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Winkler, A. V. Kononova, H. Trautmann, T. Tusar, P. Machado, &#38; T. Bäck (Eds.), <i>Parallel Problem Solving from Nature - PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg, Austria, September 14-18, 2024, Proceedings, Part II</i> (Vol. 15149, pp. 137–153). Springer. <a href=\"https://doi.org/10.1007/978-3-031-70068-2_9\">https://doi.org/10.1007/978-3-031-70068-2_9</a>","ieee":"M. Seiler, U. Skvorc, G. Cenikj, C. Doerr, and H. Trautmann, “Learned Features vs. Classical ELA on Affine BBOB Functions,” in <i>Parallel Problem Solving from Nature - PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg, Austria, September 14-18, 2024, Proceedings, Part II</i>, 2024, vol. 15149, pp. 137–153, doi: <a href=\"https://doi.org/10.1007/978-3-031-70068-2_9\">10.1007/978-3-031-70068-2_9</a>.","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. <i>Parallel Problem Solving from Nature - PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg, Austria, September 14-18, 2024, Proceedings, Part II</i>. Vol 15149. Lecture Notes in Computer Science. Springer; 2024:137–153. doi:<a href=\"https://doi.org/10.1007/978-3-031-70068-2_9\">10.1007/978-3-031-70068-2_9</a>","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={<a href=\"https://doi.org/10.1007/978-3-031-70068-2_9\">10.1007/978-3-031-70068-2_9</a>}, 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} }","mla":"Seiler, Moritz, et al. “Learned Features vs. Classical ELA on Affine BBOB Functions.” <i>Parallel Problem Solving from Nature - PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg, Austria, September 14-18, 2024, Proceedings, Part II</i>, edited by Michael Affenzeller et al., vol. 15149, Springer, 2024, pp. 137–153, doi:<a href=\"https://doi.org/10.1007/978-3-031-70068-2_9\">10.1007/978-3-031-70068-2_9</a>."},"publication":"Parallel Problem Solving from Nature - PPSN XVIII - 18th International Conference, PPSN 2024, Hagenberg, Austria, September 14-18, 2024, Proceedings, Part II","date_created":"2025-06-04T12:48:56Z","type":"conference","author":[{"id":"105520","full_name":"Seiler, Moritz","last_name":"Seiler","first_name":"Moritz"},{"full_name":"Skvorc, Urban","last_name":"Skvorc","first_name":"Urban","id":"103764"},{"full_name":"Cenikj, Gjorgjina","last_name":"Cenikj","first_name":"Gjorgjina"},{"full_name":"Doerr, Carola","last_name":"Doerr","first_name":"Carola"},{"orcid":"0000-0002-9788-8282","first_name":"Heike","last_name":"Trautmann","full_name":"Trautmann, Heike","id":"100740"}],"title":"Learned Features vs. Classical ELA on Affine BBOB Functions","year":"2024","status":"public","intvolume":"     15149","date_updated":"2025-06-04T12:49:30Z","language":[{"iso":"eng"}],"_id":"60132","series_title":"Lecture Notes in Computer Science","publisher":"Springer","page":"137–153","editor":[{"full_name":"Affenzeller, Michael","last_name":"Affenzeller","first_name":"Michael"},{"last_name":"Winkler","first_name":"Stephan M.","full_name":"Winkler, Stephan M."},{"last_name":"Kononova","first_name":"Anna V.","full_name":"Kononova, Anna V."},{"full_name":"Trautmann, Heike","first_name":"Heike","last_name":"Trautmann"},{"last_name":"Tusar","first_name":"Tea","full_name":"Tusar, Tea"},{"full_name":"Machado, Penousal","first_name":"Penousal","last_name":"Machado"},{"first_name":"Thomas","last_name":"Bäck","full_name":"Bäck, Thomas"}],"volume":15149,"doi":"10.1007/978-3-031-70068-2_9","user_id":"15504"},{"_id":"59283","language":[{"iso":"eng"}],"page":"211–216","volume":32,"user_id":"15504","doi":"10.1162/EVCO_A_00341","author":[{"full_name":"Prager, Raphael Patrick","first_name":"Raphael Patrick","last_name":"Prager"},{"orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike","full_name":"Trautmann, Heike","id":"100740"}],"status":"public","title":"Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python","year":"2024","intvolume":"        32","date_updated":"2025-04-03T05:56:33Z","date_created":"2025-04-03T05:56:07Z","type":"journal_article","citation":{"bibtex":"@article{Prager_Trautmann_2024, title={Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python}, volume={32}, DOI={<a href=\"https://doi.org/10.1162/EVCO_A_00341\">10.1162/EVCO_A_00341</a>}, number={3}, journal={Evol. Comput.}, author={Prager, Raphael Patrick and Trautmann, Heike}, year={2024}, pages={211–216} }","ama":"Prager RP, Trautmann H. Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python. <i>Evol Comput</i>. 2024;32(3):211–216. doi:<a href=\"https://doi.org/10.1162/EVCO_A_00341\">10.1162/EVCO_A_00341</a>","mla":"Prager, Raphael Patrick, and Heike Trautmann. “Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python.” <i>Evol. Comput.</i>, vol. 32, no. 3, 2024, pp. 211–216, doi:<a href=\"https://doi.org/10.1162/EVCO_A_00341\">10.1162/EVCO_A_00341</a>.","short":"R.P. Prager, H. Trautmann, Evol. Comput. 32 (2024) 211–216.","chicago":"Prager, Raphael Patrick, and Heike Trautmann. “Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python.” <i>Evol. Comput.</i> 32, no. 3 (2024): 211–216. <a href=\"https://doi.org/10.1162/EVCO_A_00341\">https://doi.org/10.1162/EVCO_A_00341</a>.","ieee":"R. P. Prager and H. Trautmann, “Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python,” <i>Evol. Comput.</i>, vol. 32, no. 3, pp. 211–216, 2024, doi: <a href=\"https://doi.org/10.1162/EVCO_A_00341\">10.1162/EVCO_A_00341</a>.","apa":"Prager, R. P., &#38; Trautmann, H. (2024). Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python. <i>Evol. Comput.</i>, <i>32</i>(3), 211–216. <a href=\"https://doi.org/10.1162/EVCO_A_00341\">https://doi.org/10.1162/EVCO_A_00341</a>"},"publication":"Evol. Comput.","issue":"3"},{"citation":{"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.","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 <i>Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, 129–139. FOGA ’23. New York, NY, USA: Association for Computing Machinery, 2023. <a href=\"https://doi.org/10.1145/3594805.3607136\">https://doi.org/10.1145/3594805.3607136</a>.","ieee":"R. P. Prager <i>et al.</i>, “Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features,” in <i>Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, 2023, pp. 129–139, doi: <a href=\"https://doi.org/10.1145/3594805.3607136\">10.1145/3594805.3607136</a>.","apa":"Prager, R. P., Dietrich, K., Schneider, L., Schäpermeier, L., Bischl, B., Kerschke, P., Trautmann, H., &#38; Mersmann, O. (2023). Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features. <i>Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, 129–139. <a href=\"https://doi.org/10.1145/3594805.3607136\">https://doi.org/10.1145/3594805.3607136</a>","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={<a href=\"https://doi.org/10.1145/3594805.3607136\">10.1145/3594805.3607136</a>}, 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} }","ama":"Prager RP, Dietrich K, Schneider L, et al. Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features. In: <i>Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>. FOGA ’23. Association for Computing Machinery; 2023:129–139. doi:<a href=\"https://doi.org/10.1145/3594805.3607136\">10.1145/3594805.3607136</a>","mla":"Prager, Raphael Patrick, et al. “Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features.” <i>Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, Association for Computing Machinery, 2023, pp. 129–139, doi:<a href=\"https://doi.org/10.1145/3594805.3607136\">10.1145/3594805.3607136</a>."},"place":"New York, NY, USA","status":"public","page":"129–139","_id":"47522","publisher":"Association for Computing Machinery","user_id":"15504","publication":"Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms","abstract":[{"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.","lang":"eng"}],"date_created":"2023-09-27T15:43:17Z","type":"conference","keyword":["Benchmarking","Instance Generator","Black-Box Continuous Optimization","Exploratory Landscape Analysis","Neural Networks"],"department":[{"_id":"34"},{"_id":"819"}],"title":"Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features","year":"2023","author":[{"full_name":"Prager, Raphael Patrick","first_name":"Raphael Patrick","last_name":"Prager"},{"full_name":"Dietrich, Konstantin","last_name":"Dietrich","first_name":"Konstantin"},{"last_name":"Schneider","first_name":"Lennart","full_name":"Schneider, Lennart"},{"first_name":"Lennart","last_name":"Schäpermeier","full_name":"Schäpermeier, Lennart"},{"full_name":"Bischl, Bernd","first_name":"Bernd","last_name":"Bischl"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"id":"100740","orcid":"0000-0002-9788-8282","first_name":"Heike","last_name":"Trautmann","full_name":"Trautmann, Heike"},{"last_name":"Mersmann","first_name":"Olaf","full_name":"Mersmann, Olaf"}],"publication_identifier":{"isbn":["9798400702020"]},"date_updated":"2023-10-16T12:33:02Z","language":[{"iso":"eng"}],"series_title":"FOGA ’23","doi":"10.1145/3594805.3607136"},{"publication_identifier":{"isbn":["978-3-031-30229-9"]},"author":[{"full_name":"Prager, Raphael Patrick","last_name":"Prager","first_name":"Raphael Patrick"},{"id":"100740","last_name":"Trautmann","first_name":"Heike","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike"}],"year":"2023","status":"public","title":"Nullifying the Inherent Bias of Non-invariant Exploratory Landscape Analysis Features","date_updated":"2023-10-16T12:36:45Z","_id":"46297","language":[{"iso":"eng"}],"publisher":"Springer Nature Switzerland","page":"411–425","editor":[{"full_name":"Correia, João","first_name":"João","last_name":"Correia"},{"full_name":"Smith, Stephen","last_name":"Smith","first_name":"Stephen"},{"full_name":"Qaddoura, Raneem","first_name":"Raneem","last_name":"Qaddoura"}],"user_id":"15504","citation":{"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} }","ama":"Prager RP, Trautmann H. Nullifying the Inherent Bias of Non-invariant Exploratory Landscape Analysis Features. In: Correia J, Smith S, Qaddoura R, eds. <i>Applications of Evolutionary Computation</i>. Springer Nature Switzerland; 2023:411–425.","mla":"Prager, Raphael Patrick, and Heike Trautmann. “Nullifying the Inherent Bias of Non-Invariant Exploratory Landscape Analysis Features.” <i>Applications of Evolutionary Computation</i>, 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.","chicago":"Prager, Raphael Patrick, and Heike Trautmann. “Nullifying the Inherent Bias of Non-Invariant Exploratory Landscape Analysis Features.” In <i>Applications of Evolutionary Computation</i>, 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 <i>Applications of Evolutionary Computation</i>, 2023, pp. 411–425.","apa":"Prager, R. P., &#38; Trautmann, H. (2023). Nullifying the Inherent Bias of Non-invariant Exploratory Landscape Analysis Features. In J. Correia, S. Smith, &#38; R. Qaddoura (Eds.), <i>Applications of Evolutionary Computation</i> (pp. 411–425). Springer Nature Switzerland."},"publication":"Applications of Evolutionary Computation","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."}],"place":"Cham","date_created":"2023-08-04T06:54:22Z","department":[{"_id":"819"},{"_id":"34"}],"type":"conference"},{"date_updated":"2023-10-16T12:36:17Z","author":[{"full_name":"Schäpermeier, Lennart","last_name":"Schäpermeier","first_name":"Lennart"},{"full_name":"Kerschke, Pascal","first_name":"Pascal","last_name":"Kerschke"},{"full_name":"Grimme, Christian","last_name":"Grimme","first_name":"Christian"},{"id":"100740","orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike","full_name":"Trautmann, Heike"}],"publication_identifier":{"isbn":["978-3-031-27250-9"]},"title":"Peak-A-Boo! Generating Multi-objective Multiple Peaks Benchmark Problems with Precise Pareto Sets","year":"2023","status":"public","editor":[{"full_name":"Emmerich, Michael","first_name":"Michael","last_name":"Emmerich"},{"first_name":"André","last_name":"Deutz","full_name":"Deutz, André"},{"last_name":"Wang","first_name":"Hao","full_name":"Wang, Hao"},{"last_name":"Kononova","first_name":"Anna V.","full_name":"Kononova, Anna V."},{"first_name":"Boris","last_name":"Naujoks","full_name":"Naujoks, Boris"},{"full_name":"Li, Ke","first_name":"Ke","last_name":"Li"},{"first_name":"Kaisa","last_name":"Miettinen","full_name":"Miettinen, Kaisa"},{"full_name":"Yevseyeva, Iryna","first_name":"Iryna","last_name":"Yevseyeva"}],"user_id":"15504","language":[{"iso":"eng"}],"_id":"46298","publisher":"Springer Nature Switzerland","page":"291–304","abstract":[{"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.","lang":"eng"}],"citation":{"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} }","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. <i>Evolutionary Multi-Criterion Optimization</i>. Springer Nature Switzerland; 2023:291–304.","mla":"Schäpermeier, Lennart, et al. “Peak-A-Boo! Generating Multi-Objective Multiple Peaks Benchmark Problems with Precise Pareto Sets.” <i>Evolutionary Multi-Criterion Optimization</i>, edited by Michael Emmerich et al., Springer Nature Switzerland, 2023, pp. 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 <i>Evolutionary Multi-Criterion Optimization</i>, 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.","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.","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 <i>Evolutionary Multi-Criterion Optimization</i>, 2023, pp. 291–304.","apa":"Schäpermeier, L., Kerschke, P., Grimme, C., &#38; 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, &#38; I. Yevseyeva (Eds.), <i>Evolutionary Multi-Criterion Optimization</i> (pp. 291–304). Springer Nature Switzerland."},"publication":"Evolutionary Multi-Criterion Optimization","department":[{"_id":"819"},{"_id":"34"}],"type":"conference","place":"Cham","date_created":"2023-08-04T06:56:10Z"},{"type":"journal_article","department":[{"_id":"819"},{"_id":"34"}],"date_created":"2023-08-04T07:01:33Z","abstract":[{"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.","lang":"eng"}],"publication":"Evolutionary Computation","citation":{"apa":"Prager, R. P., &#38; Trautmann, H. (2023). Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python. <i>Evolutionary Computation</i>, 1–25. <a href=\"https://doi.org/10.1162/evco_a_00341\">https://doi.org/10.1162/evco_a_00341</a>","ieee":"R. P. Prager and H. Trautmann, “Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python,” <i>Evolutionary Computation</i>, pp. 1–25, 2023, doi: <a href=\"https://doi.org/10.1162/evco_a_00341\">10.1162/evco_a_00341</a>.","short":"R.P. Prager, H. Trautmann, Evolutionary Computation (2023) 1–25.","chicago":"Prager, Raphael Patrick, and Heike Trautmann. “Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python.” <i>Evolutionary Computation</i>, 2023, 1–25. <a href=\"https://doi.org/10.1162/evco_a_00341\">https://doi.org/10.1162/evco_a_00341</a>.","mla":"Prager, Raphael Patrick, and Heike Trautmann. “Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python.” <i>Evolutionary Computation</i>, 2023, pp. 1–25, doi:<a href=\"https://doi.org/10.1162/evco_a_00341\">10.1162/evco_a_00341</a>.","ama":"Prager RP, Trautmann H. Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python. <i>Evolutionary Computation</i>. Published online 2023:1–25. doi:<a href=\"https://doi.org/10.1162/evco_a_00341\">10.1162/evco_a_00341</a>","bibtex":"@article{Prager_Trautmann_2023, title={Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python}, DOI={<a href=\"https://doi.org/10.1162/evco_a_00341\">10.1162/evco_a_00341</a>}, journal={Evolutionary Computation}, author={Prager, Raphael Patrick and Trautmann, Heike}, year={2023}, pages={1–25} }"},"user_id":"15504","doi":"10.1162/evco_a_00341","page":"1–25","language":[{"iso":"eng"}],"_id":"46299","date_updated":"2023-10-16T12:35:56Z","status":"public","year":"2023","title":"Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python","author":[{"first_name":"Raphael Patrick","last_name":"Prager","full_name":"Prager, Raphael Patrick"},{"orcid":"0000-0002-9788-8282","first_name":"Heike","last_name":"Trautmann","full_name":"Trautmann, Heike","id":"100740"}],"publication_identifier":{"issn":["1063-6560"]}},{"page":"451–454","_id":"52530","publisher":"ACM","language":[{"iso":"eng"}],"doi":"10.1145/3583133.3590757","user_id":"15504","editor":[{"full_name":"Silva, Sara","last_name":"Silva","first_name":"Sara"},{"first_name":"Luís","last_name":"Paquete","full_name":"Paquete, Luís"}],"status":"public","title":"Investigating the Viability of Existing Exploratory Landscape Analysis Features for Mixed-Integer Problems","year":"2023","author":[{"full_name":"Prager, Raphael Patrick","first_name":"Raphael Patrick","last_name":"Prager"},{"id":"100740","first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike"}],"date_updated":"2024-03-13T10:28:07Z","date_created":"2024-03-13T09:55:17Z","type":"conference","department":[{"_id":"819"}],"publication":"Companion Proceedings of the Conference on Genetic and Evolutionary Computation, GECCO 2023, Companion Volume, Lisbon, Portugal, July 15-19, 2023","citation":{"bibtex":"@inproceedings{Prager_Trautmann_2023, title={Investigating the Viability of Existing Exploratory Landscape Analysis Features for Mixed-Integer Problems}, DOI={<a href=\"https://doi.org/10.1145/3583133.3590757\">10.1145/3583133.3590757</a>}, 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} }","ama":"Prager RP, Trautmann H. Investigating the Viability of Existing Exploratory Landscape Analysis Features for Mixed-Integer Problems. In: Silva S, Paquete L, eds. <i>Companion Proceedings of the Conference on Genetic and Evolutionary Computation, GECCO 2023, Companion Volume, Lisbon, Portugal, July 15-19, 2023</i>. ACM; 2023:451–454. doi:<a href=\"https://doi.org/10.1145/3583133.3590757\">10.1145/3583133.3590757</a>","mla":"Prager, Raphael Patrick, and Heike Trautmann. “Investigating the Viability of Existing Exploratory Landscape Analysis Features for Mixed-Integer Problems.” <i>Companion Proceedings of the Conference on Genetic and Evolutionary Computation, GECCO 2023, Companion Volume, Lisbon, Portugal, July 15-19, 2023</i>, edited by Sara Silva and Luís Paquete, ACM, 2023, pp. 451–454, doi:<a href=\"https://doi.org/10.1145/3583133.3590757\">10.1145/3583133.3590757</a>.","chicago":"Prager, Raphael Patrick, and Heike Trautmann. “Investigating the Viability of Existing Exploratory Landscape Analysis Features for Mixed-Integer Problems.” In <i>Companion Proceedings of the Conference on Genetic and Evolutionary Computation, GECCO 2023, Companion Volume, Lisbon, Portugal, July 15-19, 2023</i>, edited by Sara Silva and Luís Paquete, 451–454. ACM, 2023. <a href=\"https://doi.org/10.1145/3583133.3590757\">https://doi.org/10.1145/3583133.3590757</a>.","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.","ieee":"R. P. Prager and H. Trautmann, “Investigating the Viability of Existing Exploratory Landscape Analysis Features for Mixed-Integer Problems,” in <i>Companion Proceedings of the Conference on Genetic and Evolutionary Computation, GECCO 2023, Companion Volume, Lisbon, Portugal, July 15-19, 2023</i>, 2023, pp. 451–454, doi: <a href=\"https://doi.org/10.1145/3583133.3590757\">10.1145/3583133.3590757</a>.","apa":"Prager, R. P., &#38; Trautmann, H. (2023). Investigating the Viability of Existing Exploratory Landscape Analysis Features for Mixed-Integer Problems. In S. Silva &#38; L. Paquete (Eds.), <i>Companion Proceedings of the Conference on Genetic and Evolutionary Computation, GECCO 2023, Companion Volume, Lisbon, Portugal, July 15-19, 2023</i> (pp. 451–454). ACM. <a href=\"https://doi.org/10.1145/3583133.3590757\">https://doi.org/10.1145/3583133.3590757</a>"}},{"intvolume":"       940","date_updated":"2024-06-10T11:57:21Z","publication_identifier":{"issn":["0304-3975"]},"author":[{"full_name":"Heins, Jonathan","first_name":"Jonathan","last_name":"Heins"},{"id":"102979","full_name":"Bossek, Jakob","orcid":"0000-0002-4121-4668","first_name":"Jakob","last_name":"Bossek"},{"last_name":"Pohl","first_name":"Janina","full_name":"Pohl, Janina"},{"id":"105520","last_name":"Seiler","first_name":"Moritz","full_name":"Seiler, Moritz"},{"first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike","id":"100740"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"}],"title":"A study on the effects of normalized TSP features for automated algorithm selection","year":"2023","status":"public","volume":940,"doi":"https://doi.org/10.1016/j.tcs.2022.10.019","user_id":"15504","_id":"46310","language":[{"iso":"eng"}],"page":"123-145","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."}],"citation":{"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.” <i>Theoretical Computer Science</i> 940 (2023): 123–45. <a href=\"https://doi.org/10.1016/j.tcs.2022.10.019\">https://doi.org/10.1016/j.tcs.2022.10.019</a>.","short":"J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, P. Kerschke, Theoretical Computer Science 940 (2023) 123–145.","apa":"Heins, J., Bossek, J., Pohl, J., Seiler, M., Trautmann, H., &#38; Kerschke, P. (2023). A study on the effects of normalized TSP features for automated algorithm selection. <i>Theoretical Computer Science</i>, <i>940</i>, 123–145. <a href=\"https://doi.org/10.1016/j.tcs.2022.10.019\">https://doi.org/10.1016/j.tcs.2022.10.019</a>","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,” <i>Theoretical Computer Science</i>, vol. 940, pp. 123–145, 2023, doi: <a href=\"https://doi.org/10.1016/j.tcs.2022.10.019\">https://doi.org/10.1016/j.tcs.2022.10.019</a>.","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. <i>Theoretical Computer Science</i>. 2023;940:123-145. doi:<a href=\"https://doi.org/10.1016/j.tcs.2022.10.019\">https://doi.org/10.1016/j.tcs.2022.10.019</a>","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={<a href=\"https://doi.org/10.1016/j.tcs.2022.10.019\">https://doi.org/10.1016/j.tcs.2022.10.019</a>}, 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} }","mla":"Heins, Jonathan, et al. “A Study on the Effects of Normalized TSP Features for Automated Algorithm Selection.” <i>Theoretical Computer Science</i>, vol. 940, 2023, pp. 123–45, doi:<a href=\"https://doi.org/10.1016/j.tcs.2022.10.019\">https://doi.org/10.1016/j.tcs.2022.10.019</a>."},"publication":"Theoretical Computer Science","department":[{"_id":"34"},{"_id":"819"}],"type":"journal_article","keyword":["Feature normalization","Algorithm selection","Traveling salesperson problem"],"date_created":"2023-08-04T07:18:38Z"},{"page":"361 - 368","language":[{"iso":"eng"}],"_id":"48898","user_id":"15504","doi":"10.1109/SSCI52147.2023.10372008","title":"Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP","status":"public","year":"2023","author":[{"id":"105520","full_name":"Seiler, Moritz","first_name":"Moritz","last_name":"Seiler"},{"last_name":"Rook","first_name":"Jeroen","full_name":"Rook, Jeroen"},{"first_name":"Jonathan","last_name":"Heins","full_name":"Heins, Jonathan"},{"id":"102978","full_name":"Preuß, Oliver Ludger","orcid":"0009-0008-9308-2418","last_name":"Preuß","first_name":"Oliver Ludger"},{"id":"102979","full_name":"Bossek, Jakob","orcid":"0000-0002-4121-4668","first_name":"Jakob","last_name":"Bossek"},{"full_name":"Trautmann, Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike","id":"100740"}],"publication_status":"accepted","date_updated":"2024-06-10T11:56:58Z","date_created":"2023-11-14T15:59:01Z","type":"conference","department":[{"_id":"819"}],"publication":"2023 IEEE Symposium Series on Computational Intelligence (SSCI)","citation":{"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 <i>2023 IEEE Symposium Series on Computational Intelligence (SSCI)</i>, pp. 361–368, doi: <a href=\"https://doi.org/10.1109/SSCI52147.2023.10372008\">10.1109/SSCI52147.2023.10372008</a>.","apa":"Seiler, M., Rook, J., Heins, J., Preuß, O. L., Bossek, J., &#38; Trautmann, H. (n.d.). Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP. <i>2023 IEEE Symposium Series on Computational Intelligence (SSCI)</i>, 361–368. <a href=\"https://doi.org/10.1109/SSCI52147.2023.10372008\">https://doi.org/10.1109/SSCI52147.2023.10372008</a>","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.","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 <i>2023 IEEE Symposium Series on Computational Intelligence (SSCI)</i>, 361–68, n.d. <a href=\"https://doi.org/10.1109/SSCI52147.2023.10372008\">https://doi.org/10.1109/SSCI52147.2023.10372008</a>.","mla":"Seiler, Moritz, et al. “Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP.” <i>2023 IEEE Symposium Series on Computational Intelligence (SSCI)</i>, pp. 361–68, doi:<a href=\"https://doi.org/10.1109/SSCI52147.2023.10372008\">10.1109/SSCI52147.2023.10372008</a>.","bibtex":"@inproceedings{Seiler_Rook_Heins_Preuß_Bossek_Trautmann, title={Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP}, DOI={<a href=\"https://doi.org/10.1109/SSCI52147.2023.10372008\">10.1109/SSCI52147.2023.10372008</a>}, 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} }","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: <i>2023 IEEE Symposium Series on Computational Intelligence (SSCI)</i>. ; :361-368. doi:<a href=\"https://doi.org/10.1109/SSCI52147.2023.10372008\">10.1109/SSCI52147.2023.10372008</a>"},"extern":"1","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."}]},{"date_updated":"2023-10-16T12:35:41Z","title":"(Semi-)Automatische Kommentarmoderation zur Erhaltung Konstruktiver Diskurse","status":"public","year":"2022","publication_identifier":{"isbn":["978-3-658-35658-3"]},"author":[{"full_name":"Niemann, Marco","last_name":"Niemann","first_name":"Marco"},{"first_name":"Dennis","last_name":"Assenmacher","full_name":"Assenmacher, Dennis"},{"full_name":"Brunk, Jens","last_name":"Brunk","first_name":"Jens"},{"full_name":"Riehle, Dennis Maximilian","first_name":"Dennis Maximilian","last_name":"Riehle"},{"full_name":"Becker, Jörg","last_name":"Becker","first_name":"Jörg"},{"first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","full_name":"Trautmann, Heike","id":"100740"}],"doi":"10.1007/978-3-658-35658-3_13","user_id":"15504","editor":[{"last_name":"Weitzel","first_name":"Gerrit","full_name":"Weitzel, Gerrit"},{"full_name":"Mündges, Stephan","first_name":"Stephan","last_name":"Mündges"}],"page":"249–274","_id":"46300","language":[{"iso":"eng"}],"publisher":"VS Verlag für Sozialwissenschaften","publication":"Hate Speech — Definitionen, Ausprägungen, Lösungen","citation":{"ieee":"M. Niemann, D. Assenmacher, J. Brunk, D. M. Riehle, J. Becker, and H. Trautmann, “(Semi-)Automatische Kommentarmoderation zur Erhaltung Konstruktiver Diskurse,” in <i>Hate Speech — Definitionen, Ausprägungen, Lösungen</i>, G. Weitzel and S. Mündges, Eds. Wiesbaden: VS Verlag für Sozialwissenschaften, 2022, pp. 249–274.","apa":"Niemann, M., Assenmacher, D., Brunk, J., Riehle, D. M., Becker, J., &#38; Trautmann, H. (2022). (Semi-)Automatische Kommentarmoderation zur Erhaltung Konstruktiver Diskurse. In G. Weitzel &#38; S. Mündges (Eds.), <i>Hate Speech — Definitionen, Ausprägungen, Lösungen</i> (pp. 249–274). VS Verlag für Sozialwissenschaften. <a href=\"https://doi.org/10.1007/978-3-658-35658-3_13\">https://doi.org/10.1007/978-3-658-35658-3_13</a>","chicago":"Niemann, Marco, Dennis Assenmacher, Jens Brunk, Dennis Maximilian Riehle, Jörg Becker, and Heike Trautmann. “(Semi-)Automatische Kommentarmoderation Zur Erhaltung Konstruktiver Diskurse.” In <i>Hate Speech — Definitionen, Ausprägungen, Lösungen</i>, edited by Gerrit Weitzel and Stephan Mündges, 249–274. Wiesbaden: VS Verlag für Sozialwissenschaften, 2022. <a href=\"https://doi.org/10.1007/978-3-658-35658-3_13\">https://doi.org/10.1007/978-3-658-35658-3_13</a>.","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.","mla":"Niemann, Marco, et al. “(Semi-)Automatische Kommentarmoderation Zur Erhaltung Konstruktiver Diskurse.” <i>Hate Speech — Definitionen, Ausprägungen, Lösungen</i>, edited by Gerrit Weitzel and Stephan Mündges, VS Verlag für Sozialwissenschaften, 2022, pp. 249–274, doi:<a href=\"https://doi.org/10.1007/978-3-658-35658-3_13\">10.1007/978-3-658-35658-3_13</a>.","bibtex":"@inbook{Niemann_Assenmacher_Brunk_Riehle_Becker_Trautmann_2022, place={Wiesbaden}, title={(Semi-)Automatische Kommentarmoderation zur Erhaltung Konstruktiver Diskurse}, DOI={<a href=\"https://doi.org/10.1007/978-3-658-35658-3_13\">10.1007/978-3-658-35658-3_13</a>}, 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} }","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. <i>Hate Speech — Definitionen, Ausprägungen, Lösungen</i>. VS Verlag für Sozialwissenschaften; 2022:249–274. doi:<a href=\"https://doi.org/10.1007/978-3-658-35658-3_13\">10.1007/978-3-658-35658-3_13</a>"},"type":"book_chapter","department":[{"_id":"819"},{"_id":"34"}],"place":"Wiesbaden","date_created":"2023-08-04T07:03:47Z"}]