[{"language":[{"iso":"eng"}],"keyword":["Optimization","Evolutionary computation","Benchmark testing","Hyperparameter optimization","Portfolios","Extraterrestrial measurements","Dispersion","Exploratory landscape analysis","mixed-variable problem","mixed search spaces","automated algorithm selection"],"user_id":"15504","department":[{"_id":"819"}],"_id":"54548","status":"public","type":"journal_article","publication":"IEEE Transactions on Evolutionary Computation","doi":"10.1109/TEVC.2024.3399560","title":"Exploratory Landscape Analysis for Mixed-Variable Problems","author":[{"first_name":"Raphael Patrick","full_name":"Prager, Raphael Patrick","last_name":"Prager"},{"id":"100740","full_name":"Trautmann, Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike"}],"date_created":"2024-06-03T06:16:33Z","date_updated":"2024-06-03T06:17:13Z","citation":{"ama":"Prager RP, Trautmann H. Exploratory Landscape Analysis for Mixed-Variable Problems. <i>IEEE Transactions on Evolutionary Computation</i>. Published online 2024:1-1. doi:<a href=\"https://doi.org/10.1109/TEVC.2024.3399560\">10.1109/TEVC.2024.3399560</a>","chicago":"Prager, Raphael Patrick, and Heike Trautmann. “Exploratory Landscape Analysis for Mixed-Variable Problems.” <i>IEEE Transactions on Evolutionary Computation</i>, 2024, 1–1. <a href=\"https://doi.org/10.1109/TEVC.2024.3399560\">https://doi.org/10.1109/TEVC.2024.3399560</a>.","ieee":"R. P. Prager and H. Trautmann, “Exploratory Landscape Analysis for Mixed-Variable Problems,” <i>IEEE Transactions on Evolutionary Computation</i>, pp. 1–1, 2024, doi: <a href=\"https://doi.org/10.1109/TEVC.2024.3399560\">10.1109/TEVC.2024.3399560</a>.","mla":"Prager, Raphael Patrick, and Heike Trautmann. “Exploratory Landscape Analysis for Mixed-Variable Problems.” <i>IEEE Transactions on Evolutionary Computation</i>, 2024, pp. 1–1, doi:<a href=\"https://doi.org/10.1109/TEVC.2024.3399560\">10.1109/TEVC.2024.3399560</a>.","short":"R.P. Prager, H. Trautmann, IEEE Transactions on Evolutionary Computation (2024) 1–1.","bibtex":"@article{Prager_Trautmann_2024, title={Exploratory Landscape Analysis for Mixed-Variable Problems}, DOI={<a href=\"https://doi.org/10.1109/TEVC.2024.3399560\">10.1109/TEVC.2024.3399560</a>}, journal={IEEE Transactions on Evolutionary Computation}, author={Prager, Raphael Patrick and Trautmann, Heike}, year={2024}, pages={1–1} }","apa":"Prager, R. P., &#38; Trautmann, H. (2024). Exploratory Landscape Analysis for Mixed-Variable Problems. <i>IEEE Transactions on Evolutionary Computation</i>, 1–1. <a href=\"https://doi.org/10.1109/TEVC.2024.3399560\">https://doi.org/10.1109/TEVC.2024.3399560</a>"},"page":"1-1","year":"2024"},{"type":"journal_article","publication":"Theoretical Computer Science","status":"public","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."}],"user_id":"15504","department":[{"_id":"34"},{"_id":"819"}],"_id":"46310","language":[{"iso":"eng"}],"keyword":["Feature normalization","Algorithm selection","Traveling salesperson problem"],"publication_identifier":{"issn":["0304-3975"]},"citation":{"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>.","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>.","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>","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>","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>.","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} }","short":"J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, P. Kerschke, Theoretical Computer Science 940 (2023) 123–145."},"intvolume":"       940","page":"123-145","year":"2023","author":[{"full_name":"Heins, Jonathan","last_name":"Heins","first_name":"Jonathan"},{"id":"102979","full_name":"Bossek, Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","first_name":"Jakob"},{"last_name":"Pohl","full_name":"Pohl, Janina","first_name":"Janina"},{"first_name":"Moritz","last_name":"Seiler","full_name":"Seiler, Moritz","id":"105520"},{"id":"100740","full_name":"Trautmann, Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","first_name":"Heike"},{"first_name":"Pascal","full_name":"Kerschke, Pascal","last_name":"Kerschke"}],"date_created":"2023-08-04T07:18:38Z","volume":940,"date_updated":"2024-06-10T11:57:21Z","doi":"https://doi.org/10.1016/j.tcs.2022.10.019","title":"A study on the effects of normalized TSP features for automated algorithm selection"},{"page":"1–15","citation":{"ieee":"J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, and P. Kerschke, “On the Potential of Normalized TSP Features for Automated Algorithm Selection,” in <i>Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, New York, NY, USA: Association for Computing Machinery, 2021, pp. 1–15.","chicago":"Heins, Jonathan, Jakob Bossek, Janina Pohl, Moritz Seiler, Heike Trautmann, and Pascal Kerschke. “On the Potential of Normalized TSP Features for Automated Algorithm Selection.” In <i>Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, 1–15. New York, NY, USA: Association for Computing Machinery, 2021.","ama":"Heins J, Bossek J, Pohl J, Seiler M, Trautmann H, Kerschke P. On the Potential of Normalized TSP Features for Automated Algorithm Selection. In: <i>Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>. Association for Computing Machinery; 2021:1–15.","bibtex":"@inbook{Heins_Bossek_Pohl_Seiler_Trautmann_Kerschke_2021, place={New York, NY, USA}, title={On the Potential of Normalized TSP Features for Automated Algorithm Selection}, booktitle={Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms}, publisher={Association for Computing Machinery}, author={Heins, Jonathan and Bossek, Jakob and Pohl, Janina and Seiler, Moritz and Trautmann, Heike and Kerschke, Pascal}, year={2021}, pages={1–15} }","mla":"Heins, Jonathan, et al. “On the Potential of Normalized TSP Features for Automated Algorithm Selection.” <i>Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i>, Association for Computing Machinery, 2021, pp. 1–15.","short":"J. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, P. Kerschke, in: Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, Association for Computing Machinery, New York, NY, USA, 2021, pp. 1–15.","apa":"Heins, J., Bossek, J., Pohl, J., Seiler, M., Trautmann, H., &#38; Kerschke, P. (2021). On the Potential of Normalized TSP Features for Automated Algorithm Selection. In <i>Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms</i> (pp. 1–15). Association for Computing Machinery."},"place":"New York, NY, USA","year":"2021","publication_identifier":{"isbn":["978-1-4503-8352-3"]},"title":"On the Potential of Normalized TSP Features for Automated Algorithm Selection","date_created":"2023-11-14T15:58:58Z","author":[{"first_name":"Jonathan","last_name":"Heins","full_name":"Heins, Jonathan"},{"first_name":"Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","full_name":"Bossek, Jakob","id":"102979"},{"full_name":"Pohl, Janina","last_name":"Pohl","first_name":"Janina"},{"first_name":"Moritz","last_name":"Seiler","full_name":"Seiler, Moritz"},{"full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike"},{"last_name":"Kerschke","full_name":"Kerschke, Pascal","first_name":"Pascal"}],"date_updated":"2023-12-13T10:47:23Z","publisher":"Association for Computing Machinery","status":"public","abstract":[{"text":"Classic automated algorithm selection (AS) for (combinatorial) optimization problems heavily relies on so-called instance features, i.e., numerical characteristics of the problem at hand ideally extracted with computationally low-demanding routines. For the traveling salesperson problem (TSP) a plethora of features have been suggested. Most of these features are, if at all, only normalized imprecisely raising the issue of feature values being strongly affected by the instance size. Such artifacts may have detrimental effects on algorithm selection models. We propose a normalization for two feature groups which stood out in multiple AS studies on the TSP: (a) features based on a minimum spanning tree (MST) and (b) a k-nearest neighbor graph (NNG) transformation of the input instance. To this end we theoretically derive minimum and maximum values for properties of MSTs and k-NNGs of Euclidean graphs. We analyze the differences in feature space between normalized versions of these features and their unnormalized counterparts. Our empirical investigations on various TSP benchmark sets point out that the feature scaling succeeds in eliminating the effect of the instance size. Eventually, a proof-of-concept AS-study shows promising results: models trained with normalized features tend to outperform those trained with the respective vanilla features.","lang":"eng"}],"publication":"Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms","type":"book_chapter","extern":"1","language":[{"iso":"eng"}],"keyword":["automated algorithm selection","graph theory","instance features","normalization","traveling salesperson problem (TSP)"],"department":[{"_id":"819"}],"user_id":"102979","_id":"48881"},{"place":"Berlin, Heidelberg","year":"2020","page":"48–64","citation":{"chicago":"Seiler, Moritz, Janina Pohl, Jakob Bossek, Pascal Kerschke, and Heike Trautmann. “Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem.” In <i>Parallel Problem Solving from {Nature} (PPSN XVI)</i>, 48–64. Berlin, Heidelberg: Springer-Verlag, 2020. <a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">https://doi.org/10.1007/978-3-030-58112-1_4</a>.","ieee":"M. Seiler, J. Pohl, J. Bossek, P. Kerschke, and H. Trautmann, “Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem,” in <i>Parallel Problem Solving from {Nature} (PPSN XVI)</i>, 2020, pp. 48–64, doi: <a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">10.1007/978-3-030-58112-1_4</a>.","ama":"Seiler M, Pohl J, Bossek J, Kerschke P, Trautmann H. Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. In: <i>Parallel Problem Solving from {Nature} (PPSN XVI)</i>. Springer-Verlag; 2020:48–64. doi:<a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">10.1007/978-3-030-58112-1_4</a>","bibtex":"@inproceedings{Seiler_Pohl_Bossek_Kerschke_Trautmann_2020, place={Berlin, Heidelberg}, title={Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">10.1007/978-3-030-58112-1_4</a>}, booktitle={Parallel Problem Solving from {Nature} (PPSN XVI)}, publisher={Springer-Verlag}, author={Seiler, Moritz and Pohl, Janina and Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}, year={2020}, pages={48–64} }","short":"M. Seiler, J. Pohl, J. Bossek, P. Kerschke, H. Trautmann, in: Parallel Problem Solving from {Nature} (PPSN XVI), Springer-Verlag, Berlin, Heidelberg, 2020, pp. 48–64.","mla":"Seiler, Moritz, et al. “Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem.” <i>Parallel Problem Solving from {Nature} (PPSN XVI)</i>, Springer-Verlag, 2020, pp. 48–64, doi:<a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">10.1007/978-3-030-58112-1_4</a>.","apa":"Seiler, M., Pohl, J., Bossek, J., Kerschke, P., &#38; Trautmann, H. (2020). Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. <i>Parallel Problem Solving from {Nature} (PPSN XVI)</i>, 48–64. <a href=\"https://doi.org/10.1007/978-3-030-58112-1_4\">https://doi.org/10.1007/978-3-030-58112-1_4</a>"},"publication_identifier":{"isbn":["978-3-030-58111-4"]},"title":"Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem","doi":"10.1007/978-3-030-58112-1_4","date_updated":"2023-12-13T10:49:45Z","publisher":"Springer-Verlag","date_created":"2023-11-14T15:59:00Z","author":[{"last_name":"Seiler","full_name":"Seiler, Moritz","first_name":"Moritz"},{"first_name":"Janina","last_name":"Pohl","full_name":"Pohl, Janina"},{"id":"102979","full_name":"Bossek, Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob"},{"first_name":"Pascal","full_name":"Kerschke, Pascal","last_name":"Kerschke"},{"first_name":"Heike","last_name":"Trautmann","full_name":"Trautmann, Heike"}],"abstract":[{"lang":"eng","text":"In this work we focus on the well-known Euclidean Traveling Salesperson Problem (TSP) and two highly competitive inexact heuristic TSP solvers, EAX and LKH, in the context of per-instance algorithm selection (AS). We evolve instances with nodes where the solvers show strongly different performance profiles. These instances serve as a basis for an exploratory study on the identification of well-discriminating problem characteristics (features). Our results in a nutshell: we show that even though (1) promising features exist, (2) these are in line with previous results from the literature, and (3) models trained with these features are more accurate than models adopting sophisticated feature selection methods, the advantage is not close to the virtual best solver in terms of penalized average runtime and so is the performance gain over the single best solver. However, we show that a feature-free deep neural network based approach solely based on visual representation of the instances already matches classical AS model results and thus shows huge potential for future studies."}],"status":"public","publication":"Parallel Problem Solving from {Nature} (PPSN XVI)","type":"conference","keyword":["Automated algorithm selection","Deep learning","Feature-based approaches","Traveling Salesperson Problem"],"language":[{"iso":"eng"}],"extern":"1","_id":"48897","department":[{"_id":"819"}],"user_id":"102979"},{"date_updated":"2023-12-13T10:52:17Z","volume":88,"author":[{"first_name":"Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","id":"102979","full_name":"Bossek, Jakob"},{"first_name":"Pascal","full_name":"Kerschke, Pascal","last_name":"Kerschke"},{"last_name":"Trautmann","full_name":"Trautmann, Heike","first_name":"Heike"}],"date_created":"2023-11-14T15:58:53Z","title":"A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms","doi":"10.1016/j.asoc.2019.105901","publication_identifier":{"issn":["1568-4946"]},"issue":"C","year":"2020","intvolume":"        88","citation":{"apa":"Bossek, J., Kerschke, P., &#38; Trautmann, H. (2020). A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms. <i>Applied Soft Computing</i>, <i>88</i>(C). <a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>","mla":"Bossek, Jakob, et al. “A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms.” <i>Applied Soft Computing</i>, vol. 88, no. C, 2020, doi:<a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">10.1016/j.asoc.2019.105901</a>.","bibtex":"@article{Bossek_Kerschke_Trautmann_2020, title={A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms}, volume={88}, DOI={<a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">10.1016/j.asoc.2019.105901</a>}, number={C}, journal={Applied Soft Computing}, author={Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}, year={2020} }","short":"J. Bossek, P. Kerschke, H. Trautmann, Applied Soft Computing 88 (2020).","chicago":"Bossek, Jakob, Pascal Kerschke, and Heike Trautmann. “A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms.” <i>Applied Soft Computing</i> 88, no. C (2020). <a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>.","ieee":"J. Bossek, P. Kerschke, and H. Trautmann, “A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms,” <i>Applied Soft Computing</i>, vol. 88, no. C, 2020, doi: <a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">10.1016/j.asoc.2019.105901</a>.","ama":"Bossek J, Kerschke P, Trautmann H. A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms. <i>Applied Soft Computing</i>. 2020;88(C). doi:<a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">10.1016/j.asoc.2019.105901</a>"},"_id":"48848","department":[{"_id":"819"}],"user_id":"102979","keyword":["Algorithm selection","Combinatorial optimization","Multi-objective optimization","Performance measurement","Traveling Salesperson Problem"],"language":[{"iso":"eng"}],"publication":"Applied Soft Computing","type":"journal_article","abstract":[{"lang":"eng","text":"We build upon a recently proposed multi-objective view onto performance measurement of single-objective stochastic solvers. The trade-off between the fraction of failed runs and the mean runtime of successful runs \\textendash both to be minimized \\textendash is directly analyzed based on a study on algorithm selection of inexact state-of-the-art solvers for the famous Traveling Salesperson Problem (TSP). Moreover, we adopt the hypervolume indicator (HV) commonly used in multi-objective optimization for simultaneously assessing both conflicting objectives and investigate relations to commonly used performance indicators, both theoretically and empirically. Next to Penalized Average Runtime (PAR) and Penalized Quantile Runtime (PQR), the HV measure is used as a core concept within the construction of per-instance algorithm selection models offering interesting insights into complementary behavior of inexact TSP solvers. \\textbullet The multi-objective perspective is naturally generalizable to multiple objectives. \\textbullet Proof of relationship between HV and the PAR in the considered bi-objective space. \\textbullet New insights into complementary behavior of stochastic optimization algorithms."}],"status":"public"},{"_id":"46334","department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","keyword":["Algorithm selection","Multi-objective optimization","Performance measurement","Combinatorial optimization","Traveling Salesperson Problem"],"language":[{"iso":"eng"}],"publication":"Applied Soft Computing","type":"journal_article","abstract":[{"lang":"eng","text":"We build upon a recently proposed multi-objective view onto performance measurement of single-objective stochastic solvers. The trade-off between the fraction of failed runs and the mean runtime of successful runs – both to be minimized – is directly analyzed based on a study on algorithm selection of inexact state-of-the-art solvers for the famous Traveling Salesperson Problem (TSP). Moreover, we adopt the hypervolume indicator (HV) commonly used in multi-objective optimization for simultaneously assessing both conflicting objectives and investigate relations to commonly used performance indicators, both theoretically and empirically. Next to Penalized Average Runtime (PAR) and Penalized Quantile Runtime (PQR), the HV measure is used as a core concept within the construction of per-instance algorithm selection models offering interesting insights into complementary behavior of inexact TSP solvers."}],"status":"public","date_updated":"2024-06-10T12:00:46Z","volume":88,"date_created":"2023-08-04T07:42:26Z","author":[{"first_name":"Jakob","id":"102979","full_name":"Bossek, Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668"},{"first_name":"Pascal","full_name":"Kerschke, Pascal","last_name":"Kerschke"},{"full_name":"Trautmann, Heike","id":"100740","last_name":"Trautmann","orcid":"0000-0002-9788-8282","first_name":"Heike"}],"title":"A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms","doi":"https://doi.org/10.1016/j.asoc.2019.105901","publication_identifier":{"issn":["1568-4946"]},"year":"2020","intvolume":"        88","page":"105901","citation":{"chicago":"Bossek, Jakob, Pascal Kerschke, and Heike Trautmann. “A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms.” <i>Applied Soft Computing</i> 88 (2020): 105901. <a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>.","ieee":"J. Bossek, P. Kerschke, and H. Trautmann, “A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms,” <i>Applied Soft Computing</i>, vol. 88, p. 105901, 2020, doi: <a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>.","ama":"Bossek J, Kerschke P, Trautmann H. A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms. <i>Applied Soft Computing</i>. 2020;88:105901. doi:<a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>","mla":"Bossek, Jakob, et al. “A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms.” <i>Applied Soft Computing</i>, vol. 88, 2020, p. 105901, doi:<a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>.","short":"J. Bossek, P. Kerschke, H. Trautmann, Applied Soft Computing 88 (2020) 105901.","bibtex":"@article{Bossek_Kerschke_Trautmann_2020, title={A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms}, volume={88}, DOI={<a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>}, journal={Applied Soft Computing}, author={Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}, year={2020}, pages={105901} }","apa":"Bossek, J., Kerschke, P., &#38; Trautmann, H. (2020). A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms. <i>Applied Soft Computing</i>, <i>88</i>, 105901. <a href=\"https://doi.org/10.1016/j.asoc.2019.105901\">https://doi.org/10.1016/j.asoc.2019.105901</a>"}},{"type":"conference","editor":[{"last_name":"Battiti","full_name":"Battiti, Roberto","first_name":"Roberto"},{"full_name":"Brunato, Mauro","last_name":"Brunato","first_name":"Mauro"},{"first_name":"Ilias","full_name":"Kotsireas, Ilias","last_name":"Kotsireas"},{"last_name":"Pardalos","full_name":"Pardalos, Panos M.","first_name":"Panos M."}],"status":"public","_id":"48875","department":[{"_id":"819"}],"user_id":"102979","series_title":"Lecture Notes in Computer Science","extern":"1","publication_identifier":{"isbn":["978-3-030-05348-2"]},"place":"Cham","page":"215–219","citation":{"apa":"Bossek, J., &#38; Trautmann, H. (2019). Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time. In R. Battiti, M. Brunato, I. Kotsireas, &#38; P. M. Pardalos (Eds.), <i>Learning and Intelligent Optimization</i> (pp. 215–219). Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-030-05348-2_19\">https://doi.org/10.1007/978-3-030-05348-2_19</a>","short":"J. Bossek, H. Trautmann, in: R. Battiti, M. Brunato, I. Kotsireas, P.M. Pardalos (Eds.), Learning and Intelligent Optimization, Springer International Publishing, Cham, 2019, pp. 215–219.","bibtex":"@inproceedings{Bossek_Trautmann_2019, place={Cham}, series={Lecture Notes in Computer Science}, title={Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-05348-2_19\">10.1007/978-3-030-05348-2_19</a>}, booktitle={Learning and Intelligent Optimization}, publisher={Springer International Publishing}, author={Bossek, Jakob and Trautmann, Heike}, editor={Battiti, Roberto and Brunato, Mauro and Kotsireas, Ilias and Pardalos, Panos M.}, year={2019}, pages={215–219}, collection={Lecture Notes in Computer Science} }","mla":"Bossek, Jakob, and Heike Trautmann. “Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time.” <i>Learning and Intelligent Optimization</i>, edited by Roberto Battiti et al., Springer International Publishing, 2019, pp. 215–219, doi:<a href=\"https://doi.org/10.1007/978-3-030-05348-2_19\">10.1007/978-3-030-05348-2_19</a>.","chicago":"Bossek, Jakob, and Heike Trautmann. “Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time.” In <i>Learning and Intelligent Optimization</i>, edited by Roberto Battiti, Mauro Brunato, Ilias Kotsireas, and Panos M. Pardalos, 215–219. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2019. <a href=\"https://doi.org/10.1007/978-3-030-05348-2_19\">https://doi.org/10.1007/978-3-030-05348-2_19</a>.","ieee":"J. Bossek and H. Trautmann, “Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time,” in <i>Learning and Intelligent Optimization</i>, 2019, pp. 215–219, doi: <a href=\"https://doi.org/10.1007/978-3-030-05348-2_19\">10.1007/978-3-030-05348-2_19</a>.","ama":"Bossek J, Trautmann H. Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time. In: Battiti R, Brunato M, Kotsireas I, Pardalos PM, eds. <i>Learning and Intelligent Optimization</i>. Lecture Notes in Computer Science. Springer International Publishing; 2019:215–219. doi:<a href=\"https://doi.org/10.1007/978-3-030-05348-2_19\">10.1007/978-3-030-05348-2_19</a>"},"date_updated":"2023-12-13T10:47:32Z","author":[{"full_name":"Bossek, Jakob","id":"102979","orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob"},{"first_name":"Heike","full_name":"Trautmann, Heike","last_name":"Trautmann"}],"doi":"10.1007/978-3-030-05348-2_19","publication":"Learning and Intelligent Optimization","abstract":[{"lang":"eng","text":"A multiobjective perspective onto common performance measures such as the PAR10 score or the expected runtime of single-objective stochastic solvers is presented by directly investigating the tradeoff between the fraction of failed runs and the average runtime. Multi-objective indicators operating in the bi-objective space allow for an overall performance comparison on a set of instances paving the way for instance-based automated algorithm selection techniques."}],"keyword":["Algorithm selection","Performance measurement"],"language":[{"iso":"eng"}],"year":"2019","publisher":"Springer International Publishing","date_created":"2023-11-14T15:58:57Z","title":"Multi-Objective Performance Measurement: Alternatives to PAR10 and Expected Running Time"},{"series_title":"GECCO’18","user_id":"102979","department":[{"_id":"819"}],"_id":"48885","extern":"1","language":[{"iso":"eng"}],"keyword":["algorithm selection","optimization","performance measures","transportation","travelling salesperson problem"],"type":"conference","publication":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","status":"public","abstract":[{"lang":"eng","text":"Performance comparisons of optimization algorithms are heavily influenced by the underlying indicator(s). In this paper we investigate commonly used performance indicators for single-objective stochastic solvers, such as the Penalized Average Runtime (e.g., PAR10) or the Expected Running Time (ERT), based on exemplary benchmark performances of state-of-the-art inexact TSP solvers. Thereby, we introduce a methodology for analyzing the effects of (usually heuristically set) indicator parametrizations - such as the penalty factor and the method used for aggregating across multiple runs - w.r.t. the robustness of the considered optimization algorithms."}],"date_created":"2023-11-14T15:58:59Z","author":[{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"first_name":"Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","id":"102979","full_name":"Bossek, Jakob"},{"first_name":"Heike","full_name":"Trautmann, Heike","last_name":"Trautmann"}],"publisher":"Association for Computing Machinery","date_updated":"2023-12-13T10:48:38Z","doi":"10.1145/3205651.3208233","title":"Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers","publication_identifier":{"isbn":["978-1-4503-5764-7"]},"citation":{"chicago":"Kerschke, Pascal, Jakob Bossek, and Heike Trautmann. “Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, 1737–1744. GECCO’18. New York, NY, USA: Association for Computing Machinery, 2018. <a href=\"https://doi.org/10.1145/3205651.3208233\">https://doi.org/10.1145/3205651.3208233</a>.","ieee":"P. Kerschke, J. Bossek, and H. Trautmann, “Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, 2018, pp. 1737–1744, doi: <a href=\"https://doi.org/10.1145/3205651.3208233\">10.1145/3205651.3208233</a>.","ama":"Kerschke P, Bossek J, Trautmann H. Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>. GECCO’18. Association for Computing Machinery; 2018:1737–1744. doi:<a href=\"https://doi.org/10.1145/3205651.3208233\">10.1145/3205651.3208233</a>","apa":"Kerschke, P., Bossek, J., &#38; Trautmann, H. (2018). Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers. <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, 1737–1744. <a href=\"https://doi.org/10.1145/3205651.3208233\">https://doi.org/10.1145/3205651.3208233</a>","mla":"Kerschke, Pascal, et al. “Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers.” <i>Proceedings of the Genetic and Evolutionary Computation Conference Companion</i>, Association for Computing Machinery, 2018, pp. 1737–1744, doi:<a href=\"https://doi.org/10.1145/3205651.3208233\">10.1145/3205651.3208233</a>.","bibtex":"@inproceedings{Kerschke_Bossek_Trautmann_2018, place={New York, NY, USA}, series={GECCO’18}, title={Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers}, DOI={<a href=\"https://doi.org/10.1145/3205651.3208233\">10.1145/3205651.3208233</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference Companion}, publisher={Association for Computing Machinery}, author={Kerschke, Pascal and Bossek, Jakob and Trautmann, Heike}, year={2018}, pages={1737–1744}, collection={GECCO’18} }","short":"P. Kerschke, J. Bossek, H. Trautmann, in: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, New York, NY, USA, 2018, pp. 1737–1744."},"page":"1737–1744","place":"New York, NY, USA","year":"2018"},{"status":"public","abstract":[{"lang":"eng","text":"The Travelling Salesperson Problem (TSP) is one of the best-studied NP-hard problems. Over the years, many different solution approaches and solvers have been developed. For the first time, we directly compare five state-of-the-art inexact solvers\\textemdash namely, LKH, EAX, restart variants of those, and MAOS\\textemdash on a large set of well-known benchmark instances and demonstrate complementary performance, in that different instances may be solved most effectively by different algorithms. We leverage this complementarity to build an algorithm selector, which selects the best TSP solver on a per-instance basis and thus achieves significantly improved performance compared to the single best solver, representing an advance in the state of the art in solving the Euclidean TSP. Our in-depth analysis of the selectors provides insight into what drives this performance improvement."}],"publication":"Evolutionary Computation","type":"journal_article","language":[{"iso":"eng"}],"keyword":["automated algorithm selection","machine learning.","performance modeling","Travelling Salesperson Problem"],"department":[{"_id":"819"}],"user_id":"102979","_id":"48884","intvolume":"        26","page":"597–620","citation":{"mla":"Kerschke, Pascal, et al. “Leveraging TSP Solver Complementarity through Machine Learning.” <i>Evolutionary Computation</i>, vol. 26, no. 4, 2018, pp. 597–620, doi:<a href=\"https://doi.org/10.1162/evco_a_00215\">10.1162/evco_a_00215</a>.","short":"P. Kerschke, L. Kotthoff, J. Bossek, H.H. Hoos, H. Trautmann, Evolutionary Computation 26 (2018) 597–620.","bibtex":"@article{Kerschke_Kotthoff_Bossek_Hoos_Trautmann_2018, title={Leveraging TSP Solver Complementarity through Machine Learning}, volume={26}, DOI={<a href=\"https://doi.org/10.1162/evco_a_00215\">10.1162/evco_a_00215</a>}, number={4}, journal={Evolutionary Computation}, author={Kerschke, Pascal and Kotthoff, Lars and Bossek, Jakob and Hoos, Holger H. and Trautmann, Heike}, year={2018}, pages={597–620} }","apa":"Kerschke, P., Kotthoff, L., Bossek, J., Hoos, H. H., &#38; Trautmann, H. (2018). Leveraging TSP Solver Complementarity through Machine Learning. <i>Evolutionary Computation</i>, <i>26</i>(4), 597–620. <a href=\"https://doi.org/10.1162/evco_a_00215\">https://doi.org/10.1162/evco_a_00215</a>","chicago":"Kerschke, Pascal, Lars Kotthoff, Jakob Bossek, Holger H. Hoos, and Heike Trautmann. “Leveraging TSP Solver Complementarity through Machine Learning.” <i>Evolutionary Computation</i> 26, no. 4 (2018): 597–620. <a href=\"https://doi.org/10.1162/evco_a_00215\">https://doi.org/10.1162/evco_a_00215</a>.","ieee":"P. Kerschke, L. Kotthoff, J. Bossek, H. H. Hoos, and H. Trautmann, “Leveraging TSP Solver Complementarity through Machine Learning,” <i>Evolutionary Computation</i>, vol. 26, no. 4, pp. 597–620, 2018, doi: <a href=\"https://doi.org/10.1162/evco_a_00215\">10.1162/evco_a_00215</a>.","ama":"Kerschke P, Kotthoff L, Bossek J, Hoos HH, Trautmann H. Leveraging TSP Solver Complementarity through Machine Learning. <i>Evolutionary Computation</i>. 2018;26(4):597–620. doi:<a href=\"https://doi.org/10.1162/evco_a_00215\">10.1162/evco_a_00215</a>"},"year":"2018","issue":"4","publication_identifier":{"issn":["1063-6560"]},"doi":"10.1162/evco_a_00215","title":"Leveraging TSP Solver Complementarity through Machine Learning","volume":26,"date_created":"2023-11-14T15:58:58Z","author":[{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"},{"first_name":"Lars","last_name":"Kotthoff","full_name":"Kotthoff, Lars"},{"last_name":"Bossek","orcid":"0000-0002-4121-4668","id":"102979","full_name":"Bossek, Jakob","first_name":"Jakob"},{"last_name":"Hoos","full_name":"Hoos, Holger H.","first_name":"Holger H."},{"full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike"}],"date_updated":"2023-12-13T10:51:26Z"},{"keyword":["Algorithm selection","Feature selection","Instance hardness","TSP"],"language":[{"iso":"eng"}],"abstract":[{"text":"Despite the intrinsic hardness of the Traveling Salesperson Problem (TSP) heuristic solvers, e.g., LKH+restart and EAX+restart, are remarkably successful in generating satisfactory or even optimal solutions. However, the reasons for their success are not yet fully understood. Recent approaches take an analytical viewpoint and try to identify instance features, which make an instance hard or easy to solve. We contribute to this area by generating instance sets for couples of TSP algorithms A and B by maximizing/minimizing their performance difference in order to generate instances which are easier to solve for one solver and much harder to solve for the other. This instance set offers the potential to identify key features which allow to distinguish between the problem hardness classes of both algorithms.","lang":"eng"}],"publication":"Learning and Intelligent Optimization","title":"Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers","publisher":"Springer International Publishing","date_created":"2023-11-14T15:58:57Z","year":"2016","extern":"1","_id":"48873","department":[{"_id":"819"}],"user_id":"102979","series_title":"Lecture Notes in Computer Science","editor":[{"full_name":"Festa, Paola","last_name":"Festa","first_name":"Paola"},{"full_name":"Sellmann, Meinolf","last_name":"Sellmann","first_name":"Meinolf"},{"first_name":"Joaquin","full_name":"Vanschoren, Joaquin","last_name":"Vanschoren"}],"status":"public","type":"conference","doi":"10.1007/978-3-319-50349-3_4","date_updated":"2023-12-13T10:47:05Z","author":[{"last_name":"Bossek","orcid":"0000-0002-4121-4668","full_name":"Bossek, Jakob","id":"102979","first_name":"Jakob"},{"first_name":"Heike","full_name":"Trautmann, Heike","last_name":"Trautmann"}],"place":"Cham","page":"48–59","citation":{"short":"J. Bossek, H. Trautmann, in: P. Festa, M. Sellmann, J. Vanschoren (Eds.), Learning and Intelligent Optimization, Springer International Publishing, Cham, 2016, pp. 48–59.","mla":"Bossek, Jakob, and Heike Trautmann. “Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers.” <i>Learning and Intelligent Optimization</i>, edited by Paola Festa et al., Springer International Publishing, 2016, pp. 48–59, doi:<a href=\"https://doi.org/10.1007/978-3-319-50349-3_4\">10.1007/978-3-319-50349-3_4</a>.","bibtex":"@inproceedings{Bossek_Trautmann_2016, place={Cham}, series={Lecture Notes in Computer Science}, title={Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers}, DOI={<a href=\"https://doi.org/10.1007/978-3-319-50349-3_4\">10.1007/978-3-319-50349-3_4</a>}, booktitle={Learning and Intelligent Optimization}, publisher={Springer International Publishing}, author={Bossek, Jakob and Trautmann, Heike}, editor={Festa, Paola and Sellmann, Meinolf and Vanschoren, Joaquin}, year={2016}, pages={48–59}, collection={Lecture Notes in Computer Science} }","apa":"Bossek, J., &#38; Trautmann, H. (2016). Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers. In P. Festa, M. Sellmann, &#38; J. Vanschoren (Eds.), <i>Learning and Intelligent Optimization</i> (pp. 48–59). Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-319-50349-3_4\">https://doi.org/10.1007/978-3-319-50349-3_4</a>","chicago":"Bossek, Jakob, and Heike Trautmann. “Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers.” In <i>Learning and Intelligent Optimization</i>, edited by Paola Festa, Meinolf Sellmann, and Joaquin Vanschoren, 48–59. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2016. <a href=\"https://doi.org/10.1007/978-3-319-50349-3_4\">https://doi.org/10.1007/978-3-319-50349-3_4</a>.","ieee":"J. Bossek and H. Trautmann, “Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers,” in <i>Learning and Intelligent Optimization</i>, 2016, pp. 48–59, doi: <a href=\"https://doi.org/10.1007/978-3-319-50349-3_4\">10.1007/978-3-319-50349-3_4</a>.","ama":"Bossek J, Trautmann H. Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers. In: Festa P, Sellmann M, Vanschoren J, eds. <i>Learning and Intelligent Optimization</i>. Lecture Notes in Computer Science. Springer International Publishing; 2016:48–59. doi:<a href=\"https://doi.org/10.1007/978-3-319-50349-3_4\">10.1007/978-3-319-50349-3_4</a>"},"publication_identifier":{"isbn":["978-3-319-50349-3"]},"publication_status":"published"},{"publication":"Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation","type":"conference","status":"public","abstract":[{"text":"The steady supply of new optimization methods makes the algorithm selection problem (ASP) an increasingly pressing and challenging task, specially for real-world black-box optimization problems. The introduced approach considers the ASP as a cost-sensitive classification task which is based on Exploratory Landscape Analysis. Low-level features gathered by systematic sampling of the function on the feasible set are used to predict a well-performing algorithm out of a given portfolio. Example-specific label costs are defined by the expected runtime of each candidate algorithm. We use one-sided support vector regression to solve this learning problem. The approach is illustrated by means of the optimization problems and algorithms of the BBOB’09/10 workshop.","lang":"eng"}],"department":[{"_id":"34"},{"_id":"819"}],"series_title":"GECCO ’12","user_id":"15504","_id":"46396","language":[{"iso":"eng"}],"keyword":["machine learning","exploratory landscape analysis","fitness landscape","benchmarking","evolutionary optimization","bbob test set","algorithm selection"],"publication_identifier":{"isbn":["9781450311779"]},"page":"313–320","citation":{"ieee":"B. Bischl, O. Mersmann, H. Trautmann, and M. Preuß, “Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning,” in <i>Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation</i>, 2012, pp. 313–320, doi: <a href=\"https://doi.org/10.1145/2330163.2330209\">10.1145/2330163.2330209</a>.","chicago":"Bischl, Bernd, Olaf Mersmann, Heike Trautmann, and Mike Preuß. “Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning.” In <i>Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation</i>, 313–320. GECCO ’12. New York, NY, USA: Association for Computing Machinery, 2012. <a href=\"https://doi.org/10.1145/2330163.2330209\">https://doi.org/10.1145/2330163.2330209</a>.","ama":"Bischl B, Mersmann O, Trautmann H, Preuß M. Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning. In: <i>Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation</i>. GECCO ’12. Association for Computing Machinery; 2012:313–320. doi:<a href=\"https://doi.org/10.1145/2330163.2330209\">10.1145/2330163.2330209</a>","apa":"Bischl, B., Mersmann, O., Trautmann, H., &#38; Preuß, M. (2012). Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning. <i>Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation</i>, 313–320. <a href=\"https://doi.org/10.1145/2330163.2330209\">https://doi.org/10.1145/2330163.2330209</a>","bibtex":"@inproceedings{Bischl_Mersmann_Trautmann_Preuß_2012, place={New York, NY, USA}, series={GECCO ’12}, title={Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning}, DOI={<a href=\"https://doi.org/10.1145/2330163.2330209\">10.1145/2330163.2330209</a>}, booktitle={Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation}, publisher={Association for Computing Machinery}, author={Bischl, Bernd and Mersmann, Olaf and Trautmann, Heike and Preuß, Mike}, year={2012}, pages={313–320}, collection={GECCO ’12} }","short":"B. Bischl, O. Mersmann, H. Trautmann, M. Preuß, in: Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, Association for Computing Machinery, New York, NY, USA, 2012, pp. 313–320.","mla":"Bischl, Bernd, et al. “Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning.” <i>Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation</i>, Association for Computing Machinery, 2012, pp. 313–320, doi:<a href=\"https://doi.org/10.1145/2330163.2330209\">10.1145/2330163.2330209</a>."},"year":"2012","place":"New York, NY, USA","date_created":"2023-08-04T15:51:56Z","author":[{"full_name":"Bischl, Bernd","last_name":"Bischl","first_name":"Bernd"},{"first_name":"Olaf","full_name":"Mersmann, Olaf","last_name":"Mersmann"},{"first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike","id":"100740"},{"first_name":"Mike","last_name":"Preuß","full_name":"Preuß, Mike"}],"publisher":"Association for Computing Machinery","date_updated":"2023-10-16T13:48:48Z","doi":"10.1145/2330163.2330209","title":"Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning"}]