deutz, H. Wang, A.V. Kononova, B. Naujoks, K. Li, K. Miettinen, I. Yevseyeva (Eds.), Evolutionary Multi-Criterion Optimization, Springer Nature Switzerland, Cham, 2023, pp. 291–304.' date_created: 2023-08-04T06:56:10Z date_updated: 2023-10-16T12:36:17Z department: - _id: '819' - _id: '34' editor: - first_name: Michael full_name: Emmerich, Michael last_name: Emmerich - first_name: André full_name: Deutz, André last_name: Deutz - first_name: Hao full_name: Wang, Hao last_name: Wang - first_name: Anna V. full_name: Kononova, Anna V. last_name: Kononova - first_name: Boris full_name: Naujoks, Boris last_name: Naujoks - first_name: Ke full_name: Li, Ke last_name: Li - first_name: Kaisa full_name: Miettinen, Kaisa last_name: Miettinen - first_name: Iryna full_name: Yevseyeva, Iryna last_name: Yevseyeva language: - iso: eng page: 291–304 place: Cham publication: Evolutionary Multi-Criterion Optimization publication_identifier: isbn: - 978-3-031-27250-9 publisher: Springer Nature Switzerland status: public title: Peak-A-Boo! Generating Multi-objective Multiple Peaks Benchmark Problems with Precise Pareto Sets type: conference user_id: '15504' year: '2023' ... --- _id: '46299' abstract: - lang: eng text: The herein proposed Python package pflacco provides a set of numerical features to characterize single-objective continuous and constrained optimization problems. Thereby, pflacco addresses two major challenges in the area optimization. Firstly, it provides the means to develop an understanding of a given problem instance, which is crucial for designing, selecting, or configuring optimization algorithms in general. Secondly, these numerical features can be utilized in the research streams of automated algorithm selection and configuration. While the majority of these landscape features is already available in the R package flacco, our Python implementation offers these tools to an even wider audience and thereby promotes research interests and novel avenues in the area of optimization. author: - first_name: Raphael Patrick full_name: Prager, Raphael Patrick last_name: Prager - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: 'Prager RP, Trautmann H. Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python. Evolutionary Computation. Published online 2023:1–25. doi:10.1162/evco_a_00341' apa: 'Prager, R. P., & Trautmann, H. (2023). Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python. Evolutionary Computation, 1–25. https://doi.org/10.1162/evco_a_00341' bibtex: '@article{Prager_Trautmann_2023, title={Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python}, DOI={10.1162/evco_a_00341}, journal={Evolutionary Computation}, author={Prager, Raphael Patrick and Trautmann, Heike}, year={2023}, pages={1–25} }' chicago: 'Prager, Raphael Patrick, and Heike Trautmann. “Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python.” Evolutionary Computation, 2023, 1–25. https://doi.org/10.1162/evco_a_00341.' ieee: 'R. P. Prager and H. Trautmann, “Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python,” Evolutionary Computation, pp. 1–25, 2023, doi: 10.1162/evco_a_00341.' mla: 'Prager, Raphael Patrick, and Heike Trautmann. “Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python.” Evolutionary Computation, 2023, pp. 1–25, doi:10.1162/evco_a_00341.' short: R.P. Prager, H. Trautmann, Evolutionary Computation (2023) 1–25. date_created: 2023-08-04T07:01:33Z date_updated: 2023-10-16T12:35:56Z department: - _id: '819' - _id: '34' doi: 10.1162/evco_a_00341 language: - iso: eng page: 1–25 publication: Evolutionary Computation publication_identifier: issn: - 1063-6560 status: public title: 'Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python' type: journal_article user_id: '15504' year: '2023' ... --- _id: '52530' author: - first_name: Raphael Patrick full_name: Prager, Raphael Patrick last_name: Prager - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: 'Prager RP, Trautmann H. Investigating the Viability of Existing Exploratory Landscape Analysis Features for Mixed-Integer Problems. In: Silva S, Paquete L, eds. Companion Proceedings of the Conference on Genetic and Evolutionary Computation, GECCO 2023, Companion Volume, Lisbon, Portugal, July 15-19, 2023. ACM; 2023:451–454. doi:10.1145/3583133.3590757' apa: Prager, R. P., & Trautmann, H. (2023). Investigating the Viability of Existing Exploratory Landscape Analysis Features for Mixed-Integer Problems. In S. Silva & L. Paquete (Eds.), Companion Proceedings of the Conference on Genetic and Evolutionary Computation, GECCO 2023, Companion Volume, Lisbon, Portugal, July 15-19, 2023 (pp. 451–454). ACM. https://doi.org/10.1145/3583133.3590757 bibtex: '@inproceedings{Prager_Trautmann_2023, title={Investigating the Viability of Existing Exploratory Landscape Analysis Features for Mixed-Integer Problems}, DOI={10.1145/3583133.3590757}, booktitle={Companion Proceedings of the Conference on Genetic and Evolutionary Computation, GECCO 2023, Companion Volume, Lisbon, Portugal, July 15-19, 2023}, publisher={ACM}, author={Prager, Raphael Patrick and Trautmann, Heike}, editor={Silva, Sara and Paquete, Luís}, year={2023}, pages={451–454} }' chicago: Prager, Raphael Patrick, and Heike Trautmann. “Investigating the Viability of Existing Exploratory Landscape Analysis Features for Mixed-Integer Problems.” In Companion Proceedings of the Conference on Genetic and Evolutionary Computation, GECCO 2023, Companion Volume, Lisbon, Portugal, July 15-19, 2023, edited by Sara Silva and Luís Paquete, 451–454. ACM, 2023. https://doi.org/10.1145/3583133.3590757. ieee: 'R. P. Prager and H. Trautmann, “Investigating the Viability of Existing Exploratory Landscape Analysis Features for Mixed-Integer Problems,” in Companion Proceedings of the Conference on Genetic and Evolutionary Computation, GECCO 2023, Companion Volume, Lisbon, Portugal, July 15-19, 2023, 2023, pp. 451–454, doi: 10.1145/3583133.3590757.' mla: Prager, Raphael Patrick, and Heike Trautmann. “Investigating the Viability of Existing Exploratory Landscape Analysis Features for Mixed-Integer Problems.” Companion Proceedings of the Conference on Genetic and Evolutionary Computation, GECCO 2023, Companion Volume, Lisbon, Portugal, July 15-19, 2023, edited by Sara Silva and Luís Paquete, ACM, 2023, pp. 451–454, doi:10.1145/3583133.3590757. short: 'R.P. Prager, H. Trautmann, in: S. Silva, L. Paquete (Eds.), Companion Proceedings of the Conference on Genetic and Evolutionary Computation, GECCO 2023, Companion Volume, Lisbon, Portugal, July 15-19, 2023, ACM, 2023, pp. 451–454.' date_created: 2024-03-13T09:55:17Z date_updated: 2024-03-13T10:28:07Z department: - _id: '819' doi: 10.1145/3583133.3590757 editor: - first_name: Sara full_name: Silva, Sara last_name: Silva - first_name: Luís full_name: Paquete, Luís last_name: Paquete language: - iso: eng page: 451–454 publication: Companion Proceedings of the Conference on Genetic and Evolutionary Computation, GECCO 2023, Companion Volume, Lisbon, Portugal, July 15-19, 2023 publisher: ACM status: public title: Investigating the Viability of Existing Exploratory Landscape Analysis Features for Mixed-Integer Problems type: conference user_id: '15504' year: '2023' ... --- _id: '46310' abstract: - lang: eng 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) nearest neighbor relationships of the input instance. To this end we theoretically derive minimum and maximum values for properties of MSTs and k-nearest neighbor graphs (NNG) 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. A proof-of-concept AS-study shows promising results: models trained with normalized features tend to outperform those trained with the respective vanilla features.' author: - first_name: Jonathan full_name: Heins, Jonathan last_name: Heins - first_name: Jakob full_name: Bossek, Jakob id: '102979' last_name: Bossek orcid: 0000-0002-4121-4668 - first_name: Janina full_name: Pohl, Janina last_name: Pohl - 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: Heins J, Bossek J, Pohl J, Seiler M, Trautmann H, Kerschke P. A study on the effects of normalized TSP features for automated algorithm selection. Theoretical Computer Science. 2023;940:123-145. doi:https://doi.org/10.1016/j.tcs.2022.10.019 apa: Heins, J., Bossek, J., Pohl, J., Seiler, M., Trautmann, H., & Kerschke, P. (2023). A study on the effects of normalized TSP features for automated algorithm selection. Theoretical Computer Science, 940, 123–145. https://doi.org/10.1016/j.tcs.2022.10.019 bibtex: '@article{Heins_Bossek_Pohl_Seiler_Trautmann_Kerschke_2023, title={A study on the effects of normalized TSP features for automated algorithm selection}, volume={940}, DOI={https://doi.org/10.1016/j.tcs.2022.10.019}, journal={Theoretical Computer Science}, author={Heins, Jonathan and Bossek, Jakob and Pohl, Janina and Seiler, Moritz and Trautmann, Heike and Kerschke, Pascal}, year={2023}, pages={123–145} }' chicago: 'Heins, Jonathan, Jakob Bossek, Janina Pohl, Moritz Seiler, Heike Trautmann, and Pascal Kerschke. “A Study on the Effects of Normalized TSP Features for Automated Algorithm Selection.” Theoretical Computer Science 940 (2023): 123–45. https://doi.org/10.1016/j.tcs.2022.10.019.' ieee: 'J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, and P. Kerschke, “A study on the effects of normalized TSP features for automated algorithm selection,” Theoretical Computer Science, vol. 940, pp. 123–145, 2023, doi: https://doi.org/10.1016/j.tcs.2022.10.019.' mla: Heins, Jonathan, et al. “A Study on the Effects of Normalized TSP Features for Automated Algorithm Selection.” Theoretical Computer Science, vol. 940, 2023, pp. 123–45, doi:https://doi.org/10.1016/j.tcs.2022.10.019. short: J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, P. Kerschke, Theoretical Computer Science 940 (2023) 123–145. date_created: 2023-08-04T07:18:38Z date_updated: 2024-06-10T11:57:21Z department: - _id: '34' - _id: '819' doi: https://doi.org/10.1016/j.tcs.2022.10.019 intvolume: ' 940' keyword: - Feature normalization - Algorithm selection - Traveling salesperson problem language: - iso: eng page: 123-145 publication: Theoretical Computer Science publication_identifier: issn: - 0304-3975 status: public title: A study on the effects of normalized TSP features for automated algorithm selection type: journal_article user_id: '15504' volume: 940 year: '2023' ... --- _id: '48898' abstract: - lang: eng text: 'Automated Algorithm Configuration (AAC) usually takes a global perspective: it identifies a parameter configuration for an (optimization) algorithm that maximizes a performance metric over a set of instances. However, the optimal choice of parameters strongly depends on the instance at hand and should thus be calculated on a per-instance basis. We explore the potential of Per-Instance Algorithm Configuration (PIAC) by using Reinforcement Learning (RL). To this end, we propose a novel PIAC approach that is based on deep neural networks. We apply it to predict configurations for the Lin\textendash Kernighan heuristic (LKH) for the Traveling Salesperson Problem (TSP) individually for every single instance. To train our PIAC approach, we create a large set of 100000 TSP instances with 2000 nodes each \textemdash currently the largest benchmark set to the best of our knowledge. We compare our approach to the state-of-the-art AAC method Sequential Model-based Algorithm Configuration (SMAC). The results show that our PIAC approach outperforms this baseline on both the newly created instance set and established instance sets.' author: - first_name: Moritz full_name: Seiler, Moritz id: '105520' last_name: Seiler - first_name: Jeroen full_name: Rook, Jeroen last_name: Rook - first_name: Jonathan full_name: Heins, Jonathan last_name: Heins - first_name: Oliver Ludger full_name: Preuß, Oliver Ludger id: '102978' last_name: Preuß orcid: 0009-0008-9308-2418 - 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: 'Seiler M, Rook J, Heins J, Preuß OL, Bossek J, Trautmann H. Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP. In: 2023 IEEE Symposium Series on Computational Intelligence (SSCI). ; :361-368. doi:10.1109/SSCI52147.2023.10372008' apa: Seiler, M., Rook, J., Heins, J., Preuß, O. L., Bossek, J., & Trautmann, H. (n.d.). Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP. 2023 IEEE Symposium Series on Computational Intelligence (SSCI), 361–368. https://doi.org/10.1109/SSCI52147.2023.10372008 bibtex: '@inproceedings{Seiler_Rook_Heins_Preuß_Bossek_Trautmann, title={Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP}, DOI={10.1109/SSCI52147.2023.10372008}, booktitle={2023 IEEE Symposium Series on Computational Intelligence (SSCI)}, author={Seiler, Moritz and Rook, Jeroen and Heins, Jonathan and Preuß, Oliver Ludger and Bossek, Jakob and Trautmann, Heike}, pages={361–368} }' chicago: Seiler, Moritz, Jeroen Rook, Jonathan Heins, Oliver Ludger Preuß, Jakob Bossek, and Heike Trautmann. “Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP.” In 2023 IEEE Symposium Series on Computational Intelligence (SSCI), 361–68, n.d. https://doi.org/10.1109/SSCI52147.2023.10372008. ieee: 'M. Seiler, J. Rook, J. Heins, O. L. Preuß, J. Bossek, and H. Trautmann, “Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP,” in 2023 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 361–368, doi: 10.1109/SSCI52147.2023.10372008.' mla: Seiler, Moritz, et al. “Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP.” 2023 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 361–68, doi:10.1109/SSCI52147.2023.10372008. short: 'M. Seiler, J. Rook, J. Heins, O.L. Preuß, J. Bossek, H. Trautmann, in: 2023 IEEE Symposium Series on Computational Intelligence (SSCI), n.d., pp. 361–368.' date_created: 2023-11-14T15:59:01Z date_updated: 2024-06-10T11:56:58Z department: - _id: '819' doi: 10.1109/SSCI52147.2023.10372008 extern: '1' language: - iso: eng page: 361 - 368 publication: 2023 IEEE Symposium Series on Computational Intelligence (SSCI) publication_status: accepted status: public title: Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP type: conference user_id: '15504' year: '2023' ... --- _id: '46300' author: - first_name: Marco full_name: Niemann, Marco last_name: Niemann - first_name: Dennis full_name: Assenmacher, Dennis last_name: Assenmacher - first_name: Jens full_name: Brunk, Jens last_name: Brunk - first_name: Dennis Maximilian full_name: Riehle, Dennis Maximilian last_name: Riehle - first_name: Jörg full_name: Becker, Jörg last_name: Becker - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: 'Niemann M, Assenmacher D, Brunk J, Riehle DM, Becker J, Trautmann H. (Semi-)Automatische Kommentarmoderation zur Erhaltung Konstruktiver Diskurse. In: Weitzel G, Mündges S, eds. Hate Speech — Definitionen, Ausprägungen, Lösungen. VS Verlag für Sozialwissenschaften; 2022:249–274. doi:10.1007/978-3-658-35658-3_13' apa: Niemann, M., Assenmacher, D., Brunk, J., Riehle, D. M., Becker, J., & Trautmann, H. (2022). (Semi-)Automatische Kommentarmoderation zur Erhaltung Konstruktiver Diskurse. In G. Weitzel & S. Mündges (Eds.), Hate Speech — Definitionen, Ausprägungen, Lösungen (pp. 249–274). VS Verlag für Sozialwissenschaften. https://doi.org/10.1007/978-3-658-35658-3_13 bibtex: '@inbook{Niemann_Assenmacher_Brunk_Riehle_Becker_Trautmann_2022, place={Wiesbaden}, title={(Semi-)Automatische Kommentarmoderation zur Erhaltung Konstruktiver Diskurse}, DOI={10.1007/978-3-658-35658-3_13}, booktitle={Hate Speech — Definitionen, Ausprägungen, Lösungen}, publisher={VS Verlag für Sozialwissenschaften}, author={Niemann, Marco and Assenmacher, Dennis and Brunk, Jens and Riehle, Dennis Maximilian and Becker, Jörg and Trautmann, Heike}, editor={Weitzel, Gerrit and Mündges, Stephan}, year={2022}, pages={249–274} }' chicago: 'Niemann, Marco, Dennis Assenmacher, Jens Brunk, Dennis Maximilian Riehle, Jörg Becker, and Heike Trautmann. “(Semi-)Automatische Kommentarmoderation Zur Erhaltung Konstruktiver Diskurse.” In Hate Speech — Definitionen, Ausprägungen, Lösungen, edited by Gerrit Weitzel and Stephan Mündges, 249–274. Wiesbaden: VS Verlag für Sozialwissenschaften, 2022. https://doi.org/10.1007/978-3-658-35658-3_13.' ieee: 'M. Niemann, D. Assenmacher, J. Brunk, D. M. Riehle, J. Becker, and H. Trautmann, “(Semi-)Automatische Kommentarmoderation zur Erhaltung Konstruktiver Diskurse,” in Hate Speech — Definitionen, Ausprägungen, Lösungen, G. Weitzel and S. Mündges, Eds. Wiesbaden: VS Verlag für Sozialwissenschaften, 2022, pp. 249–274.' mla: Niemann, Marco, et al. “(Semi-)Automatische Kommentarmoderation Zur Erhaltung Konstruktiver Diskurse.” Hate Speech — Definitionen, Ausprägungen, Lösungen, edited by Gerrit Weitzel and Stephan Mündges, VS Verlag für Sozialwissenschaften, 2022, pp. 249–274, doi:10.1007/978-3-658-35658-3_13. short: 'M. Niemann, D. Assenmacher, J. Brunk, D.M. Riehle, J. Becker, H. Trautmann, in: G. Weitzel, S. Mündges (Eds.), Hate Speech — Definitionen, Ausprägungen, Lösungen, VS Verlag für Sozialwissenschaften, Wiesbaden, 2022, pp. 249–274.' date_created: 2023-08-04T07:03:47Z date_updated: 2023-10-16T12:35:41Z department: - _id: '819' - _id: '34' doi: 10.1007/978-3-658-35658-3_13 editor: - first_name: Gerrit full_name: Weitzel, Gerrit last_name: Weitzel - first_name: Stephan full_name: Mündges, Stephan last_name: Mündges language: - iso: eng page: 249–274 place: Wiesbaden publication: Hate Speech — Definitionen, Ausprägungen, Lösungen publication_identifier: isbn: - 978-3-658-35658-3 publisher: VS Verlag für Sozialwissenschaften status: public title: (Semi-)Automatische Kommentarmoderation zur Erhaltung Konstruktiver Diskurse type: book_chapter user_id: '15504' year: '2022' ... --- _id: '46301' author: - first_name: D full_name: Assenmacher, D last_name: Assenmacher - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 citation: ama: 'Assenmacher D, Trautmann H. Textual One-Pass Stream Clustering with Automated Distance Threshold Adaption. In: et al. Tran T, ed. Intelligent Information and Database Systems. Springer International Publishing; 2022:3–16. doi:10.1007/978-3-031-21743-2_1' apa: Assenmacher, D., & Trautmann, H. (2022). Textual One-Pass Stream Clustering with Automated Distance Threshold Adaption. In T. et al. Tran (Ed.), Intelligent Information and Database Systems (pp. 3–16). Springer International Publishing. https://doi.org/10.1007/978-3-031-21743-2_1 bibtex: '@inproceedings{Assenmacher_Trautmann_2022, place={Cham}, title={Textual One-Pass Stream Clustering with Automated Distance Threshold Adaption}, DOI={10.1007/978-3-031-21743-2_1}, booktitle={Intelligent Information and Database Systems}, publisher={Springer International Publishing}, author={Assenmacher, D and Trautmann, Heike}, editor={et al. Tran, T}, year={2022}, pages={3–16} }' chicago: 'Assenmacher, D, and Heike Trautmann. “Textual One-Pass Stream Clustering with Automated Distance Threshold Adaption.” In Intelligent Information and Database Systems, edited by T et al. Tran, 3–16. Cham: Springer International Publishing, 2022. https://doi.org/10.1007/978-3-031-21743-2_1.' ieee: 'D. Assenmacher and H. Trautmann, “Textual One-Pass Stream Clustering with Automated Distance Threshold Adaption,” in Intelligent Information and Database Systems, 2022, pp. 3–16, doi: 10.1007/978-3-031-21743-2_1.' mla: Assenmacher, D., and Heike Trautmann. “Textual One-Pass Stream Clustering with Automated Distance Threshold Adaption.” Intelligent Information and Database Systems, edited by T et al. Tran, Springer International Publishing, 2022, pp. 3–16, doi:10.1007/978-3-031-21743-2_1. short: 'D. Assenmacher, H. Trautmann, in: T. et al. Tran (Ed.), Intelligent Information and Database Systems, Springer International Publishing, Cham, 2022, pp. 3–16.' date_created: 2023-08-04T07:04:54Z date_updated: 2023-10-16T12:35:22Z department: - _id: '819' - _id: '34' doi: 10.1007/978-3-031-21743-2_1 editor: - first_name: T full_name: et al. Tran, T last_name: et al. Tran language: - iso: eng page: 3–16 place: Cham publication: Intelligent Information and Database Systems publisher: Springer International Publishing status: public title: Textual One-Pass Stream Clustering with Automated Distance Threshold Adaption type: conference user_id: '15504' year: '2022' ... --- _id: '46316' abstract: - lang: eng text: ' Computational social science uses computational and statistical methods in order to evaluate social interaction. The public availability of data sets is thus a necessary precondition for reliable and replicable research. These data allow researchers to benchmark the computational methods they develop, test the generalizability of their findings, and build confidence in their results. When social media data are concerned, data sharing is often restricted for legal or privacy reasons, which makes the comparison of methods and the replicability of research results infeasible. Social media analytics research, consequently, faces an integrity crisis. How is it possible to create trust in computational or statistical analyses, when they cannot be validated by third parties? In this work, we explore this well-known, yet little discussed, problem for social media analytics. We investigate how this problem can be solved by looking at related computational research areas. Moreover, we propose and implement a prototype to address the problem in the form of a new evaluation framework that enables the comparison of algorithms without the need to exchange data directly, while maintaining flexibility for the algorithm design. ' author: - first_name: Dennis full_name: Assenmacher, Dennis last_name: Assenmacher - first_name: Derek full_name: Weber, Derek last_name: Weber - first_name: Mike full_name: Preuss, Mike last_name: Preuss - first_name: André Calero full_name: Valdez, André Calero last_name: Valdez - first_name: Alison full_name: Bradshaw, Alison last_name: Bradshaw - first_name: Björn full_name: Ross, Björn last_name: Ross - first_name: Stefano full_name: Cresci, Stefano last_name: Cresci - first_name: Heike full_name: Trautmann, Heike id: '100740' last_name: Trautmann orcid: 0000-0002-9788-8282 - first_name: Frank full_name: Neumann, Frank last_name: Neumann - first_name: Christian full_name: Grimme, Christian last_name: Grimme citation: ama: 'Assenmacher D, Weber D, Preuss M, et al. Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem. Social Science Computer Review. 2022;40(6):1496-1522. doi:10.1177/08944393211012268' apa: 'Assenmacher, D., Weber, D., Preuss, M., Valdez, A. C., Bradshaw, A., Ross, B., Cresci, S., Trautmann, H., Neumann, F., & Grimme, C. (2022). Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem. Social Science Computer Review, 40(6), 1496–1522. https://doi.org/10.1177/08944393211012268' bibtex: '@article{Assenmacher_Weber_Preuss_Valdez_Bradshaw_Ross_Cresci_Trautmann_Neumann_Grimme_2022, title={Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem}, volume={40}, DOI={10.1177/08944393211012268}, number={6}, journal={Social Science Computer Review}, author={Assenmacher, Dennis and Weber, Derek and Preuss, Mike and Valdez, André Calero and Bradshaw, Alison and Ross, Björn and Cresci, Stefano and Trautmann, Heike and Neumann, Frank and Grimme, Christian}, year={2022}, pages={1496–1522} }' chicago: 'Assenmacher, Dennis, Derek Weber, Mike Preuss, André Calero Valdez, Alison Bradshaw, Björn Ross, Stefano Cresci, Heike Trautmann, Frank Neumann, and Christian Grimme. “Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem.” Social Science Computer Review 40, no. 6 (2022): 1496–1522. https://doi.org/10.1177/08944393211012268.' ieee: 'D. Assenmacher et al., “Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem,” Social Science Computer Review, vol. 40, no. 6, pp. 1496–1522, 2022, doi: 10.1177/08944393211012268.' mla: 'Assenmacher, Dennis, et al. “Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem.” Social Science Computer Review, vol. 40, no. 6, 2022, pp. 1496–522, doi:10.1177/08944393211012268.' short: D. Assenmacher, D. Weber, M. Preuss, A.C. Valdez, A. Bradshaw, B. Ross, S. Cresci, H. Trautmann, F. Neumann, C. Grimme, Social Science Computer Review 40 (2022) 1496–1522. date_created: 2023-08-04T07:26:36Z date_updated: 2023-10-16T12:57:24Z department: - _id: '34' - _id: '819' doi: 10.1177/08944393211012268 intvolume: ' 40' issue: '6' language: - iso: eng page: 1496-1522 publication: Social Science Computer Review status: public title: 'Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem' type: journal_article user_id: '15504' volume: 40 year: '2022' ... --- _id: '46306' abstract: - lang: eng text: Hyperparameter optimization (HPO) is a key component of machine learning models for achieving peak predictive performance. While numerous methods and algorithms for HPO have been proposed over the last years, little progress has been made in illuminating and examining the actual structure of these black-box optimization problems. Exploratory landscape analysis (ELA) subsumes a set of techniques that can be used to gain knowledge about properties of unknown optimization problems. In this paper, we evaluate the performance of five different black-box optimizers on 30 HPO problems, which consist of two-, three- and five-dimensional continuous search spaces of the XGBoost learner trained on 10 different data sets. This is contrasted with the performance of the same optimizers evaluated on 360 problem instances from the black-box optimization benchmark (BBOB). We then compute ELA features on the HPO and BBOB problems and examine similarities and differences. A cluster analysis of the HPO and BBOB problems in ELA feature space allows us to identify how the HPO problems compare to the BBOB problems on a structural meta-level. We identify a subset of BBOB problems that are close to the HPO problems in ELA feature space and show that optimizer performance is comparably similar on these two sets of benchmark problems. We highlight open challenges of ELA for HPO and discuss potential directions of future research and applications. author: - first_name: Lennart full_name: Schneider, Lennart last_name: Schneider - first_name: Lennart full_name: Schäpermeier, Lennart last_name: Schäpermeier - first_name: Raphael Patrick full_name: Prager, Raphael Patrick last_name: Prager - first_name: Bernd full_name: Bischl, Bernd last_name: Bischl - 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: 'Schneider L, Schäpermeier L, Prager RP, Bischl B, Trautmann H, Kerschke P. HPO x ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis. In: Rudolph G, Kononova AV, Aguirre H, Kerschke P, Ochoa G, Tušar T, eds. Parallel Problem Solving from Nature — PPSN XVII. Springer International Publishing; 2022:575–589. doi:10.1007/978-3-031-14714-2_40' apa: 'Schneider, L., Schäpermeier, L., Prager, R. P., Bischl, B., Trautmann, H., & Kerschke, P. (2022). HPO x ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis. In G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, & T. Tušar (Eds.), Parallel Problem Solving from Nature — PPSN XVII (pp. 575–589). Springer International Publishing. https://doi.org/10.1007/978-3-031-14714-2_40' bibtex: '@inproceedings{Schneider_Schäpermeier_Prager_Bischl_Trautmann_Kerschke_2022, place={Cham}, title={HPO x ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis}, DOI={10.1007/978-3-031-14714-2_40}, booktitle={Parallel Problem Solving from Nature — PPSN XVII}, publisher={Springer International Publishing}, author={Schneider, Lennart and Schäpermeier, Lennart and Prager, Raphael Patrick and Bischl, Bernd 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={575–589} }' chicago: 'Schneider, Lennart, Lennart Schäpermeier, Raphael Patrick Prager, Bernd Bischl, Heike Trautmann, and Pascal Kerschke. “HPO x ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis.” In Parallel Problem Solving from Nature — PPSN XVII, edited by Günter Rudolph, Anna V. Kononova, Hernán Aguirre, Pascal Kerschke, Gabriela Ochoa, and Tea Tušar, 575–589. Cham: Springer International Publishing, 2022. https://doi.org/10.1007/978-3-031-14714-2_40.' ieee: 'L. Schneider, L. Schäpermeier, R. P. Prager, B. Bischl, H. Trautmann, and P. Kerschke, “HPO x ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis,” in Parallel Problem Solving from Nature — PPSN XVII, 2022, pp. 575–589, doi: 10.1007/978-3-031-14714-2_40.' mla: 'Schneider, Lennart, et al. “HPO x ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis.” Parallel Problem Solving from Nature — PPSN XVII, edited by Günter Rudolph et al., Springer International Publishing, 2022, pp. 575–589, doi:10.1007/978-3-031-14714-2_40.' short: 'L. Schneider, L. Schäpermeier, R.P. Prager, B. Bischl, 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. 575–589.' date_created: 2023-08-04T07:15:16Z date_updated: 2023-10-16T12:51:27Z department: - _id: '34' - _id: '819' doi: 10.1007/978-3-031-14714-2_40 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: 575–589 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: 'HPO x ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis' type: conference user_id: '15504' year: '2022' ... --- _id: '46308' abstract: - lang: eng text: Single-objective continuous optimization can be challenging, especially when dealing with multimodal problems. This work sheds light on the effects that multi-objective optimization may have in the single-objective space. For this purpose, we examine the inner mechanisms of the recently developed sophisticated local search procedure SOMOGSA. This method solves multimodal single-objective continuous optimization problems based on first expanding the problem with an additional objective (e.g., a sphere function) to the bi-objective domain and subsequently exploiting local structures of the resulting landscapes. Our study particularly focuses on the sensitivity of this multiobjectivization approach w.r.t. (1) the parametrization of the artificial second objective, as well as (2) the position of the initial starting points in the search space. As SOMOGSA is a modular framework for encapsulating local search, we integrate Nelder–Mead local search as optimizer in the respective module and compare the performance of the resulting hybrid local search to its original single-objective counterpart. We show that the SOMOGSA framework can significantly boost local search by multiobjectivization. Hence, combined with more sophisticated local search and metaheuristics, this may help solve highly multimodal optimization problems in the future. author: - first_name: Pelin full_name: Aspar, Pelin last_name: Aspar - first_name: Vera full_name: Steinhoff, Vera last_name: Steinhoff - first_name: Lennart full_name: Schäpermeier, Lennart last_name: Schäpermeier - 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 - first_name: Christian full_name: Grimme, Christian last_name: Grimme citation: ama: 'Aspar P, Steinhoff V, Schäpermeier L, Kerschke P, Trautmann H, Grimme C. The objective that freed me: a multi-objective local search approach for continuous single-objective optimization. Natural Computing. 2022;1:1–15. doi:10.1007/s11047-022-09919-w' apa: 'Aspar, P., Steinhoff, V., Schäpermeier, L., Kerschke, P., Trautmann, H., & Grimme, C. (2022). The objective that freed me: a multi-objective local search approach for continuous single-objective optimization. Natural Computing, 1, 1–15. https://doi.org/10.1007/s11047-022-09919-w' bibtex: '@article{Aspar_Steinhoff_Schäpermeier_Kerschke_Trautmann_Grimme_2022, title={The objective that freed me: a multi-objective local search approach for continuous single-objective optimization}, volume={1}, DOI={10.1007/s11047-022-09919-w}, journal={Natural Computing}, author={Aspar, Pelin and Steinhoff, Vera and Schäpermeier, Lennart and Kerschke, Pascal and Trautmann, Heike and Grimme, Christian}, year={2022}, pages={1–15} }' chicago: 'Aspar, Pelin, Vera Steinhoff, Lennart Schäpermeier, Pascal Kerschke, Heike Trautmann, and Christian Grimme. “The Objective That Freed Me: A Multi-Objective Local Search Approach for Continuous Single-Objective Optimization.” Natural Computing 1 (2022): 1–15. https://doi.org/10.1007/s11047-022-09919-w.' ieee: 'P. Aspar, V. Steinhoff, L. Schäpermeier, P. Kerschke, H. Trautmann, and C. Grimme, “The objective that freed me: a multi-objective local search approach for continuous single-objective optimization,” Natural Computing, vol. 1, pp. 1–15, 2022, doi: 10.1007/s11047-022-09919-w.' mla: 'Aspar, Pelin, et al. “The Objective That Freed Me: A Multi-Objective Local Search Approach for Continuous Single-Objective Optimization.” Natural Computing, vol. 1, 2022, pp. 1–15, doi:10.1007/s11047-022-09919-w.' short: P. Aspar, V. Steinhoff, L. Schäpermeier, P. Kerschke, H. Trautmann, C. Grimme, Natural Computing 1 (2022) 1–15. date_created: 2023-08-04T07:16:40Z date_updated: 2023-10-16T12:52:33Z department: - _id: '34' - _id: '819' doi: 10.1007/s11047-022-09919-w intvolume: ' 1' language: - iso: eng page: 1–15 publication: Natural Computing status: public title: 'The objective that freed me: a multi-objective local search approach for continuous single-objective optimization' type: journal_article user_id: '15504' volume: 1 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' ...