[{"publication_identifier":{"isbn":["9781450311779"]},"citation":{"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>","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.","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} }","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>.","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>"},"page":"313–320","place":"New York, NY, USA","year":"2012","author":[{"first_name":"Bernd","full_name":"Bischl, Bernd","last_name":"Bischl"},{"full_name":"Mersmann, Olaf","last_name":"Mersmann","first_name":"Olaf"},{"first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","id":"100740","full_name":"Trautmann, Heike"},{"full_name":"Preuß, Mike","last_name":"Preuß","first_name":"Mike"}],"date_created":"2023-08-04T15:51:56Z","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","type":"conference","publication":"Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation","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"}],"series_title":"GECCO ’12","user_id":"15504","department":[{"_id":"34"},{"_id":"819"}],"_id":"46396","language":[{"iso":"eng"}],"keyword":["machine learning","exploratory landscape analysis","fitness landscape","benchmarking","evolutionary optimization","bbob test set","algorithm selection"]},{"year":"2012","citation":{"mla":"Bischl, B., et al. “Resampling Methods in Model Validation.” <i>Evolutionary Computation Journal</i>, vol. 20, no. 2, 2012, pp. 249–275, doi:<a href=\"https://doi.org/10.1162/EVCO_a_00069\">10.1162/EVCO_a_00069</a>.","short":"B. Bischl, O. Mersmann, H. Trautmann, C. Weihs, Evolutionary Computation Journal 20 (2012) 249–275.","bibtex":"@article{Bischl_Mersmann_Trautmann_Weihs_2012, title={Resampling Methods in Model Validation}, volume={20}, DOI={<a href=\"https://doi.org/10.1162/EVCO_a_00069\">10.1162/EVCO_a_00069</a>}, number={2}, journal={Evolutionary Computation Journal}, author={Bischl, B and Mersmann, O and Trautmann, Heike and Weihs, C}, year={2012}, pages={249–275} }","apa":"Bischl, B., Mersmann, O., Trautmann, H., &#38; Weihs, C. (2012). Resampling Methods in Model Validation. <i>Evolutionary Computation Journal</i>, <i>20</i>(2), 249–275. <a href=\"https://doi.org/10.1162/EVCO_a_00069\">https://doi.org/10.1162/EVCO_a_00069</a>","ama":"Bischl B, Mersmann O, Trautmann H, Weihs C. Resampling Methods in Model Validation. <i>Evolutionary Computation Journal</i>. 2012;20(2):249–275. doi:<a href=\"https://doi.org/10.1162/EVCO_a_00069\">10.1162/EVCO_a_00069</a>","chicago":"Bischl, B, O Mersmann, Heike Trautmann, and C Weihs. “Resampling Methods in Model Validation.” <i>Evolutionary Computation Journal</i> 20, no. 2 (2012): 249–275. <a href=\"https://doi.org/10.1162/EVCO_a_00069\">https://doi.org/10.1162/EVCO_a_00069</a>.","ieee":"B. Bischl, O. Mersmann, H. Trautmann, and C. Weihs, “Resampling Methods in Model Validation,” <i>Evolutionary Computation Journal</i>, vol. 20, no. 2, pp. 249–275, 2012, doi: <a href=\"https://doi.org/10.1162/EVCO_a_00069\">10.1162/EVCO_a_00069</a>."},"page":"249–275","intvolume":"        20","issue":"2","title":"Resampling Methods in Model Validation","doi":"10.1162/EVCO_a_00069","date_updated":"2023-10-16T13:53:58Z","author":[{"last_name":"Bischl","full_name":"Bischl, B","first_name":"B"},{"first_name":"O","full_name":"Mersmann, O","last_name":"Mersmann"},{"last_name":"Trautmann","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike","id":"100740","first_name":"Heike"},{"first_name":"C","full_name":"Weihs, C","last_name":"Weihs"}],"date_created":"2023-08-04T15:54:41Z","volume":20,"abstract":[{"text":"Meta-modeling has become a crucial tool in solving expensive optimization problems. Much of the work in the past has focused on finding a good regression method to model the fitness function. Examples include classical linear regression, splines, neural networks, Kriging and support vector regression. This paper specifically draws attention to the fact that assessing model accuracy is a crucial aspect in the meta-modeling framework. Resampling strategies such as cross-validation, subsampling, bootstrapping, and nested resampling are prominent methods for model validation and are systematically discussed with respect to possible pitfalls, shortcomings, and specific features. A survey of meta-modeling techniques within evolutionary optimization is provided. In addition, practical examples illustrating some of the pitfalls associated with model selection and performance assessment are presented. Finally, recommendations are given for choosing a model validation technique for a particular setting.","lang":"eng"}],"status":"public","type":"journal_article","publication":"Evolutionary Computation Journal","language":[{"iso":"eng"}],"_id":"46399","user_id":"15504","department":[{"_id":"34"},{"_id":"819"}]},{"intvolume":"        60","page":"610–621","citation":{"ieee":"G. Rudolph, H. Trautmann, and O. Schütze, “Homogene Approximation der Paretofront bei mehrkriteriellen Kontrollproblemen,” <i>at-Automatisierungstechnik</i>, vol. 60, pp. 610–621, 2012, doi: <a href=\"https://doi.org/10.1524/auto.2012.1033\">10.1524/auto.2012.1033</a>.","chicago":"Rudolph, G, Heike Trautmann, and O Schütze. “Homogene Approximation Der Paretofront Bei Mehrkriteriellen Kontrollproblemen.” <i>At-Automatisierungstechnik</i> 60 (2012): 610–621. <a href=\"https://doi.org/10.1524/auto.2012.1033\">https://doi.org/10.1524/auto.2012.1033</a>.","ama":"Rudolph G, Trautmann H, Schütze O. Homogene Approximation der Paretofront bei mehrkriteriellen Kontrollproblemen. <i>at-Automatisierungstechnik</i>. 2012;60:610–621. doi:<a href=\"https://doi.org/10.1524/auto.2012.1033\">10.1524/auto.2012.1033</a>","short":"G. Rudolph, H. Trautmann, O. Schütze, At-Automatisierungstechnik 60 (2012) 610–621.","mla":"Rudolph, G., et al. “Homogene Approximation Der Paretofront Bei Mehrkriteriellen Kontrollproblemen.” <i>At-Automatisierungstechnik</i>, vol. 60, 2012, pp. 610–621, doi:<a href=\"https://doi.org/10.1524/auto.2012.1033\">10.1524/auto.2012.1033</a>.","bibtex":"@article{Rudolph_Trautmann_Schütze_2012, title={Homogene Approximation der Paretofront bei mehrkriteriellen Kontrollproblemen}, volume={60}, DOI={<a href=\"https://doi.org/10.1524/auto.2012.1033\">10.1524/auto.2012.1033</a>}, journal={at-Automatisierungstechnik}, author={Rudolph, G and Trautmann, Heike and Schütze, O}, year={2012}, pages={610–621} }","apa":"Rudolph, G., Trautmann, H., &#38; Schütze, O. (2012). Homogene Approximation der Paretofront bei mehrkriteriellen Kontrollproblemen. <i>At-Automatisierungstechnik</i>, <i>60</i>, 610–621. <a href=\"https://doi.org/10.1524/auto.2012.1033\">https://doi.org/10.1524/auto.2012.1033</a>"},"year":"2012","doi":"10.1524/auto.2012.1033","title":"Homogene Approximation der Paretofront bei mehrkriteriellen Kontrollproblemen","volume":60,"author":[{"first_name":"G","last_name":"Rudolph","full_name":"Rudolph, G"},{"orcid":"0000-0002-9788-8282","last_name":"Trautmann","full_name":"Trautmann, Heike","id":"100740","first_name":"Heike"},{"last_name":"Schütze","full_name":"Schütze, O","first_name":"O"}],"date_created":"2023-08-04T15:55:34Z","date_updated":"2023-10-16T13:54:17Z","status":"public","abstract":[{"text":"Es   werden   mehrkriterielle   evolutio-näre   Algorithmen   (EMOA)   für   zwei-   und   höherdimensio-nale   Probleme   vorgestellt,   die   gleichmäßig   verteilte   Lö-sungen   entlang   der   wahren   Paretofront   generieren.   Diesist   insbesondere   wichtig   im   Kontext   mehrkriterieller   Kon-trollprobleme.   Die   Methodik   beruht   auf   der   Minimierungdes   gemittelten   Hausdorff-Abstandes in   Bezug   aufdie  Paretofront.  Die  EMOA-Varianten  werden  vergleichendzu  aktuellen   Verfahren  auf  Benchmarkproblemen   getestet.","lang":"ger"}],"publication":"at-Automatisierungstechnik","type":"journal_article","language":[{"iso":"eng"}],"department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","_id":"46400"},{"citation":{"ieee":"O. Mersmann, B. Bischl, J. Bossek, H. Trautmann, M. Wagner, and F. Neumann, “Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness,” in <i>Learning and Intelligent Optimization</i>, 2012, pp. 115–129, doi: <a href=\"https://doi.org/10.1007/978-3-642-34413-8_9\">https://doi.org/10.1007/978-3-642-34413-8_9</a>.","chicago":"Mersmann, Olaf, Bernd Bischl, Jakob Bossek, Heike Trautmann, Markus Wagner, and Frank Neumann. “Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness.” In <i>Learning and Intelligent Optimization</i>, edited by Youssef Hamadi and Marc Schoenauer, 115–129. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. <a href=\"https://doi.org/10.1007/978-3-642-34413-8_9\">https://doi.org/10.1007/978-3-642-34413-8_9</a>.","ama":"Mersmann O, Bischl B, Bossek J, Trautmann H, Wagner M, Neumann F. Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness. In: Hamadi Y, Schoenauer M, eds. <i>Learning and Intelligent Optimization</i>. Springer Berlin Heidelberg; 2012:115–129. doi:<a href=\"https://doi.org/10.1007/978-3-642-34413-8_9\">https://doi.org/10.1007/978-3-642-34413-8_9</a>","apa":"Mersmann, O., Bischl, B., Bossek, J., Trautmann, H., Wagner, M., &#38; Neumann, F. (2012). Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness. In Y. Hamadi &#38; M. Schoenauer (Eds.), <i>Learning and Intelligent Optimization</i> (pp. 115–129). Springer Berlin Heidelberg. <a href=\"https://doi.org/10.1007/978-3-642-34413-8_9\">https://doi.org/10.1007/978-3-642-34413-8_9</a>","mla":"Mersmann, Olaf, et al. “Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness.” <i>Learning and Intelligent Optimization</i>, edited by Youssef Hamadi and Marc Schoenauer, Springer Berlin Heidelberg, 2012, pp. 115–129, doi:<a href=\"https://doi.org/10.1007/978-3-642-34413-8_9\">https://doi.org/10.1007/978-3-642-34413-8_9</a>.","bibtex":"@inproceedings{Mersmann_Bischl_Bossek_Trautmann_Wagner_Neumann_2012, place={Berlin, Heidelberg}, title={Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness}, DOI={<a href=\"https://doi.org/10.1007/978-3-642-34413-8_9\">https://doi.org/10.1007/978-3-642-34413-8_9</a>}, booktitle={Learning and Intelligent Optimization}, publisher={Springer Berlin Heidelberg}, author={Mersmann, Olaf and Bischl, Bernd and Bossek, Jakob and Trautmann, Heike and Wagner, Markus and Neumann, Frank}, editor={Hamadi, Youssef and Schoenauer, Marc}, year={2012}, pages={115–129} }","short":"O. Mersmann, B. Bischl, J. Bossek, H. Trautmann, M. Wagner, F. Neumann, in: Y. Hamadi, M. Schoenauer (Eds.), Learning and Intelligent Optimization, Springer Berlin Heidelberg, Berlin, Heidelberg, 2012, pp. 115–129."},"page":"115–129","place":"Berlin, Heidelberg","year":"2012","publication_identifier":{"isbn":["978-3-642-34413-8"]},"doi":"https://doi.org/10.1007/978-3-642-34413-8_9","title":"Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness","author":[{"first_name":"Olaf","full_name":"Mersmann, Olaf","last_name":"Mersmann"},{"full_name":"Bischl, Bernd","last_name":"Bischl","first_name":"Bernd"},{"full_name":"Bossek, Jakob","id":"102979","last_name":"Bossek","orcid":"0000-0002-4121-4668","first_name":"Jakob"},{"orcid":"0000-0002-9788-8282","last_name":"Trautmann","full_name":"Trautmann, Heike","id":"100740","first_name":"Heike"},{"first_name":"Markus","last_name":"Wagner","full_name":"Wagner, Markus"},{"first_name":"Frank","full_name":"Neumann, Frank","last_name":"Neumann"}],"date_created":"2023-08-04T15:53:33Z","date_updated":"2024-06-10T11:57:32Z","publisher":"Springer Berlin Heidelberg","status":"public","abstract":[{"text":"With this paper we contribute to the understanding of the success of 2-opt based local search algorithms for solving the traveling salesman problem (TSP). Although 2-opt is widely used in practice, it is hard to understand its success from a theoretical perspective. We take a statistical approach and examine the features of TSP instances that make the problem either hard or easy to solve. As a measure of problem difficulty for 2-opt we use the approximation ratio that it achieves on a given instance. Our investigations point out important features that make TSP instances hard or easy to be approximated by 2-opt.","lang":"eng"}],"editor":[{"full_name":"Hamadi, Youssef","last_name":"Hamadi","first_name":"Youssef"},{"last_name":"Schoenauer","full_name":"Schoenauer, Marc","first_name":"Marc"}],"type":"conference","publication":"Learning and Intelligent Optimization","language":[{"iso":"eng"}],"user_id":"15504","department":[{"_id":"34"},{"_id":"819"}],"_id":"46398"},{"language":[{"iso":"eng"}],"keyword":["exploratory landscape analysis","evolutionary optimization","fitness landscape","benchmarking","BBOB test set"],"user_id":"15504","series_title":"GECCO ’11","department":[{"_id":"34"},{"_id":"819"}],"_id":"46401","status":"public","abstract":[{"lang":"eng","text":"Exploratory Landscape Analysis subsumes a number of techniques employed to obtain knowledge about the properties of an unknown optimization problem, especially insofar as these properties are important for the performance of optimization algorithms. Where in a first attempt, one could rely on high-level features designed by experts, we approach the problem from a different angle here, namely by using relatively cheap low-level computer generated features. Interestingly, very few features are needed to separate the BBOB problem groups and also for relating a problem to high-level, expert designed features, paving the way for automatic algorithm selection."}],"type":"conference","publication":"Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation","doi":"10.1145/2001576.2001690","title":"Exploratory Landscape Analysis","date_created":"2023-08-04T15:58:22Z","author":[{"first_name":"Olaf","last_name":"Mersmann","full_name":"Mersmann, Olaf"},{"first_name":"Bernd","last_name":"Bischl","full_name":"Bischl, Bernd"},{"first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","id":"100740","full_name":"Trautmann, Heike"},{"full_name":"Preuss, Mike","last_name":"Preuss","first_name":"Mike"},{"last_name":"Weihs","full_name":"Weihs, Claus","first_name":"Claus"},{"last_name":"Rudolph","full_name":"Rudolph, Günter","first_name":"Günter"}],"publisher":"Association for Computing Machinery","date_updated":"2023-10-16T13:54:34Z","citation":{"ieee":"O. Mersmann, B. Bischl, H. Trautmann, M. Preuss, C. Weihs, and G. Rudolph, “Exploratory Landscape Analysis,” in <i>Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation</i>, 2011, pp. 829–836, doi: <a href=\"https://doi.org/10.1145/2001576.2001690\">10.1145/2001576.2001690</a>.","chicago":"Mersmann, Olaf, Bernd Bischl, Heike Trautmann, Mike Preuss, Claus Weihs, and Günter Rudolph. “Exploratory Landscape Analysis.” In <i>Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation</i>, 829–836. GECCO ’11. New York, NY, USA: Association for Computing Machinery, 2011. <a href=\"https://doi.org/10.1145/2001576.2001690\">https://doi.org/10.1145/2001576.2001690</a>.","ama":"Mersmann O, Bischl B, Trautmann H, Preuss M, Weihs C, Rudolph G. Exploratory Landscape Analysis. In: <i>Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation</i>. GECCO ’11. Association for Computing Machinery; 2011:829–836. doi:<a href=\"https://doi.org/10.1145/2001576.2001690\">10.1145/2001576.2001690</a>","apa":"Mersmann, O., Bischl, B., Trautmann, H., Preuss, M., Weihs, C., &#38; Rudolph, G. (2011). Exploratory Landscape Analysis. <i>Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation</i>, 829–836. <a href=\"https://doi.org/10.1145/2001576.2001690\">https://doi.org/10.1145/2001576.2001690</a>","short":"O. Mersmann, B. Bischl, H. Trautmann, M. Preuss, C. Weihs, G. Rudolph, in: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, Association for Computing Machinery, New York, NY, USA, 2011, pp. 829–836.","bibtex":"@inproceedings{Mersmann_Bischl_Trautmann_Preuss_Weihs_Rudolph_2011, place={New York, NY, USA}, series={GECCO ’11}, title={Exploratory Landscape Analysis}, DOI={<a href=\"https://doi.org/10.1145/2001576.2001690\">10.1145/2001576.2001690</a>}, booktitle={Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation}, publisher={Association for Computing Machinery}, author={Mersmann, Olaf and Bischl, Bernd and Trautmann, Heike and Preuss, Mike and Weihs, Claus and Rudolph, Günter}, year={2011}, pages={829–836}, collection={GECCO ’11} }","mla":"Mersmann, Olaf, et al. “Exploratory Landscape Analysis.” <i>Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation</i>, Association for Computing Machinery, 2011, pp. 829–836, doi:<a href=\"https://doi.org/10.1145/2001576.2001690\">10.1145/2001576.2001690</a>."},"page":"829–836","year":"2011","place":"New York, NY, USA","publication_identifier":{"isbn":["9781450305570"]}},{"doi":"https://doi.org/10.1007/978-3-642-19893-9_2","title":"A Taxonomy of Online Stopping Criteria for Multi-Objective Evolutionary Algorithms","author":[{"last_name":"Wagner","full_name":"Wagner, Tobias","first_name":"Tobias"},{"orcid":"0000-0002-9788-8282","last_name":"Trautmann","full_name":"Trautmann, Heike","id":"100740","first_name":"Heike"},{"first_name":"Luis","last_name":"Martí","full_name":"Martí, Luis"}],"date_created":"2023-08-04T15:59:18Z","publisher":"Springer Berlin Heidelberg","date_updated":"2023-10-16T13:54:50Z","page":"16–30","citation":{"apa":"Wagner, T., Trautmann, H., &#38; Martí, L. (2011). A Taxonomy of Online Stopping Criteria for Multi-Objective Evolutionary Algorithms. In R. H. C. Takahashi, K. Deb, E. F. Wanner, &#38; S. Greco (Eds.), <i>Evolutionary Multi-Criterion Optimization</i> (pp. 16–30). Springer Berlin Heidelberg. <a href=\"https://doi.org/10.1007/978-3-642-19893-9_2\">https://doi.org/10.1007/978-3-642-19893-9_2</a>","mla":"Wagner, Tobias, et al. “A Taxonomy of Online Stopping Criteria for Multi-Objective Evolutionary Algorithms.” <i>Evolutionary Multi-Criterion Optimization</i>, edited by Ricardo H. C. Takahashi et al., Springer Berlin Heidelberg, 2011, pp. 16–30, doi:<a href=\"https://doi.org/10.1007/978-3-642-19893-9_2\">https://doi.org/10.1007/978-3-642-19893-9_2</a>.","short":"T. Wagner, H. Trautmann, L. Martí, in: R.H.C. Takahashi, K. Deb, E.F. Wanner, S. Greco (Eds.), Evolutionary Multi-Criterion Optimization, Springer Berlin Heidelberg, Berlin, Heidelberg, 2011, pp. 16–30.","bibtex":"@inproceedings{Wagner_Trautmann_Martí_2011, place={Berlin, Heidelberg}, title={A Taxonomy of Online Stopping Criteria for Multi-Objective Evolutionary Algorithms}, DOI={<a href=\"https://doi.org/10.1007/978-3-642-19893-9_2\">https://doi.org/10.1007/978-3-642-19893-9_2</a>}, booktitle={Evolutionary Multi-Criterion Optimization}, publisher={Springer Berlin Heidelberg}, author={Wagner, Tobias and Trautmann, Heike and Martí, Luis}, editor={Takahashi, Ricardo H. C. and Deb, Kalyanmoy and Wanner, Elizabeth F. and Greco, Salvatore}, year={2011}, pages={16–30} }","ama":"Wagner T, Trautmann H, Martí L. A Taxonomy of Online Stopping Criteria for Multi-Objective Evolutionary Algorithms. In: Takahashi RHC, Deb K, Wanner EF, Greco S, eds. <i>Evolutionary Multi-Criterion Optimization</i>. Springer Berlin Heidelberg; 2011:16–30. doi:<a href=\"https://doi.org/10.1007/978-3-642-19893-9_2\">https://doi.org/10.1007/978-3-642-19893-9_2</a>","ieee":"T. Wagner, H. Trautmann, and L. Martí, “A Taxonomy of Online Stopping Criteria for Multi-Objective Evolutionary Algorithms,” in <i>Evolutionary Multi-Criterion Optimization</i>, 2011, pp. 16–30, doi: <a href=\"https://doi.org/10.1007/978-3-642-19893-9_2\">https://doi.org/10.1007/978-3-642-19893-9_2</a>.","chicago":"Wagner, Tobias, Heike Trautmann, and Luis Martí. “A Taxonomy of Online Stopping Criteria for Multi-Objective Evolutionary Algorithms.” In <i>Evolutionary Multi-Criterion Optimization</i>, edited by Ricardo H. C. Takahashi, Kalyanmoy Deb, Elizabeth F. Wanner, and Salvatore Greco, 16–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. <a href=\"https://doi.org/10.1007/978-3-642-19893-9_2\">https://doi.org/10.1007/978-3-642-19893-9_2</a>."},"year":"2011","place":"Berlin, Heidelberg","publication_identifier":{"isbn":["978-3-642-19893-9"]},"language":[{"iso":"eng"}],"department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","_id":"46402","status":"public","editor":[{"first_name":"Ricardo H. C.","full_name":"Takahashi, Ricardo H. C.","last_name":"Takahashi"},{"full_name":"Deb, Kalyanmoy","last_name":"Deb","first_name":"Kalyanmoy"},{"last_name":"Wanner","full_name":"Wanner, Elizabeth F.","first_name":"Elizabeth F."},{"first_name":"Salvatore","full_name":"Greco, Salvatore","last_name":"Greco"}],"abstract":[{"text":"The use of multi-objective evolutionary algorithms for solving black-box problems with multiple conflicting objectives has become an important research area. However, when no gradient information is available, the examination of formal convergence or optimality criteria is often impossible. Thus, sophisticated heuristic online stopping criteria (OSC) have recently become subject of intensive research. In order to establish formal guidelines for a systematic research, we present a taxonomy of OSC in this paper. We integrate the known approaches within the taxonomy and discuss them by extracting their building blocks. The formal structure of the taxonomy is used as a basis for the implementation of a comprehensive MATLAB toolbox. Both contributions, the formal taxonomy and the MATLAB implementation, provide a framework for the analysis and evaluation of existing and new OSC approaches.","lang":"eng"}],"publication":"Evolutionary Multi-Criterion Optimization","type":"conference"},{"year":"2011","citation":{"short":"B. Naujoks, H. Trautmann, S. Wessing, C. Weihs, Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 225 (2011) 1081–1096.","bibtex":"@article{Naujoks_Trautmann_Wessing_Weihs_2011, title={Advanced concepts for multi-objective evolutionary optimization in aircraft industry}, volume={225}, DOI={<a href=\"https://doi.org/10.1177/0954410011414120\">10.1177/0954410011414120</a>}, number={10}, journal={Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering}, author={Naujoks, B and Trautmann, Heike and Wessing, S and Weihs, C}, year={2011}, pages={1081–1096} }","mla":"Naujoks, B., et al. “Advanced Concepts for Multi-Objective Evolutionary Optimization in Aircraft Industry.” <i>Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering</i>, vol. 225, no. 10, 2011, pp. 1081–96, doi:<a href=\"https://doi.org/10.1177/0954410011414120\">10.1177/0954410011414120</a>.","apa":"Naujoks, B., Trautmann, H., Wessing, S., &#38; Weihs, C. (2011). Advanced concepts for multi-objective evolutionary optimization in aircraft industry. <i>Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering</i>, <i>225</i>(10), 1081–1096. <a href=\"https://doi.org/10.1177/0954410011414120\">https://doi.org/10.1177/0954410011414120</a>","ama":"Naujoks B, Trautmann H, Wessing S, Weihs C. Advanced concepts for multi-objective evolutionary optimization in aircraft industry. <i>Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering</i>. 2011;225(10):1081-1096. doi:<a href=\"https://doi.org/10.1177/0954410011414120\">10.1177/0954410011414120</a>","chicago":"Naujoks, B, Heike Trautmann, S Wessing, and C Weihs. “Advanced Concepts for Multi-Objective Evolutionary Optimization in Aircraft Industry.” <i>Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering</i> 225, no. 10 (2011): 1081–96. <a href=\"https://doi.org/10.1177/0954410011414120\">https://doi.org/10.1177/0954410011414120</a>.","ieee":"B. Naujoks, H. Trautmann, S. Wessing, and C. Weihs, “Advanced concepts for multi-objective evolutionary optimization in aircraft industry,” <i>Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering</i>, vol. 225, no. 10, pp. 1081–1096, 2011, doi: <a href=\"https://doi.org/10.1177/0954410011414120\">10.1177/0954410011414120</a>."},"intvolume":"       225","page":"1081-1096","issue":"10","title":"Advanced concepts for multi-objective evolutionary optimization in aircraft industry","doi":"10.1177/0954410011414120","date_updated":"2023-10-16T13:55:07Z","author":[{"first_name":"B","full_name":"Naujoks, B","last_name":"Naujoks"},{"full_name":"Trautmann, Heike","id":"100740","last_name":"Trautmann","orcid":"0000-0002-9788-8282","first_name":"Heike"},{"first_name":"S","full_name":"Wessing, S","last_name":"Wessing"},{"first_name":"C","full_name":"Weihs, C","last_name":"Weihs"}],"date_created":"2023-08-04T16:00:28Z","volume":225,"abstract":[{"text":" Evolutionary (multi-objective optimization) algorithms (EMOAs) are widely accepted to be competitive optimization methods in industry today. However, normally only standard techniques are employed by the engineering experts. Here, it is shown how these standard techniques can be completed and improved with respect to interactivity to other tools, runtime, and parameterization. The coupling with metamodels serves as an example for the interactivity to other tools, while the online convergence detection relates to runtime, i.e. stopping criteria. Finally, sequential parameter optimization improves results focussing on parameter tuning. We show that invoking all these methods on their own already enhances EMOAs for aerodynamic applications. It is concluded with an outlook on how these methods might come together to foster aerospace applications and, at a time, widen the application area to multi-disciplinary optimization tasks. ","lang":"eng"}],"status":"public","type":"journal_article","publication":"Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering","language":[{"iso":"eng"}],"_id":"46403","user_id":"15504","department":[{"_id":"34"},{"_id":"819"}]},{"date_created":"2023-08-04T16:06:43Z","author":[{"first_name":"Sanaz","last_name":"Mostaghim","full_name":"Mostaghim, Sanaz"},{"orcid":"0000-0002-9788-8282","last_name":"Trautmann","full_name":"Trautmann, Heike","id":"100740","first_name":"Heike"},{"last_name":"Mersmann","full_name":"Mersmann, Olaf","first_name":"Olaf"}],"date_updated":"2023-10-16T13:56:31Z","publisher":"Springer Berlin Heidelberg","doi":"https://doi.org/10.1007/978-3-642-15871-1_11","title":"Preference-Based Multi-Objective Particle Swarm Optimization Using Desirabilities","publication_identifier":{"isbn":["978-3-642-15871-1"]},"citation":{"chicago":"Mostaghim, Sanaz, Heike Trautmann, and Olaf Mersmann. “Preference-Based Multi-Objective Particle Swarm Optimization Using Desirabilities.” In <i>Parallel Problem Solving from Nature, PPSN XI</i>, edited by Robert Schaefer, Carlos Cotta, Joanna Kołodziej, and Günter Rudolph, 101–110. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. <a href=\"https://doi.org/10.1007/978-3-642-15871-1_11\">https://doi.org/10.1007/978-3-642-15871-1_11</a>.","ieee":"S. Mostaghim, H. Trautmann, and O. Mersmann, “Preference-Based Multi-Objective Particle Swarm Optimization Using Desirabilities,” in <i>Parallel Problem Solving from Nature, PPSN XI</i>, 2010, pp. 101–110, doi: <a href=\"https://doi.org/10.1007/978-3-642-15871-1_11\">https://doi.org/10.1007/978-3-642-15871-1_11</a>.","ama":"Mostaghim S, Trautmann H, Mersmann O. Preference-Based Multi-Objective Particle Swarm Optimization Using Desirabilities. In: Schaefer R, Cotta C, Kołodziej J, Rudolph G, eds. <i>Parallel Problem Solving from Nature, PPSN XI</i>. Springer Berlin Heidelberg; 2010:101–110. doi:<a href=\"https://doi.org/10.1007/978-3-642-15871-1_11\">https://doi.org/10.1007/978-3-642-15871-1_11</a>","short":"S. Mostaghim, H. Trautmann, O. Mersmann, in: R. Schaefer, C. Cotta, J. Kołodziej, G. Rudolph (Eds.), Parallel Problem Solving from Nature, PPSN XI, Springer Berlin Heidelberg, Berlin, Heidelberg, 2010, pp. 101–110.","mla":"Mostaghim, Sanaz, et al. “Preference-Based Multi-Objective Particle Swarm Optimization Using Desirabilities.” <i>Parallel Problem Solving from Nature, PPSN XI</i>, edited by Robert Schaefer et al., Springer Berlin Heidelberg, 2010, pp. 101–110, doi:<a href=\"https://doi.org/10.1007/978-3-642-15871-1_11\">https://doi.org/10.1007/978-3-642-15871-1_11</a>.","bibtex":"@inproceedings{Mostaghim_Trautmann_Mersmann_2010, place={Berlin, Heidelberg}, title={Preference-Based Multi-Objective Particle Swarm Optimization Using Desirabilities}, DOI={<a href=\"https://doi.org/10.1007/978-3-642-15871-1_11\">https://doi.org/10.1007/978-3-642-15871-1_11</a>}, booktitle={Parallel Problem Solving from Nature, PPSN XI}, publisher={Springer Berlin Heidelberg}, author={Mostaghim, Sanaz and Trautmann, Heike and Mersmann, Olaf}, editor={Schaefer, Robert and Cotta, Carlos and Kołodziej, Joanna and Rudolph, Günter}, year={2010}, pages={101–110} }","apa":"Mostaghim, S., Trautmann, H., &#38; Mersmann, O. (2010). Preference-Based Multi-Objective Particle Swarm Optimization Using Desirabilities. In R. Schaefer, C. Cotta, J. Kołodziej, &#38; G. Rudolph (Eds.), <i>Parallel Problem Solving from Nature, PPSN XI</i> (pp. 101–110). Springer Berlin Heidelberg. <a href=\"https://doi.org/10.1007/978-3-642-15871-1_11\">https://doi.org/10.1007/978-3-642-15871-1_11</a>"},"page":"101–110","year":"2010","place":"Berlin, Heidelberg","user_id":"15504","department":[{"_id":"34"},{"_id":"819"}],"_id":"46408","language":[{"iso":"eng"}],"type":"conference","publication":"Parallel Problem Solving from Nature, PPSN XI","status":"public","abstract":[{"lang":"eng","text":"The integration of experts’ preferences is an important aspect in multi-objective optimization. Usually, one out of a set of Pareto optimal solutions has to be chosen based on expert knowledge. A combination of multi-objective particle swarm optimization (MOPSO) with the desirability concept is introduced to efficiently focus on desired and relevant regions of the true Pareto front of the optimization problem which facilitates the solution selection process. Desirability functions of the objectives are optimized, and the desirability index is used for selecting the global best particle in each iteration. The resulting MOPSO variant DF-MOPSO in most cases exclusively generates solutions in the desired area of the Pareto front. Approximations of the whole Pareto front result in cases of misspecified desired regions."}],"editor":[{"first_name":"Robert","last_name":"Schaefer","full_name":"Schaefer, Robert"},{"last_name":"Cotta","full_name":"Cotta, Carlos","first_name":"Carlos"},{"first_name":"Joanna","full_name":"Kołodziej, Joanna","last_name":"Kołodziej"},{"first_name":"Günter","last_name":"Rudolph","full_name":"Rudolph, Günter"}]},{"_id":"46405","series_title":"PPSN’10","user_id":"15504","department":[{"_id":"34"},{"_id":"819"}],"keyword":["benchmarking","multidimensional scaling","consensus ranking","evolutionary optimization","BBOB test set"],"language":[{"iso":"eng"}],"type":"conference","publication":"Proceedings of the 11th International Conference on Parallel Problem Solving from Nature: Part I","abstract":[{"lang":"eng","text":"We present methods to answer two basic questions that arise when benchmarking optimization algorithms. The first one is: which algorithm is the ’best’ one? and the second one: which algorithm should I use for my real world problem? Both are connected and neither is easy to answer. We present methods which can be used to analyse the raw data of a benchmark experiment and derive some insight regarding the answers to these questions. We employ the presented methods to analyse the BBOB’09 benchmark results and present some initial findings."}],"status":"public","publisher":"Springer-Verlag","date_updated":"2023-10-16T13:55:43Z","author":[{"first_name":"Olaf","full_name":"Mersmann, Olaf","last_name":"Mersmann"},{"first_name":"Mike","full_name":"Preuss, Mike","last_name":"Preuss"},{"full_name":"Trautmann, Heike","id":"100740","last_name":"Trautmann","orcid":"0000-0002-9788-8282","first_name":"Heike"}],"date_created":"2023-08-04T16:02:28Z","title":"Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis","publication_identifier":{"isbn":["3642158439"]},"place":"Berlin, Heidelberg","year":"2010","citation":{"apa":"Mersmann, O., Preuss, M., &#38; Trautmann, H. (2010). Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis. <i>Proceedings of the 11th International Conference on Parallel Problem Solving from Nature: Part I</i>, 73–82.","mla":"Mersmann, Olaf, et al. “Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis.” <i>Proceedings of the 11th International Conference on Parallel Problem Solving from Nature: Part I</i>, Springer-Verlag, 2010, pp. 73–82.","short":"O. Mersmann, M. Preuss, H. Trautmann, in: Proceedings of the 11th International Conference on Parallel Problem Solving from Nature: Part I, Springer-Verlag, Berlin, Heidelberg, 2010, pp. 73–82.","bibtex":"@inproceedings{Mersmann_Preuss_Trautmann_2010, place={Berlin, Heidelberg}, series={PPSN’10}, title={Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis}, booktitle={Proceedings of the 11th International Conference on Parallel Problem Solving from Nature: Part I}, publisher={Springer-Verlag}, author={Mersmann, Olaf and Preuss, Mike and Trautmann, Heike}, year={2010}, pages={73–82}, collection={PPSN’10} }","ama":"Mersmann O, Preuss M, Trautmann H. Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis. In: <i>Proceedings of the 11th International Conference on Parallel Problem Solving from Nature: Part I</i>. PPSN’10. Springer-Verlag; 2010:73–82.","ieee":"O. Mersmann, M. Preuss, and H. Trautmann, “Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis,” in <i>Proceedings of the 11th International Conference on Parallel Problem Solving from Nature: Part I</i>, 2010, pp. 73–82.","chicago":"Mersmann, Olaf, Mike Preuss, and Heike Trautmann. “Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis.” In <i>Proceedings of the 11th International Conference on Parallel Problem Solving from Nature: Part I</i>, 73–82. PPSN’10. Berlin, Heidelberg: Springer-Verlag, 2010."},"page":"73–82"},{"title":"On the Distribution of EMOA Hypervolumes","date_created":"2023-08-04T16:03:45Z","author":[{"first_name":"O","last_name":"Mersmann","full_name":"Mersmann, O"},{"orcid":"0000-0002-9788-8282","last_name":"Trautmann","full_name":"Trautmann, Heike","id":"100740","first_name":"Heike"},{"first_name":"B","full_name":"Naujoks, B","last_name":"Naujoks"},{"first_name":"C","full_name":"Weihs, C","last_name":"Weihs"}],"volume":6073,"date_updated":"2023-10-16T13:55:59Z","publisher":"Springer","citation":{"bibtex":"@inproceedings{Mersmann_Trautmann_Naujoks_Weihs_2010, series={Lecture Notes in Computer Science}, title={On the Distribution of EMOA Hypervolumes}, volume={6073}, booktitle={Learning and Intelligent Optimization, 4$^th$ International Conference, LION 4, Venice, Italy}, publisher={Springer}, author={Mersmann, O and Trautmann, Heike and Naujoks, B and Weihs, C}, editor={Blum, C and Battiti, R}, year={2010}, pages={333–337}, collection={Lecture Notes in Computer Science} }","short":"O. Mersmann, H. Trautmann, B. Naujoks, C. Weihs, in: C. Blum, R. Battiti (Eds.), Learning and Intelligent Optimization, 4$^th$ International Conference, LION 4, Venice, Italy, Springer, 2010, pp. 333–337.","mla":"Mersmann, O., et al. “On the Distribution of EMOA Hypervolumes.” <i>Learning and Intelligent Optimization, 4$^th$ International Conference, LION 4, Venice, Italy</i>, edited by C Blum and R Battiti, vol. 6073, Springer, 2010, pp. 333–337.","apa":"Mersmann, O., Trautmann, H., Naujoks, B., &#38; Weihs, C. (2010). On the Distribution of EMOA Hypervolumes. In C. Blum &#38; R. Battiti (Eds.), <i>Learning and Intelligent Optimization, 4$^th$ International Conference, LION 4, Venice, Italy</i> (Vol. 6073, pp. 333–337). Springer.","ama":"Mersmann O, Trautmann H, Naujoks B, Weihs C. On the Distribution of EMOA Hypervolumes. In: Blum C, Battiti R, eds. <i>Learning and Intelligent Optimization, 4$^th$ International Conference, LION 4, Venice, Italy</i>. Vol 6073. Lecture Notes in Computer Science. Springer; 2010:333–337.","chicago":"Mersmann, O, Heike Trautmann, B Naujoks, and C Weihs. “On the Distribution of EMOA Hypervolumes.” In <i>Learning and Intelligent Optimization, 4$^th$ International Conference, LION 4, Venice, Italy</i>, edited by C Blum and R Battiti, 6073:333–337. Lecture Notes in Computer Science. Springer, 2010.","ieee":"O. Mersmann, H. Trautmann, B. Naujoks, and C. Weihs, “On the Distribution of EMOA Hypervolumes,” in <i>Learning and Intelligent Optimization, 4$^th$ International Conference, LION 4, Venice, Italy</i>, 2010, vol. 6073, pp. 333–337."},"page":"333–337","intvolume":"      6073","year":"2010","language":[{"iso":"eng"}],"user_id":"15504","series_title":"Lecture Notes in Computer Science","department":[{"_id":"34"},{"_id":"819"}],"_id":"46406","status":"public","abstract":[{"text":"We present methods to answer two basic questions that arise when benchmarking optimization algorithms. The first one is: which algorithm is the 'best' one? and the second one: which algorithm should I use for my real world problem? Both are connected and neither is easy to answer. We present methods which can be used to analyse the raw data of a benchmark experiment and derive some insight regarding the answers to these questions. We employ the presented methods to analyse the BBOB'09 benchmark results and present some initial findings.","lang":"eng"}],"editor":[{"first_name":"C","full_name":"Blum, C","last_name":"Blum"},{"last_name":"Battiti","full_name":"Battiti, R","first_name":"R"}],"type":"conference","publication":"Learning and Intelligent Optimization, 4$^th$ International Conference, LION 4, Venice, Italy"},{"date_updated":"2023-10-16T13:56:15Z","author":[{"last_name":"Mersmann","full_name":"Mersmann, Olaf","first_name":"Olaf"},{"orcid":"0000-0002-9788-8282","last_name":"Trautmann","full_name":"Trautmann, Heike","id":"100740","first_name":"Heike"},{"first_name":"Boris","full_name":"Naujoks, Boris","last_name":"Naujoks"},{"first_name":"Claus","last_name":"Weihs","full_name":"Weihs, Claus"}],"date_created":"2023-08-04T16:05:53Z","title":"Benchmarking evolutionary multiobjective optimization algorithms","doi":"10.1109/CEC.2010.5586241","publication_identifier":{"issn":["1941-0026"]},"year":"2010","citation":{"short":"O. Mersmann, H. Trautmann, B. Naujoks, C. Weihs, in: IEEE Congress on Evolutionary Computation, 2010, pp. 1–8.","mla":"Mersmann, Olaf, et al. “Benchmarking Evolutionary Multiobjective Optimization Algorithms.” <i>IEEE Congress on Evolutionary Computation</i>, 2010, pp. 1–8, doi:<a href=\"https://doi.org/10.1109/CEC.2010.5586241\">10.1109/CEC.2010.5586241</a>.","bibtex":"@inproceedings{Mersmann_Trautmann_Naujoks_Weihs_2010, title={Benchmarking evolutionary multiobjective optimization algorithms}, DOI={<a href=\"https://doi.org/10.1109/CEC.2010.5586241\">10.1109/CEC.2010.5586241</a>}, booktitle={IEEE Congress on Evolutionary Computation}, author={Mersmann, Olaf and Trautmann, Heike and Naujoks, Boris and Weihs, Claus}, year={2010}, pages={1–8} }","apa":"Mersmann, O., Trautmann, H., Naujoks, B., &#38; Weihs, C. (2010). Benchmarking evolutionary multiobjective optimization algorithms. <i>IEEE Congress on Evolutionary Computation</i>, 1–8. <a href=\"https://doi.org/10.1109/CEC.2010.5586241\">https://doi.org/10.1109/CEC.2010.5586241</a>","ama":"Mersmann O, Trautmann H, Naujoks B, Weihs C. Benchmarking evolutionary multiobjective optimization algorithms. In: <i>IEEE Congress on Evolutionary Computation</i>. ; 2010:1-8. doi:<a href=\"https://doi.org/10.1109/CEC.2010.5586241\">10.1109/CEC.2010.5586241</a>","ieee":"O. Mersmann, H. Trautmann, B. Naujoks, and C. Weihs, “Benchmarking evolutionary multiobjective optimization algorithms,” in <i>IEEE Congress on Evolutionary Computation</i>, 2010, pp. 1–8, doi: <a href=\"https://doi.org/10.1109/CEC.2010.5586241\">10.1109/CEC.2010.5586241</a>.","chicago":"Mersmann, Olaf, Heike Trautmann, Boris Naujoks, and Claus Weihs. “Benchmarking Evolutionary Multiobjective Optimization Algorithms.” In <i>IEEE Congress on Evolutionary Computation</i>, 1–8, 2010. <a href=\"https://doi.org/10.1109/CEC.2010.5586241\">https://doi.org/10.1109/CEC.2010.5586241</a>."},"page":"1-8","_id":"46407","user_id":"15504","department":[{"_id":"34"},{"_id":"819"}],"language":[{"iso":"eng"}],"type":"conference","publication":"IEEE Congress on Evolutionary Computation","abstract":[{"text":"Choosing and tuning an optimization procedure for a given class of nonlinear optimization problems is not an easy task. One way to proceed is to consider this as a tournament, where each procedure will compete in different ‘disciplines’. Here, disciplines could either be different functions, which we want to optimize, or specific performance measures of the optimization procedure. We would then be interested in the algorithm that performs best in a majority of cases or whose average performance is maximal. We will focus on evolutionary multiobjective optimization algorithms (EMOA), and will present a novel approach to the design and analysis of evolutionary multiobjective benchmark experiments based on similar work from the context of machine learning. We focus on deriving a consensus among several benchmarks over different test problems and illustrate the methodology by reanalyzing the results of the CEC 2007 EMOA competition.","lang":"eng"}],"status":"public"},{"language":[{"iso":"eng"}],"department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","_id":"46404","status":"public","editor":[{"first_name":"R","full_name":"Teti, R","last_name":"Teti"}],"publication":"Proceedings of the 7$^th$ CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (CIRP ICME ’10)","type":"conference","title":"Sequential Parameter Optimisation for Multi-Objective Evolutionary Optimisation of Additive Layer Manufacturing","author":[{"last_name":"Ding","full_name":"Ding, J","first_name":"J"},{"full_name":"Wessing, S","last_name":"Wessing","first_name":"S"},{"orcid":"0000-0002-9788-8282","last_name":"Trautmann","full_name":"Trautmann, Heike","id":"100740","first_name":"Heike"},{"first_name":"J","last_name":"Mehnen","full_name":"Mehnen, J"},{"last_name":"Naujoks","full_name":"Naujoks, B","first_name":"B"}],"date_created":"2023-08-04T16:01:38Z","publisher":"Copyright C.O.C. Com. org. Conv.","date_updated":"2023-10-16T13:55:25Z","citation":{"chicago":"Ding, J, S Wessing, Heike Trautmann, J Mehnen, and B Naujoks. “Sequential Parameter Optimisation for Multi-Objective Evolutionary Optimisation of Additive Layer Manufacturing.” In <i>Proceedings of the 7$^th$ CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (CIRP ICME ’10)</i>, edited by R Teti. Capri, Italy: Copyright C.O.C. Com. org. Conv., 2010.","ieee":"J. Ding, S. Wessing, H. Trautmann, J. Mehnen, and B. Naujoks, “Sequential Parameter Optimisation for Multi-Objective Evolutionary Optimisation of Additive Layer Manufacturing,” in <i>Proceedings of the 7$^th$ CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (CIRP ICME ’10)</i>, 2010.","ama":"Ding J, Wessing S, Trautmann H, Mehnen J, Naujoks B. Sequential Parameter Optimisation for Multi-Objective Evolutionary Optimisation of Additive Layer Manufacturing. In: Teti R, ed. <i>Proceedings of the 7$^th$ CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (CIRP ICME ’10)</i>. Copyright C.O.C. Com. org. Conv.; 2010.","apa":"Ding, J., Wessing, S., Trautmann, H., Mehnen, J., &#38; Naujoks, B. (2010). Sequential Parameter Optimisation for Multi-Objective Evolutionary Optimisation of Additive Layer Manufacturing. In R. Teti (Ed.), <i>Proceedings of the 7$^th$ CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (CIRP ICME ’10)</i>. Copyright C.O.C. Com. org. Conv.","bibtex":"@inproceedings{Ding_Wessing_Trautmann_Mehnen_Naujoks_2010, place={Capri, Italy}, title={Sequential Parameter Optimisation for Multi-Objective Evolutionary Optimisation of Additive Layer Manufacturing}, booktitle={Proceedings of the 7$^th$ CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (CIRP ICME ’10)}, publisher={Copyright C.O.C. Com. org. Conv.}, author={Ding, J and Wessing, S and Trautmann, Heike and Mehnen, J and Naujoks, B}, editor={Teti, R}, year={2010} }","mla":"Ding, J., et al. “Sequential Parameter Optimisation for Multi-Objective Evolutionary Optimisation of Additive Layer Manufacturing.” <i>Proceedings of the 7$^th$ CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (CIRP ICME ’10)</i>, edited by R Teti, Copyright C.O.C. Com. org. Conv., 2010.","short":"J. Ding, S. Wessing, H. Trautmann, J. Mehnen, B. Naujoks, in: R. Teti (Ed.), Proceedings of the 7$^th$ CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (CIRP ICME ’10), Copyright C.O.C. Com. org. Conv., Capri, Italy, 2010."},"place":"Capri, Italy","year":"2010"},{"page":"260–269","citation":{"ama":"Voß T, Trautmann H, Igel C. New Uncertainty Handling Strategies in Multi-objective Evolutionary Optimization. In: Schaefer R, Cotta C, Kołodziej J, Rudolph G, eds. <i>Parallel Problem Solving from Nature, PPSN XI</i>. Springer Berlin Heidelberg; 2010:260–269. doi:<a href=\"https://doi.org/10.1007/978-3-642-15871-1_27\">https://doi.org/10.1007/978-3-642-15871-1_27</a>","chicago":"Voß, Thomas, Heike Trautmann, and Christian Igel. “New Uncertainty Handling Strategies in Multi-Objective Evolutionary Optimization.” In <i>Parallel Problem Solving from Nature, PPSN XI</i>, edited by Robert Schaefer, Carlos Cotta, Joanna Kołodziej, and Günter Rudolph, 260–269. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. <a href=\"https://doi.org/10.1007/978-3-642-15871-1_27\">https://doi.org/10.1007/978-3-642-15871-1_27</a>.","ieee":"T. Voß, H. Trautmann, and C. Igel, “New Uncertainty Handling Strategies in Multi-objective Evolutionary Optimization,” in <i>Parallel Problem Solving from Nature, PPSN XI</i>, 2010, pp. 260–269, doi: <a href=\"https://doi.org/10.1007/978-3-642-15871-1_27\">https://doi.org/10.1007/978-3-642-15871-1_27</a>.","apa":"Voß, T., Trautmann, H., &#38; Igel, C. (2010). New Uncertainty Handling Strategies in Multi-objective Evolutionary Optimization. In R. Schaefer, C. Cotta, J. Kołodziej, &#38; G. Rudolph (Eds.), <i>Parallel Problem Solving from Nature, PPSN XI</i> (pp. 260–269). Springer Berlin Heidelberg. <a href=\"https://doi.org/10.1007/978-3-642-15871-1_27\">https://doi.org/10.1007/978-3-642-15871-1_27</a>","bibtex":"@inproceedings{Voß_Trautmann_Igel_2010, place={Berlin, Heidelberg}, title={New Uncertainty Handling Strategies in Multi-objective Evolutionary Optimization}, DOI={<a href=\"https://doi.org/10.1007/978-3-642-15871-1_27\">https://doi.org/10.1007/978-3-642-15871-1_27</a>}, booktitle={Parallel Problem Solving from Nature, PPSN XI}, publisher={Springer Berlin Heidelberg}, author={Voß, Thomas and Trautmann, Heike and Igel, Christian}, editor={Schaefer, Robert and Cotta, Carlos and Kołodziej, Joanna and Rudolph, Günter}, year={2010}, pages={260–269} }","mla":"Voß, Thomas, et al. “New Uncertainty Handling Strategies in Multi-Objective Evolutionary Optimization.” <i>Parallel Problem Solving from Nature, PPSN XI</i>, edited by Robert Schaefer et al., Springer Berlin Heidelberg, 2010, pp. 260–269, doi:<a href=\"https://doi.org/10.1007/978-3-642-15871-1_27\">https://doi.org/10.1007/978-3-642-15871-1_27</a>.","short":"T. Voß, H. Trautmann, C. Igel, in: R. Schaefer, C. Cotta, J. Kołodziej, G. Rudolph (Eds.), Parallel Problem Solving from Nature, PPSN XI, Springer Berlin Heidelberg, Berlin, Heidelberg, 2010, pp. 260–269."},"year":"2010","place":"Berlin, Heidelberg","publication_identifier":{"isbn":["978-3-642-15871-1"]},"doi":"https://doi.org/10.1007/978-3-642-15871-1_27","title":"New Uncertainty Handling Strategies in Multi-objective Evolutionary Optimization","author":[{"last_name":"Voß","full_name":"Voß, Thomas","first_name":"Thomas"},{"id":"100740","full_name":"Trautmann, Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike"},{"last_name":"Igel","full_name":"Igel, Christian","first_name":"Christian"}],"date_created":"2023-08-04T16:07:48Z","publisher":"Springer Berlin Heidelberg","date_updated":"2023-10-16T13:56:48Z","status":"public","abstract":[{"text":"Since many real-world optimization problems are noisy, vector optimization algorithms that can cope with noise and uncertainty are required. We propose new, robust selection strategies for evolutionary multi-objective optimization in the presence of noise. We apply new measures of uncertainty for estimating the recently introduced Pareto-dominance for uncertain and noisy environments (PDU). The first measure is the inter-quartile range of the outcomes of repeated function evaluations. The second is based on axis-aligned bounding boxes around the upper and lower quantiles of the sampled fitness values in objective space. Experiments on real and artificial problems show promising results.","lang":"eng"}],"editor":[{"full_name":"Schaefer, Robert","last_name":"Schaefer","first_name":"Robert"},{"first_name":"Carlos","last_name":"Cotta","full_name":"Cotta, Carlos"},{"first_name":"Joanna","full_name":"Kołodziej, Joanna","last_name":"Kołodziej"},{"last_name":"Rudolph","full_name":"Rudolph, Günter","first_name":"Günter"}],"publication":"Parallel Problem Solving from Nature, PPSN XI","type":"conference","language":[{"iso":"eng"}],"department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","_id":"46409"},{"type":"journal_article","publication":"IEEE Transactions on Evolutionary Computation","status":"public","abstract":[{"lang":"eng","text":"In this paper, a concept for efficiently approximating the practically relevant regions of the Pareto front (PF) is introduced. Instead of the original objectives, desirability functions (DFs) of the objectives are optimized, which express the preferences of the decision maker. The original problem formulation and the optimization algorithm do not have to be modified. DFs map an objective to the domain [0, 1] and nonlinearly increase with better objective quality. By means of this mapping, values of different objectives and units become comparable. A biased distribution of the solutions in the PF approximation based on different scalings of the objectives is prevented. Thus, we propose the integration of DFs into the S-metric selection evolutionary multiobjective algorithm. The transformation ensures the meaning of the hypervolumes internally computed. Furthermore, it is shown that the reference point for the hypervolume calculation can be set intuitively. The approach is analyzed using standard test problems. Moreover, a practical validation by means of the optimization of a turning process is performed."}],"user_id":"15504","department":[{"_id":"34"},{"_id":"819"}],"_id":"46412","language":[{"iso":"eng"}],"issue":"5","publication_identifier":{"issn":["1941-0026"]},"citation":{"ieee":"T. Wagner and H. Trautmann, “Integration of Preferences in Hypervolume-Based Multiobjective Evolutionary Algorithms by Means of Desirability Functions,” <i>IEEE Transactions on Evolutionary Computation</i>, vol. 14, no. 5, pp. 688–701, 2010, doi: <a href=\"https://doi.org/10.1109/TEVC.2010.2058119\">10.1109/TEVC.2010.2058119</a>.","chicago":"Wagner, Tobias, and Heike Trautmann. “Integration of Preferences in Hypervolume-Based Multiobjective Evolutionary Algorithms by Means of Desirability Functions.” <i>IEEE Transactions on Evolutionary Computation</i> 14, no. 5 (2010): 688–701. <a href=\"https://doi.org/10.1109/TEVC.2010.2058119\">https://doi.org/10.1109/TEVC.2010.2058119</a>.","ama":"Wagner T, Trautmann H. Integration of Preferences in Hypervolume-Based Multiobjective Evolutionary Algorithms by Means of Desirability Functions. <i>IEEE Transactions on Evolutionary Computation</i>. 2010;14(5):688-701. doi:<a href=\"https://doi.org/10.1109/TEVC.2010.2058119\">10.1109/TEVC.2010.2058119</a>","apa":"Wagner, T., &#38; Trautmann, H. (2010). Integration of Preferences in Hypervolume-Based Multiobjective Evolutionary Algorithms by Means of Desirability Functions. <i>IEEE Transactions on Evolutionary Computation</i>, <i>14</i>(5), 688–701. <a href=\"https://doi.org/10.1109/TEVC.2010.2058119\">https://doi.org/10.1109/TEVC.2010.2058119</a>","bibtex":"@article{Wagner_Trautmann_2010, title={Integration of Preferences in Hypervolume-Based Multiobjective Evolutionary Algorithms by Means of Desirability Functions}, volume={14}, DOI={<a href=\"https://doi.org/10.1109/TEVC.2010.2058119\">10.1109/TEVC.2010.2058119</a>}, number={5}, journal={IEEE Transactions on Evolutionary Computation}, author={Wagner, Tobias and Trautmann, Heike}, year={2010}, pages={688–701} }","mla":"Wagner, Tobias, and Heike Trautmann. “Integration of Preferences in Hypervolume-Based Multiobjective Evolutionary Algorithms by Means of Desirability Functions.” <i>IEEE Transactions on Evolutionary Computation</i>, vol. 14, no. 5, 2010, pp. 688–701, doi:<a href=\"https://doi.org/10.1109/TEVC.2010.2058119\">10.1109/TEVC.2010.2058119</a>.","short":"T. Wagner, H. Trautmann, IEEE Transactions on Evolutionary Computation 14 (2010) 688–701."},"page":"688-701","intvolume":"        14","year":"2010","date_created":"2023-08-04T16:10:02Z","author":[{"last_name":"Wagner","full_name":"Wagner, Tobias","first_name":"Tobias"},{"first_name":"Heike","full_name":"Trautmann, Heike","id":"100740","last_name":"Trautmann","orcid":"0000-0002-9788-8282"}],"volume":14,"date_updated":"2023-10-16T13:57:41Z","doi":"10.1109/TEVC.2010.2058119","title":"Integration of Preferences in Hypervolume-Based Multiobjective Evolutionary Algorithms by Means of Desirability Functions"},{"doi":"https://doi.org/10.1016/j.cirpj.2010.06.001","title":"Work roll cooling system design optimisation in presence of uncertainty and constrains","volume":2,"author":[{"last_name":"Azene","full_name":"Azene, Y.T.","first_name":"Y.T."},{"first_name":"R.","last_name":"Roy","full_name":"Roy, R."},{"last_name":"Farrugia","full_name":"Farrugia, D.","first_name":"D."},{"last_name":"Onisa","full_name":"Onisa, C.","first_name":"C."},{"first_name":"J.","full_name":"Mehnen, J.","last_name":"Mehnen"},{"last_name":"Trautmann","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike","id":"100740","first_name":"Heike"}],"date_created":"2023-08-04T16:09:19Z","date_updated":"2023-10-16T13:57:23Z","page":"290-298","intvolume":"         2","citation":{"ama":"Azene YT, Roy R, Farrugia D, Onisa C, Mehnen J, Trautmann H. Work roll cooling system design optimisation in presence of uncertainty and constrains. <i>CIRP Journal of Manufacturing Science and Technology</i>. 2010;2(4):290-298. doi:<a href=\"https://doi.org/10.1016/j.cirpj.2010.06.001\">https://doi.org/10.1016/j.cirpj.2010.06.001</a>","apa":"Azene, Y. T., Roy, R., Farrugia, D., Onisa, C., Mehnen, J., &#38; Trautmann, H. (2010). Work roll cooling system design optimisation in presence of uncertainty and constrains. <i>CIRP Journal of Manufacturing Science and Technology</i>, <i>2</i>(4), 290–298. <a href=\"https://doi.org/10.1016/j.cirpj.2010.06.001\">https://doi.org/10.1016/j.cirpj.2010.06.001</a>","short":"Y.T. Azene, R. Roy, D. Farrugia, C. Onisa, J. Mehnen, H. Trautmann, CIRP Journal of Manufacturing Science and Technology 2 (2010) 290–298.","bibtex":"@article{Azene_Roy_Farrugia_Onisa_Mehnen_Trautmann_2010, title={Work roll cooling system design optimisation in presence of uncertainty and constrains}, volume={2}, DOI={<a href=\"https://doi.org/10.1016/j.cirpj.2010.06.001\">https://doi.org/10.1016/j.cirpj.2010.06.001</a>}, number={4}, journal={CIRP Journal of Manufacturing Science and Technology}, author={Azene, Y.T. and Roy, R. and Farrugia, D. and Onisa, C. and Mehnen, J. and Trautmann, Heike}, year={2010}, pages={290–298} }","mla":"Azene, Y. T., et al. “Work Roll Cooling System Design Optimisation in Presence of Uncertainty and Constrains.” <i>CIRP Journal of Manufacturing Science and Technology</i>, vol. 2, no. 4, 2010, pp. 290–98, doi:<a href=\"https://doi.org/10.1016/j.cirpj.2010.06.001\">https://doi.org/10.1016/j.cirpj.2010.06.001</a>.","ieee":"Y. T. Azene, R. Roy, D. Farrugia, C. Onisa, J. Mehnen, and H. Trautmann, “Work roll cooling system design optimisation in presence of uncertainty and constrains,” <i>CIRP Journal of Manufacturing Science and Technology</i>, vol. 2, no. 4, pp. 290–298, 2010, doi: <a href=\"https://doi.org/10.1016/j.cirpj.2010.06.001\">https://doi.org/10.1016/j.cirpj.2010.06.001</a>.","chicago":"Azene, Y.T., R. Roy, D. Farrugia, C. Onisa, J. Mehnen, and Heike Trautmann. “Work Roll Cooling System Design Optimisation in Presence of Uncertainty and Constrains.” <i>CIRP Journal of Manufacturing Science and Technology</i> 2, no. 4 (2010): 290–98. <a href=\"https://doi.org/10.1016/j.cirpj.2010.06.001\">https://doi.org/10.1016/j.cirpj.2010.06.001</a>."},"year":"2010","issue":"4","publication_identifier":{"issn":["1755-5817"]},"language":[{"iso":"eng"}],"keyword":["Roll cooling design","Uncertainty","Design optimisation","Multi-objective optimisation","Constraint in design"],"department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","_id":"46411","status":"public","abstract":[{"lang":"eng","text":"The paper presents a framework to optimise the design of work roll based on the cooling performance. The framework develops meta-models from a set of finite element analyses (FEA) of the roll cooling. A design of experiment technique is used to identify the FEA runs. The research also identifies sources of uncertainties in the design process. A robust evolutionary multi-objective evaluation technique is applied to the design optimisation in constrained problems with real life uncertainty. The approach handles uncertainties associated both with design variables and fitness functions. Constraints violation within the neighbourhood of a design is considered as part of a measurement for degree of feasibility and robustness of a solution."}],"publication":"CIRP Journal of Manufacturing Science and Technology","type":"journal_article"},{"date_created":"2023-08-04T16:08:41Z","author":[{"first_name":"Tobias","last_name":"Wagner","full_name":"Wagner, Tobias"},{"orcid":"0000-0002-9788-8282","last_name":"Trautmann","id":"100740","full_name":"Trautmann, Heike","first_name":"Heike"}],"date_updated":"2023-10-16T13:57:05Z","doi":"10.1109/CEC.2010.5586474","title":"Online convergence detection for evolutionary multi-objective algorithms revisited","publication_identifier":{"issn":["1941-0026"]},"page":"1-8","citation":{"chicago":"Wagner, Tobias, and Heike Trautmann. “Online Convergence Detection for Evolutionary Multi-Objective Algorithms Revisited.” In <i>IEEE Congress on Evolutionary Computation</i>, 1–8, 2010. <a href=\"https://doi.org/10.1109/CEC.2010.5586474\">https://doi.org/10.1109/CEC.2010.5586474</a>.","ieee":"T. Wagner and H. Trautmann, “Online convergence detection for evolutionary multi-objective algorithms revisited,” in <i>IEEE Congress on Evolutionary Computation</i>, 2010, pp. 1–8, doi: <a href=\"https://doi.org/10.1109/CEC.2010.5586474\">10.1109/CEC.2010.5586474</a>.","ama":"Wagner T, Trautmann H. Online convergence detection for evolutionary multi-objective algorithms revisited. In: <i>IEEE Congress on Evolutionary Computation</i>. ; 2010:1-8. doi:<a href=\"https://doi.org/10.1109/CEC.2010.5586474\">10.1109/CEC.2010.5586474</a>","apa":"Wagner, T., &#38; Trautmann, H. (2010). Online convergence detection for evolutionary multi-objective algorithms revisited. <i>IEEE Congress on Evolutionary Computation</i>, 1–8. <a href=\"https://doi.org/10.1109/CEC.2010.5586474\">https://doi.org/10.1109/CEC.2010.5586474</a>","short":"T. Wagner, H. Trautmann, in: IEEE Congress on Evolutionary Computation, 2010, pp. 1–8.","mla":"Wagner, Tobias, and Heike Trautmann. “Online Convergence Detection for Evolutionary Multi-Objective Algorithms Revisited.” <i>IEEE Congress on Evolutionary Computation</i>, 2010, pp. 1–8, doi:<a href=\"https://doi.org/10.1109/CEC.2010.5586474\">10.1109/CEC.2010.5586474</a>.","bibtex":"@inproceedings{Wagner_Trautmann_2010, title={Online convergence detection for evolutionary multi-objective algorithms revisited}, DOI={<a href=\"https://doi.org/10.1109/CEC.2010.5586474\">10.1109/CEC.2010.5586474</a>}, booktitle={IEEE Congress on Evolutionary Computation}, author={Wagner, Tobias and Trautmann, Heike}, year={2010}, pages={1–8} }"},"year":"2010","department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","_id":"46410","language":[{"iso":"eng"}],"publication":"IEEE Congress on Evolutionary Computation","type":"conference","status":"public","abstract":[{"text":"The design and application of termination criteria has become an important aspect in evolutionary multi-objective optimization. Online convergence detection (OCD) determines when further generations are no longer promising based on statistical tests on a set of performance indicators. The behavior of OCD mainly depends on two parameters, the number of preceding generations considered in the statistical tests and the desired variance limit. In this paper, guidelines for selecting appropriate combinations of these parameters are empirically derived based on design-of-experiment methods. Furthermore, a variant of OCD is introduced which directly operates on the hypervolume indicator - the internal measure of the SMS-EMOA. This allows a separated analysis of the variance criterion and reduces the complexity of OCD. Based on the experimental design, a systematic comparison with the classical OCD approach is performed and differences between the appropriate parameterizations of both variants are highlighted.","lang":"eng"}]},{"status":"public","abstract":[{"lang":"eng","text":"Over the last decades, evolutionary algorithms (EA) have proven their applicability to hard and complex industrial optimization problems in many cases. However, especially in cases with high computational demands for fitness evaluations (FE), the number of required FE is often seen as a drawback of these techniques. This is partly due to lacking robust and reliable methods to determine convergence, which would stop the algorithm before useless evaluations are carried out. To overcome this drawback, we define a method for online convergence detection (OCD) based on statistical tests, which invokes a number of performance indicators and which can be applied on a stand-alone basis (no predefined Pareto fronts, ideal and reference points). Our experiments show the general applicability of OCD by analyzing its performance for different algorithmic setups and on different classes of test functions. Furthermore, we show that the number of FE can be reduced considerably – compared to common suggestions from literature – without significantly deteriorating approximation accuracy."}],"editor":[{"full_name":"Ehrgott, Matthias","last_name":"Ehrgott","first_name":"Matthias"},{"first_name":"Carlos M.","last_name":"Fonseca","full_name":"Fonseca, Carlos M."},{"full_name":"Gandibleux, Xavier","last_name":"Gandibleux","first_name":"Xavier"},{"first_name":"Jin-Kao","full_name":"Hao, Jin-Kao","last_name":"Hao"},{"first_name":"Marc","full_name":"Sevaux, Marc","last_name":"Sevaux"}],"publication":"Evolutionary Multi-Criterion Optimization","type":"conference","language":[{"iso":"eng"}],"department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","_id":"46414","page":"198–215","citation":{"ama":"Wagner T, Trautmann H, Naujoks B. OCD: Online Convergence Detection for Evolutionary Multi-Objective Algorithms Based on Statistical Testing. In: Ehrgott M, Fonseca CM, Gandibleux X, Hao J-K, Sevaux M, eds. <i>Evolutionary Multi-Criterion Optimization</i>. Springer Berlin Heidelberg; 2009:198–215. doi:<a href=\"https://doi.org/10.1007/978-3-642-01020-0_19\">https://doi.org/10.1007/978-3-642-01020-0_19</a>","chicago":"Wagner, Tobias, Heike Trautmann, and Boris Naujoks. “OCD: Online Convergence Detection for Evolutionary Multi-Objective Algorithms Based on Statistical Testing.” In <i>Evolutionary Multi-Criterion Optimization</i>, edited by Matthias Ehrgott, Carlos M. Fonseca, Xavier Gandibleux, Jin-Kao Hao, and Marc Sevaux, 198–215. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. <a href=\"https://doi.org/10.1007/978-3-642-01020-0_19\">https://doi.org/10.1007/978-3-642-01020-0_19</a>.","ieee":"T. Wagner, H. Trautmann, and B. Naujoks, “OCD: Online Convergence Detection for Evolutionary Multi-Objective Algorithms Based on Statistical Testing,” in <i>Evolutionary Multi-Criterion Optimization</i>, 2009, pp. 198–215, doi: <a href=\"https://doi.org/10.1007/978-3-642-01020-0_19\">https://doi.org/10.1007/978-3-642-01020-0_19</a>.","apa":"Wagner, T., Trautmann, H., &#38; Naujoks, B. (2009). OCD: Online Convergence Detection for Evolutionary Multi-Objective Algorithms Based on Statistical Testing. In M. Ehrgott, C. M. Fonseca, X. Gandibleux, J.-K. Hao, &#38; M. Sevaux (Eds.), <i>Evolutionary Multi-Criterion Optimization</i> (pp. 198–215). Springer Berlin Heidelberg. <a href=\"https://doi.org/10.1007/978-3-642-01020-0_19\">https://doi.org/10.1007/978-3-642-01020-0_19</a>","short":"T. Wagner, H. Trautmann, B. Naujoks, in: M. Ehrgott, C.M. Fonseca, X. Gandibleux, J.-K. Hao, M. Sevaux (Eds.), Evolutionary Multi-Criterion Optimization, Springer Berlin Heidelberg, Berlin, Heidelberg, 2009, pp. 198–215.","mla":"Wagner, Tobias, et al. “OCD: Online Convergence Detection for Evolutionary Multi-Objective Algorithms Based on Statistical Testing.” <i>Evolutionary Multi-Criterion Optimization</i>, edited by Matthias Ehrgott et al., Springer Berlin Heidelberg, 2009, pp. 198–215, doi:<a href=\"https://doi.org/10.1007/978-3-642-01020-0_19\">https://doi.org/10.1007/978-3-642-01020-0_19</a>.","bibtex":"@inproceedings{Wagner_Trautmann_Naujoks_2009, place={Berlin, Heidelberg}, title={OCD: Online Convergence Detection for Evolutionary Multi-Objective Algorithms Based on Statistical Testing}, DOI={<a href=\"https://doi.org/10.1007/978-3-642-01020-0_19\">https://doi.org/10.1007/978-3-642-01020-0_19</a>}, booktitle={Evolutionary Multi-Criterion Optimization}, publisher={Springer Berlin Heidelberg}, author={Wagner, Tobias and Trautmann, Heike and Naujoks, Boris}, editor={Ehrgott, Matthias and Fonseca, Carlos M. and Gandibleux, Xavier and Hao, Jin-Kao and Sevaux, Marc}, year={2009}, pages={198–215} }"},"place":"Berlin, Heidelberg","year":"2009","publication_identifier":{"isbn":["978-3-642-01020-0"]},"doi":"https://doi.org/10.1007/978-3-642-01020-0_19","title":"OCD: Online Convergence Detection for Evolutionary Multi-Objective Algorithms Based on Statistical Testing","author":[{"last_name":"Wagner","full_name":"Wagner, Tobias","first_name":"Tobias"},{"id":"100740","full_name":"Trautmann, Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","first_name":"Heike"},{"first_name":"Boris","full_name":"Naujoks, Boris","last_name":"Naujoks"}],"date_created":"2023-08-04T16:15:04Z","publisher":"Springer Berlin Heidelberg","date_updated":"2023-10-16T13:58:13Z"},{"language":[{"iso":"eng"}],"user_id":"15504","department":[{"_id":"34"},{"_id":"819"}],"_id":"46415","status":"public","abstract":[{"text":"Noisy environments are a challenging task for multiobjective evolutionary algorithms. The algorithms may be trapped in local optima or even become a random search in the decision and objective space. In the course of the paper the classical definition of Pareto-dominance is enhanced subject to noisy objective functions in order to make the evolutionary search process more robust and to generate a reliable Pareto front. At each point in the decision space the objective functions are evaluated a fixed number of times and the convex hull of the objective function vectors is computed. Expectation is associated with the median of the objective function values while uncertainty is reflected by the average distance of the median in each dimension to the points defining the convex hull. By combining these two indicators a new concept of Pareto-dominance is set up. An implementation in NSGA-II and application to test problems show a gain in robustness and search quality.","lang":"eng"}],"type":"conference","publication":"2009 IEEE Congress on Evolutionary Computation","doi":"10.1109/CEC.2009.4983338","title":"Pareto-dominance in noisy environments","author":[{"last_name":"Trautmann","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike","id":"100740","first_name":"Heike"},{"full_name":"Mehnen, Jorn","last_name":"Mehnen","first_name":"Jorn"},{"last_name":"Naujoks","full_name":"Naujoks, Boris","first_name":"Boris"}],"date_created":"2023-08-04T16:16:32Z","date_updated":"2023-10-16T13:58:36Z","citation":{"ieee":"H. Trautmann, J. Mehnen, and B. Naujoks, “Pareto-dominance in noisy environments,” in <i>2009 IEEE Congress on Evolutionary Computation</i>, 2009, pp. 3119–3126, doi: <a href=\"https://doi.org/10.1109/CEC.2009.4983338\">10.1109/CEC.2009.4983338</a>.","chicago":"Trautmann, Heike, Jorn Mehnen, and Boris Naujoks. “Pareto-Dominance in Noisy Environments.” In <i>2009 IEEE Congress on Evolutionary Computation</i>, 3119–26, 2009. <a href=\"https://doi.org/10.1109/CEC.2009.4983338\">https://doi.org/10.1109/CEC.2009.4983338</a>.","ama":"Trautmann H, Mehnen J, Naujoks B. Pareto-dominance in noisy environments. In: <i>2009 IEEE Congress on Evolutionary Computation</i>. ; 2009:3119-3126. doi:<a href=\"https://doi.org/10.1109/CEC.2009.4983338\">10.1109/CEC.2009.4983338</a>","apa":"Trautmann, H., Mehnen, J., &#38; Naujoks, B. (2009). Pareto-dominance in noisy environments. <i>2009 IEEE Congress on Evolutionary Computation</i>, 3119–3126. <a href=\"https://doi.org/10.1109/CEC.2009.4983338\">https://doi.org/10.1109/CEC.2009.4983338</a>","short":"H. Trautmann, J. Mehnen, B. Naujoks, in: 2009 IEEE Congress on Evolutionary Computation, 2009, pp. 3119–3126.","mla":"Trautmann, Heike, et al. “Pareto-Dominance in Noisy Environments.” <i>2009 IEEE Congress on Evolutionary Computation</i>, 2009, pp. 3119–26, doi:<a href=\"https://doi.org/10.1109/CEC.2009.4983338\">10.1109/CEC.2009.4983338</a>.","bibtex":"@inproceedings{Trautmann_Mehnen_Naujoks_2009, title={Pareto-dominance in noisy environments}, DOI={<a href=\"https://doi.org/10.1109/CEC.2009.4983338\">10.1109/CEC.2009.4983338</a>}, booktitle={2009 IEEE Congress on Evolutionary Computation}, author={Trautmann, Heike and Mehnen, Jorn and Naujoks, Boris}, year={2009}, pages={3119–3126} }"},"page":"3119-3126","year":"2009","publication_identifier":{"issn":["1941-0026"]}},{"abstract":[{"text":"Industry applications of multiobjective optimization problems mostly are characterized by the demand for high quality solutions on the one hand. On the other hand an optimization result is desired which at any rate meets the time constraints for the evolutionary multiobjective algorithms (EMOA). The handling of this trade-off is a frequently discussed issue in multiobjective evolutionary optimization.","lang":"eng"}],"status":"public","type":"conference","publication":"2009 IEEE Congress on Evolutionary Computation","language":[{"iso":"eng"}],"_id":"46413","user_id":"15504","department":[{"_id":"34"},{"_id":"819"}],"year":"2009","citation":{"short":"B. Naujoks, H. Trautmann, in: 2009 IEEE Congress on Evolutionary Computation, 2009, pp. 332–339.","mla":"Naujoks, Boris, and Heike Trautmann. “Online Convergence Detection for Multiobjective Aerodynamic Applications.” <i>2009 IEEE Congress on Evolutionary Computation</i>, 2009, pp. 332–39, doi:<a href=\"https://doi.org/10.1109/CEC.2009.4982966\">10.1109/CEC.2009.4982966</a>.","bibtex":"@inproceedings{Naujoks_Trautmann_2009, title={Online convergence detection for multiobjective aerodynamic applications}, DOI={<a href=\"https://doi.org/10.1109/CEC.2009.4982966\">10.1109/CEC.2009.4982966</a>}, booktitle={2009 IEEE Congress on Evolutionary Computation}, author={Naujoks, Boris and Trautmann, Heike}, year={2009}, pages={332–339} }","apa":"Naujoks, B., &#38; Trautmann, H. (2009). Online convergence detection for multiobjective aerodynamic applications. <i>2009 IEEE Congress on Evolutionary Computation</i>, 332–339. <a href=\"https://doi.org/10.1109/CEC.2009.4982966\">https://doi.org/10.1109/CEC.2009.4982966</a>","chicago":"Naujoks, Boris, and Heike Trautmann. “Online Convergence Detection for Multiobjective Aerodynamic Applications.” In <i>2009 IEEE Congress on Evolutionary Computation</i>, 332–39, 2009. <a href=\"https://doi.org/10.1109/CEC.2009.4982966\">https://doi.org/10.1109/CEC.2009.4982966</a>.","ieee":"B. Naujoks and H. Trautmann, “Online convergence detection for multiobjective aerodynamic applications,” in <i>2009 IEEE Congress on Evolutionary Computation</i>, 2009, pp. 332–339, doi: <a href=\"https://doi.org/10.1109/CEC.2009.4982966\">10.1109/CEC.2009.4982966</a>.","ama":"Naujoks B, Trautmann H. Online convergence detection for multiobjective aerodynamic applications. In: <i>2009 IEEE Congress on Evolutionary Computation</i>. ; 2009:332-339. doi:<a href=\"https://doi.org/10.1109/CEC.2009.4982966\">10.1109/CEC.2009.4982966</a>"},"page":"332-339","publication_identifier":{"issn":["1941-0026"]},"title":"Online convergence detection for multiobjective aerodynamic applications","doi":"10.1109/CEC.2009.4982966","date_updated":"2023-10-16T13:57:58Z","author":[{"first_name":"Boris","full_name":"Naujoks, Boris","last_name":"Naujoks"},{"first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","full_name":"Trautmann, Heike","id":"100740"}],"date_created":"2023-08-04T16:14:08Z"},{"publication":"International Journal of Computational Intelligence Research","type":"journal_article","status":"public","_id":"46416","department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","language":[{"iso":"eng"}],"issue":"2","year":"2009","page":"72–78","intvolume":"         5","citation":{"ieee":"H. Trautmann and J. Mehnen, “Statistical Methods for Improving Multi-objective Evolutionary Optimisation,” <i>International Journal of Computational Intelligence Research</i>, vol. 5, no. 2, pp. 72–78, 2009.","chicago":"Trautmann, Heike, and J Mehnen. “Statistical Methods for Improving Multi-Objective Evolutionary Optimisation.” <i>International Journal of Computational Intelligence Research</i> 5, no. 2 (2009): 72–78.","ama":"Trautmann H, Mehnen J. Statistical Methods for Improving Multi-objective Evolutionary Optimisation. <i>International Journal of Computational Intelligence Research</i>. 2009;5(2):72–78.","apa":"Trautmann, H., &#38; Mehnen, J. (2009). Statistical Methods for Improving Multi-objective Evolutionary Optimisation. <i>International Journal of Computational Intelligence Research</i>, <i>5</i>(2), 72–78.","short":"H. Trautmann, J. Mehnen, International Journal of Computational Intelligence Research 5 (2009) 72–78.","mla":"Trautmann, Heike, and J. Mehnen. “Statistical Methods for Improving Multi-Objective Evolutionary Optimisation.” <i>International Journal of Computational Intelligence Research</i>, vol. 5, no. 2, 2009, pp. 72–78.","bibtex":"@article{Trautmann_Mehnen_2009, title={Statistical Methods for Improving Multi-objective Evolutionary Optimisation}, volume={5}, number={2}, journal={International Journal of Computational Intelligence Research}, author={Trautmann, Heike and Mehnen, J}, year={2009}, pages={72–78} }"},"date_updated":"2023-10-16T13:58:54Z","volume":5,"author":[{"first_name":"Heike","id":"100740","full_name":"Trautmann, Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann"},{"last_name":"Mehnen","full_name":"Mehnen, J","first_name":"J"}],"date_created":"2023-08-04T16:17:19Z","title":"Statistical Methods for Improving Multi-objective Evolutionary Optimisation"}]