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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","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} }","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.","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.","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","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."},"publication":"Evol. Comput.","issue":"3","type":"journal_article","date_created":"2025-04-03T05:56:07Z","intvolume":" 32","date_updated":"2025-04-03T05:56:33Z","author":[{"full_name":"Prager, Raphael Patrick","first_name":"Raphael Patrick","last_name":"Prager"},{"id":"100740","last_name":"Trautmann","orcid":"0000-0002-9788-8282","first_name":"Heike","full_name":"Trautmann, Heike"}],"status":"public","year":"2024","title":"Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python","volume":32,"user_id":"15504","doi":"10.1162/EVCO_A_00341","language":[{"iso":"eng"}],"_id":"59283","page":"211–216"},{"_id":"47522","publisher":"Association for Computing Machinery","page":"129–139","user_id":"15504","status":"public","place":"New York, NY, USA","citation":{"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} }","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","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.","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.","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.","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.","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"},"series_title":"FOGA ’23","language":[{"iso":"eng"}],"doi":"10.1145/3594805.3607136","author":[{"last_name":"Prager","first_name":"Raphael Patrick","full_name":"Prager, Raphael Patrick"},{"first_name":"Konstantin","last_name":"Dietrich","full_name":"Dietrich, Konstantin"},{"last_name":"Schneider","first_name":"Lennart","full_name":"Schneider, Lennart"},{"last_name":"Schäpermeier","first_name":"Lennart","full_name":"Schäpermeier, Lennart"},{"first_name":"Bernd","last_name":"Bischl","full_name":"Bischl, Bernd"},{"full_name":"Kerschke, Pascal","first_name":"Pascal","last_name":"Kerschke"},{"first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","full_name":"Trautmann, Heike","id":"100740"},{"full_name":"Mersmann, Olaf","first_name":"Olaf","last_name":"Mersmann"}],"publication_identifier":{"isbn":["9798400702020"]},"year":"2023","title":"Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features","date_updated":"2023-10-16T12:33:02Z","date_created":"2023-09-27T15:43:17Z","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","keyword":["Benchmarking","Instance Generator","Black-Box Continuous Optimization","Exploratory Landscape Analysis","Neural Networks"],"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"}]},{"page":"411–425","publisher":"Springer Nature Switzerland","_id":"46297","language":[{"iso":"eng"}],"user_id":"15504","editor":[{"first_name":"João","last_name":"Correia","full_name":"Correia, João"},{"full_name":"Smith, Stephen","first_name":"Stephen","last_name":"Smith"},{"full_name":"Qaddoura, Raneem","last_name":"Qaddoura","first_name":"Raneem"}],"status":"public","year":"2023","title":"Nullifying the Inherent Bias of Non-invariant Exploratory Landscape Analysis Features","publication_identifier":{"isbn":["978-3-031-30229-9"]},"author":[{"full_name":"Prager, Raphael Patrick","last_name":"Prager","first_name":"Raphael Patrick"},{"id":"100740","full_name":"Trautmann, Heike","first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann"}],"date_updated":"2023-10-16T12:36:45Z","date_created":"2023-08-04T06:54:22Z","place":"Cham","type":"conference","department":[{"_id":"819"},{"_id":"34"}],"publication":"Applications of Evolutionary Computation","citation":{"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.","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. Applications of Evolutionary Computation. Springer Nature Switzerland; 2023:411–425.","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.","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.","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 Applications of Evolutionary Computation, edited by João Correia, Stephen Smith, and Raneem Qaddoura, 411–425. Cham: Springer Nature Switzerland, 2023."},"abstract":[{"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.","lang":"eng"}]},{"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."}],"publication":"Evolutionary Multi-Criterion Optimization","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. Evolutionary Multi-Criterion Optimization. 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.” Evolutionary Multi-Criterion Optimization, 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 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.","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 Evolutionary Multi-Criterion Optimization, 2023, pp. 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."},"type":"conference","department":[{"_id":"819"},{"_id":"34"}],"date_created":"2023-08-04T06:56:10Z","place":"Cham","date_updated":"2023-10-16T12:36:17Z","year":"2023","title":"Peak-A-Boo! Generating Multi-objective Multiple Peaks Benchmark Problems with Precise Pareto Sets","status":"public","author":[{"first_name":"Lennart","last_name":"Schäpermeier","full_name":"Schäpermeier, Lennart"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"full_name":"Grimme, Christian","first_name":"Christian","last_name":"Grimme"},{"id":"100740","full_name":"Trautmann, Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike"}],"publication_identifier":{"isbn":["978-3-031-27250-9"]},"user_id":"15504","editor":[{"full_name":"Emmerich, Michael","first_name":"Michael","last_name":"Emmerich"},{"first_name":"André","last_name":"Deutz","full_name":"Deutz, André"},{"full_name":"Wang, Hao","last_name":"Wang","first_name":"Hao"},{"last_name":"Kononova","first_name":"Anna V.","full_name":"Kononova, Anna V."},{"last_name":"Naujoks","first_name":"Boris","full_name":"Naujoks, Boris"},{"first_name":"Ke","last_name":"Li","full_name":"Li, Ke"},{"last_name":"Miettinen","first_name":"Kaisa","full_name":"Miettinen, Kaisa"},{"full_name":"Yevseyeva, Iryna","last_name":"Yevseyeva","first_name":"Iryna"}],"page":"291–304","language":[{"iso":"eng"}],"_id":"46298","publisher":"Springer Nature Switzerland"},{"user_id":"15504","doi":"10.1162/evco_a_00341","page":"1–25","language":[{"iso":"eng"}],"_id":"46299","date_updated":"2023-10-16T12:35:56Z","title":"Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python","year":"2023","status":"public","publication_identifier":{"issn":["1063-6560"]},"author":[{"last_name":"Prager","first_name":"Raphael Patrick","full_name":"Prager, Raphael Patrick"},{"id":"100740","orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike","full_name":"Trautmann, Heike"}],"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":{"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.","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","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} }","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","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.","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.” Evolutionary Computation, 2023, 1–25. https://doi.org/10.1162/evco_a_00341."}},{"editor":[{"full_name":"Silva, Sara","last_name":"Silva","first_name":"Sara"},{"full_name":"Paquete, Luís","first_name":"Luís","last_name":"Paquete"}],"doi":"10.1145/3583133.3590757","user_id":"15504","_id":"52530","publisher":"ACM","language":[{"iso":"eng"}],"page":"451–454","date_updated":"2024-03-13T10:28:07Z","author":[{"last_name":"Prager","first_name":"Raphael Patrick","full_name":"Prager, Raphael Patrick"},{"full_name":"Trautmann, Heike","first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","id":"100740"}],"year":"2023","status":"public","title":"Investigating the Viability of Existing Exploratory Landscape Analysis Features for Mixed-Integer Problems","department":[{"_id":"819"}],"type":"conference","date_created":"2024-03-13T09:55:17Z","citation":{"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.","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} }","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","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.","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","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.","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."},"publication":"Companion Proceedings of the Conference on Genetic and Evolutionary Computation, GECCO 2023, Companion Volume, Lisbon, Portugal, July 15-19, 2023"},{"type":"journal_article","keyword":["Feature normalization","Algorithm selection","Traveling salesperson problem"],"department":[{"_id":"34"},{"_id":"819"}],"date_created":"2023-08-04T07:18:38Z","abstract":[{"text":"Classic automated algorithm selection (AS) for (combinatorial) optimization problems heavily relies on so-called instance features, i.e., numerical characteristics of the problem at hand ideally extracted with computationally low-demanding routines. For the traveling salesperson problem (TSP) a plethora of features have been suggested. Most of these features are, if at all, only normalized imprecisely raising the issue of feature values being strongly affected by the instance size. Such artifacts may have detrimental effects on algorithm selection models. We propose a normalization for two feature groups which stood out in multiple AS studies on the TSP: (a) features based on a minimum spanning tree (MST) and (b) 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.","lang":"eng"}],"publication":"Theoretical Computer Science","citation":{"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.","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","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} }","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","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.","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.","short":"J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, P. Kerschke, Theoretical Computer Science 940 (2023) 123–145."},"user_id":"15504","doi":"https://doi.org/10.1016/j.tcs.2022.10.019","volume":940,"page":"123-145","language":[{"iso":"eng"}],"_id":"46310","date_updated":"2024-06-10T11:57:21Z","intvolume":" 940","status":"public","title":"A study on the effects of normalized TSP features for automated algorithm selection","year":"2023","publication_identifier":{"issn":["0304-3975"]},"author":[{"first_name":"Jonathan","last_name":"Heins","full_name":"Heins, Jonathan"},{"id":"102979","full_name":"Bossek, Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","first_name":"Jakob"},{"last_name":"Pohl","first_name":"Janina","full_name":"Pohl, Janina"},{"id":"105520","first_name":"Moritz","last_name":"Seiler","full_name":"Seiler, Moritz"},{"id":"100740","first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","full_name":"Trautmann, Heike"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"}]},{"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 2023 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 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","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 2023 IEEE Symposium Series on Computational Intelligence (SSCI), 361–68, n.d. https://doi.org/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.","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} }","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"},"publication":"2023 IEEE Symposium Series on Computational Intelligence (SSCI)","extern":"1","abstract":[{"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.","lang":"eng"}],"date_created":"2023-11-14T15:59:01Z","department":[{"_id":"819"}],"type":"conference","author":[{"id":"105520","full_name":"Seiler, Moritz","first_name":"Moritz","last_name":"Seiler"},{"first_name":"Jeroen","last_name":"Rook","full_name":"Rook, Jeroen"},{"full_name":"Heins, Jonathan","first_name":"Jonathan","last_name":"Heins"},{"id":"102978","last_name":"Preuß","orcid":"0009-0008-9308-2418","first_name":"Oliver Ludger","full_name":"Preuß, Oliver Ludger"},{"id":"102979","orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob","full_name":"Bossek, Jakob"},{"id":"100740","full_name":"Trautmann, Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike"}],"status":"public","title":"Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP","year":"2023","publication_status":"accepted","date_updated":"2024-06-10T11:56:58Z","language":[{"iso":"eng"}],"_id":"48898","page":"361 - 368","user_id":"15504","doi":"10.1109/SSCI52147.2023.10372008"},{"_id":"46300","language":[{"iso":"eng"}],"publisher":"VS Verlag für Sozialwissenschaften","page":"249–274","editor":[{"full_name":"Weitzel, Gerrit","first_name":"Gerrit","last_name":"Weitzel"},{"last_name":"Mündges","first_name":"Stephan","full_name":"Mündges, Stephan"}],"doi":"10.1007/978-3-658-35658-3_13","user_id":"15504","publication_identifier":{"isbn":["978-3-658-35658-3"]},"author":[{"full_name":"Niemann, Marco","first_name":"Marco","last_name":"Niemann"},{"full_name":"Assenmacher, Dennis","last_name":"Assenmacher","first_name":"Dennis"},{"first_name":"Jens","last_name":"Brunk","full_name":"Brunk, Jens"},{"full_name":"Riehle, Dennis Maximilian","first_name":"Dennis Maximilian","last_name":"Riehle"},{"full_name":"Becker, Jörg","first_name":"Jörg","last_name":"Becker"},{"id":"100740","full_name":"Trautmann, Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","first_name":"Heike"}],"status":"public","title":"(Semi-)Automatische Kommentarmoderation zur Erhaltung Konstruktiver Diskurse","year":"2022","date_updated":"2023-10-16T12:35:41Z","place":"Wiesbaden","date_created":"2023-08-04T07:03:47Z","department":[{"_id":"819"},{"_id":"34"}],"type":"book_chapter","citation":{"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.","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.","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","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.","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","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} }","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."},"publication":"Hate Speech — Definitionen, Ausprägungen, Lösungen"},{"year":"2022","title":"Textual One-Pass Stream Clustering with Automated Distance Threshold Adaption","status":"public","author":[{"full_name":"Assenmacher, D","first_name":"D","last_name":"Assenmacher"},{"first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike","id":"100740"}],"date_updated":"2023-10-16T12:35:22Z","page":"3–16","_id":"46301","language":[{"iso":"eng"}],"publisher":"Springer International Publishing","user_id":"15504","doi":"10.1007/978-3-031-21743-2_1","editor":[{"first_name":"T","last_name":"et al. Tran","full_name":"et al. Tran, T"}],"publication":"Intelligent Information and Database Systems","citation":{"short":"D. Assenmacher, H. Trautmann, in: T. et al. Tran (Ed.), Intelligent Information and Database Systems, Springer International Publishing, Cham, 2022, pp. 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.","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","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.","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","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} }","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."},"date_created":"2023-08-04T07:04:54Z","place":"Cham","type":"conference","department":[{"_id":"819"},{"_id":"34"}]},{"department":[{"_id":"34"},{"_id":"819"}],"type":"journal_article","date_created":"2023-08-04T07:26:36Z","abstract":[{"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. ","lang":"eng"}],"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","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} }","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.","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.","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.","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","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."},"publication":"Social Science Computer Review","issue":"6","volume":40,"doi":"10.1177/08944393211012268","user_id":"15504","language":[{"iso":"eng"}],"_id":"46316","page":"1496-1522","intvolume":" 40","date_updated":"2023-10-16T12:57:24Z","author":[{"last_name":"Assenmacher","first_name":"Dennis","full_name":"Assenmacher, Dennis"},{"first_name":"Derek","last_name":"Weber","full_name":"Weber, Derek"},{"full_name":"Preuss, Mike","first_name":"Mike","last_name":"Preuss"},{"full_name":"Valdez, André Calero","last_name":"Valdez","first_name":"André Calero"},{"first_name":"Alison","last_name":"Bradshaw","full_name":"Bradshaw, Alison"},{"full_name":"Ross, Björn","first_name":"Björn","last_name":"Ross"},{"first_name":"Stefano","last_name":"Cresci","full_name":"Cresci, Stefano"},{"id":"100740","orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike","full_name":"Trautmann, Heike"},{"full_name":"Neumann, Frank","last_name":"Neumann","first_name":"Frank"},{"first_name":"Christian","last_name":"Grimme","full_name":"Grimme, Christian"}],"title":"Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem","status":"public","year":"2022"},{"status":"public","page":"575–589","_id":"46306","publisher":"Springer International Publishing","user_id":"15504","editor":[{"first_name":"Günter","last_name":"Rudolph","full_name":"Rudolph, Günter"},{"first_name":"Anna V.","last_name":"Kononova","full_name":"Kononova, Anna V."},{"full_name":"Aguirre, Hernán","first_name":"Hernán","last_name":"Aguirre"},{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"},{"first_name":"Gabriela","last_name":"Ochoa","full_name":"Ochoa, Gabriela"},{"full_name":"Tušar, Tea","last_name":"Tušar","first_name":"Tea"}],"citation":{"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.","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.","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","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.","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","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} }","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."},"place":"Cham","title":"HPO x ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis","year":"2022","author":[{"full_name":"Schneider, Lennart","last_name":"Schneider","first_name":"Lennart"},{"full_name":"Schäpermeier, Lennart","last_name":"Schäpermeier","first_name":"Lennart"},{"first_name":"Raphael Patrick","last_name":"Prager","full_name":"Prager, Raphael Patrick"},{"last_name":"Bischl","first_name":"Bernd","full_name":"Bischl, Bernd"},{"full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike","orcid":"0000-0002-9788-8282","id":"100740"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"}],"publication_identifier":{"isbn":["978-3-031-14714-2"]},"date_updated":"2023-10-16T12:51:27Z","language":[{"iso":"eng"}],"doi":"10.1007/978-3-031-14714-2_40","publication":"Parallel Problem Solving from Nature — PPSN XVII","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."}],"date_created":"2023-08-04T07:15:16Z","type":"conference","department":[{"_id":"34"},{"_id":"819"}]},{"date_updated":"2023-10-16T12:52:33Z","intvolume":" 1","status":"public","title":"The objective that freed me: a multi-objective local search approach for continuous single-objective optimization","year":"2022","author":[{"first_name":"Pelin","last_name":"Aspar","full_name":"Aspar, Pelin"},{"last_name":"Steinhoff","first_name":"Vera","full_name":"Steinhoff, Vera"},{"first_name":"Lennart","last_name":"Schäpermeier","full_name":"Schäpermeier, Lennart"},{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"},{"id":"100740","full_name":"Trautmann, Heike","orcid":"0000-0002-9788-8282","first_name":"Heike","last_name":"Trautmann"},{"full_name":"Grimme, Christian","first_name":"Christian","last_name":"Grimme"}],"user_id":"15504","doi":"10.1007/s11047-022-09919-w","volume":1,"page":"1–15","language":[{"iso":"eng"}],"_id":"46308","abstract":[{"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.","lang":"eng"}],"publication":"Natural Computing","citation":{"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} }","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","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.","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.","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"},"type":"journal_article","department":[{"_id":"34"},{"_id":"819"}],"date_created":"2023-08-04T07:16:40Z"},{"date_created":"2024-03-13T09:59:21Z","type":"journal_article","department":[{"_id":"819"}],"issue":"1","publication":"Comput. Stat.","citation":{"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} }","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","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.","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.","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"},"page":"355–379","language":[{"iso":"eng"}],"_id":"52532","user_id":"15504","doi":"10.1007/S00180-021-01124-0","volume":37,"status":"public","year":"2022","title":"Estimation of component reliability from superposed renewal processes by means of latent variables","author":[{"first_name":"Agatha S.","last_name":"Rodrigues","full_name":"Rodrigues, Agatha S."},{"last_name":"Kerschke","first_name":"Pascal","full_name":"Kerschke, Pascal"},{"full_name":"Pereira, Carlos Alberto De Bragança","last_name":"Pereira","first_name":"Carlos Alberto De Bragança"},{"id":"100740","orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike","full_name":"Trautmann, Heike"},{"full_name":"Wagner, Carolin","last_name":"Wagner","first_name":"Carolin"},{"last_name":"Hellingrath","first_name":"Bernd","full_name":"Hellingrath, Bernd"},{"last_name":"Polpo","first_name":"Adriano","full_name":"Polpo, Adriano"}],"date_updated":"2024-03-13T10:28:01Z","intvolume":" 37"}]