[{"publication":"Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC)","type":"conference","abstract":[{"text":"Here we address the problem of computing finite size Hausdorff approximations of the Pareto front of four-objective optimization problems by means of evolutionary computing. Since many applications desire an approximation evenly spread along the Pareto front and approximations that are good in the Hausdorff sense are typically evenly spread along the Pareto front we consider three different evolutionary multi-objective algorithms tailored to that purpose, where two of them are based on the Part and Selection Algorithm (PSA). Finally, we present some numerical results indicating the strength of the novel methods.","lang":"eng"}],"status":"public","_id":"46387","department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","language":[{"iso":"eng"}],"year":"2013","place":"Cancun, Mexico","page":"3190–3197","citation":{"apa":"Dominguez-Medina, C., Rudolph, G., Schütze, O., & Trautmann, H. (2013). Evenly spaced Pareto fronts of quad-objective problems using PSA partitioning technique. Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC), 3190–3197. https://doi.org/10.1109/CEC.2013.6557960","short":"C. Dominguez-Medina, G. Rudolph, O. Schütze, H. Trautmann, in: Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC), Cancun, Mexico, 2013, pp. 3190–3197.","mla":"Dominguez-Medina, C., et al. “Evenly Spaced Pareto Fronts of Quad-Objective Problems Using PSA Partitioning Technique.” Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC), 2013, pp. 3190–3197, doi:https://doi.org/10.1109/CEC.2013.6557960.","bibtex":"@inproceedings{Dominguez-Medina_Rudolph_Schütze_Trautmann_2013, place={Cancun, Mexico}, title={Evenly spaced Pareto fronts of quad-objective problems using PSA partitioning technique}, DOI={https://doi.org/10.1109/CEC.2013.6557960}, booktitle={Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC)}, author={Dominguez-Medina, C and Rudolph, G and Schütze, O and Trautmann, Heike}, year={2013}, pages={3190–3197} }","ama":"Dominguez-Medina C, Rudolph G, Schütze O, Trautmann H. Evenly spaced Pareto fronts of quad-objective problems using PSA partitioning technique. In: Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC). ; 2013:3190–3197. doi:https://doi.org/10.1109/CEC.2013.6557960","chicago":"Dominguez-Medina, C, G Rudolph, O Schütze, and Heike Trautmann. “Evenly Spaced Pareto Fronts of Quad-Objective Problems Using PSA Partitioning Technique.” In Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC), 3190–3197. Cancun, Mexico, 2013. https://doi.org/10.1109/CEC.2013.6557960.","ieee":"C. Dominguez-Medina, G. Rudolph, O. Schütze, and H. Trautmann, “Evenly spaced Pareto fronts of quad-objective problems using PSA partitioning technique,” in Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC), 2013, pp. 3190–3197, doi: https://doi.org/10.1109/CEC.2013.6557960."},"date_updated":"2023-10-16T13:45:34Z","author":[{"first_name":"C","full_name":"Dominguez-Medina, C","last_name":"Dominguez-Medina"},{"first_name":"G","full_name":"Rudolph, G","last_name":"Rudolph"},{"first_name":"O","last_name":"Schütze","full_name":"Schütze, O"},{"first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike","id":"100740"}],"date_created":"2023-08-04T15:40:15Z","title":"Evenly spaced Pareto fronts of quad-objective problems using PSA partitioning technique","doi":"https://doi.org/10.1109/CEC.2013.6557960"},{"doi":"10.1109/CIG.2013.6633627","title":"Reactive strategy choice in StarCraft by means of Fuzzy Control","date_created":"2023-08-04T15:42:58Z","author":[{"first_name":"Mike","full_name":"Preuss, Mike","last_name":"Preuss"},{"last_name":"Kozakowski","full_name":"Kozakowski, Daniel","first_name":"Daniel"},{"first_name":"Johan","last_name":"Hagelbäck","full_name":"Hagelbäck, Johan"},{"first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike","id":"100740"}],"date_updated":"2023-10-16T13:46:13Z","page":"1-8","citation":{"apa":"Preuss, M., Kozakowski, D., Hagelbäck, J., & Trautmann, H. (2013). Reactive strategy choice in StarCraft by means of Fuzzy Control. 2013 IEEE Conference on Computational Inteligence in Games (CIG), 1–8. https://doi.org/10.1109/CIG.2013.6633627","bibtex":"@inproceedings{Preuss_Kozakowski_Hagelbäck_Trautmann_2013, title={Reactive strategy choice in StarCraft by means of Fuzzy Control}, DOI={10.1109/CIG.2013.6633627}, booktitle={2013 IEEE Conference on Computational Inteligence in Games (CIG)}, author={Preuss, Mike and Kozakowski, Daniel and Hagelbäck, Johan and Trautmann, Heike}, year={2013}, pages={1–8} }","mla":"Preuss, Mike, et al. “Reactive Strategy Choice in StarCraft by Means of Fuzzy Control.” 2013 IEEE Conference on Computational Inteligence in Games (CIG), 2013, pp. 1–8, doi:10.1109/CIG.2013.6633627.","short":"M. Preuss, D. Kozakowski, J. Hagelbäck, H. Trautmann, in: 2013 IEEE Conference on Computational Inteligence in Games (CIG), 2013, pp. 1–8.","chicago":"Preuss, Mike, Daniel Kozakowski, Johan Hagelbäck, and Heike Trautmann. “Reactive Strategy Choice in StarCraft by Means of Fuzzy Control.” In 2013 IEEE Conference on Computational Inteligence in Games (CIG), 1–8, 2013. https://doi.org/10.1109/CIG.2013.6633627.","ieee":"M. Preuss, D. Kozakowski, J. Hagelbäck, and H. Trautmann, “Reactive strategy choice in StarCraft by means of Fuzzy Control,” in 2013 IEEE Conference on Computational Inteligence in Games (CIG), 2013, pp. 1–8, doi: 10.1109/CIG.2013.6633627.","ama":"Preuss M, Kozakowski D, Hagelbäck J, Trautmann H. Reactive strategy choice in StarCraft by means of Fuzzy Control. In: 2013 IEEE Conference on Computational Inteligence in Games (CIG). ; 2013:1-8. doi:10.1109/CIG.2013.6633627"},"year":"2013","language":[{"iso":"eng"}],"department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","_id":"46389","status":"public","abstract":[{"text":"Current StarCraft bots are not very flexible in their strategy choice, most of them just follow a manually optimized one, usually a rush. We suggest a method of augmenting existing bots via Fuzzy Control in order to make them react on the current game situation. According to the available information, the best matching of a pool of strategies is chosen. While the method is very general and can be applied easily to many bots, we implement it for the existing BTHAI bot and show experimentally how the modifications affects its gameplay, and how it is improved compared to the original version.","lang":"eng"}],"publication":"2013 IEEE Conference on Computational Inteligence in Games (CIG)","type":"conference"},{"citation":{"ama":"Trautmann H, Wagner T, Biermann D, Weihs C. Indicator-based Selection in Evolutionary Multiobjective Optimization Algorithms Based On the Desirability Index. Journal of Multi-Criteria Decision Analysis. 2013;20(5-6):319–337. doi:https://doi.org/10.1002/mcda.1503","ieee":"H. Trautmann, T. Wagner, D. Biermann, and C. Weihs, “Indicator-based Selection in Evolutionary Multiobjective Optimization Algorithms Based On the Desirability Index,” Journal of Multi-Criteria Decision Analysis, vol. 20, no. 5–6, pp. 319–337, 2013, doi: https://doi.org/10.1002/mcda.1503.","chicago":"Trautmann, Heike, T Wagner, D Biermann, and C Weihs. “Indicator-Based Selection in Evolutionary Multiobjective Optimization Algorithms Based On the Desirability Index.” Journal of Multi-Criteria Decision Analysis 20, no. 5–6 (2013): 319–337. https://doi.org/10.1002/mcda.1503.","mla":"Trautmann, Heike, et al. “Indicator-Based Selection in Evolutionary Multiobjective Optimization Algorithms Based On the Desirability Index.” Journal of Multi-Criteria Decision Analysis, vol. 20, no. 5–6, 2013, pp. 319–337, doi:https://doi.org/10.1002/mcda.1503.","short":"H. Trautmann, T. Wagner, D. Biermann, C. Weihs, Journal of Multi-Criteria Decision Analysis 20 (2013) 319–337.","bibtex":"@article{Trautmann_Wagner_Biermann_Weihs_2013, title={Indicator-based Selection in Evolutionary Multiobjective Optimization Algorithms Based On the Desirability Index}, volume={20}, DOI={https://doi.org/10.1002/mcda.1503}, number={5–6}, journal={Journal of Multi-Criteria Decision Analysis}, author={Trautmann, Heike and Wagner, T and Biermann, D and Weihs, C}, year={2013}, pages={319–337} }","apa":"Trautmann, H., Wagner, T., Biermann, D., & Weihs, C. (2013). Indicator-based Selection in Evolutionary Multiobjective Optimization Algorithms Based On the Desirability Index. Journal of Multi-Criteria Decision Analysis, 20(5–6), 319–337. https://doi.org/10.1002/mcda.1503"},"page":"319–337","intvolume":" 20","year":"2013","issue":"5-6","doi":"https://doi.org/10.1002/mcda.1503","title":"Indicator-based Selection in Evolutionary Multiobjective Optimization Algorithms Based On the Desirability Index","author":[{"first_name":"Heike","id":"100740","full_name":"Trautmann, Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann"},{"full_name":"Wagner, T","last_name":"Wagner","first_name":"T"},{"first_name":"D","full_name":"Biermann, D","last_name":"Biermann"},{"first_name":"C","full_name":"Weihs, C","last_name":"Weihs"}],"date_created":"2023-08-04T15:50:03Z","volume":20,"date_updated":"2023-10-16T13:48:31Z","status":"public","abstract":[{"lang":"eng","text":"In multiobjective optimization, the identification of practically relevant solutions on the Pareto-optimal front is an important research topic. Desirability functions (DFs) allow the preferences of the decision maker to be specified in an intuitive way. Recently, it has been shown for continuous optimization problems that an a priori transformation of the objectives by means of DFs can be used to focus the search of a hypervolume-based evolutionary algorithm on the desired part of the front. In many-objective optimization, however, the computational complexity of the hypervolume can become a crucial part. Thus, an alternative to this approach will be presented in this paper. The new algorithm operates in the untransformed objective space, but the desirability index (DI), that is, a DF-based scalarization, will be used as the second-level selection criterion in the non-dominated sorting. The diversity and uniform distribution of the resulting approximation are ensured by the use of an external archive. In the experiments, different preferences are specified as DFs, and their effects are investigated. It is shown that trade-off solutions are generated in the desired regions of the Pareto-optimal front and with a density adaptive to the DI. The efficiency of the approach with respect to increasing objective space dimension is also analysed using scalable test functions. The convergence speed is superior to other set-based and preference-based evolutionary multiobjective algorithms while the approach is of low computational complexity due to cheap DI evaluations. Copyright © 2013 John Wiley & Sons, Ltd."}],"type":"journal_article","publication":"Journal of Multi-Criteria Decision Analysis","language":[{"iso":"eng"}],"user_id":"15504","department":[{"_id":"34"},{"_id":"819"}],"_id":"46395"},{"status":"public","editor":[{"full_name":"Purshouse, Robin C.","last_name":"Purshouse","first_name":"Robin C."},{"full_name":"Fleming, Peter J.","last_name":"Fleming","first_name":"Peter J."},{"full_name":"Fonseca, Carlos M.","last_name":"Fonseca","first_name":"Carlos M."},{"last_name":"Greco","full_name":"Greco, Salvatore","first_name":"Salvatore"},{"first_name":"Jane","last_name":"Shaw","full_name":"Shaw, Jane"}],"abstract":[{"text":"In multi-objective optimization, set-based performance indicators have become the state of the art for assessing the quality of Pareto front approximations. As a consequence, they are also more and more used within the design of multi-objective optimization algorithms. The R2 and the Hypervolume (HV) indicator represent two popular examples. In order to understand the behavior and the approximations preferred by these indicators and algorithms, a comprehensive knowledge of the indicator’s properties is required. Whereas this knowledge is available for the HV, we presented a first approach in this direction for the R2 indicator just recently. In this paper, we build upon this knowledge and enhance the considerations with respect to the integration of preferences into the R2 indicator. More specifically, we analyze the effect of the reference point, the domain of the weights, and the distribution of weight vectors on the optimization of $\\mu$ solutions with respect to the R2 indicator. By means of theoretical findings and empirical evidence, we show the potentials of these three possibilities using the optimal distribution of $\\mu$ solutions for exemplary setups.","lang":"eng"}],"publication":"Evolutionary Multi-Criterion Optimization","type":"conference","language":[{"iso":"eng"}],"department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","_id":"46393","page":"81–95","citation":{"ama":"Wagner T, Trautmann H, Brockhoff D. Preference Articulation by Means of the R2 Indicator. In: Purshouse RC, Fleming PJ, Fonseca CM, Greco S, Shaw J, eds. Evolutionary Multi-Criterion Optimization. Springer Berlin Heidelberg; 2013:81–95.","chicago":"Wagner, Tobias, Heike Trautmann, and Dimo Brockhoff. “Preference Articulation by Means of the R2 Indicator.” In Evolutionary Multi-Criterion Optimization, edited by Robin C. Purshouse, Peter J. Fleming, Carlos M. Fonseca, Salvatore Greco, and Jane Shaw, 81–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.","ieee":"T. Wagner, H. Trautmann, and D. Brockhoff, “Preference Articulation by Means of the R2 Indicator,” in Evolutionary Multi-Criterion Optimization, 2013, pp. 81–95.","apa":"Wagner, T., Trautmann, H., & Brockhoff, D. (2013). Preference Articulation by Means of the R2 Indicator. In R. C. Purshouse, P. J. Fleming, C. M. Fonseca, S. Greco, & J. Shaw (Eds.), Evolutionary Multi-Criterion Optimization (pp. 81–95). Springer Berlin Heidelberg.","mla":"Wagner, Tobias, et al. “Preference Articulation by Means of the R2 Indicator.” Evolutionary Multi-Criterion Optimization, edited by Robin C. Purshouse et al., Springer Berlin Heidelberg, 2013, pp. 81–95.","bibtex":"@inproceedings{Wagner_Trautmann_Brockhoff_2013, place={Berlin, Heidelberg}, title={Preference Articulation by Means of the R2 Indicator}, booktitle={Evolutionary Multi-Criterion Optimization}, publisher={Springer Berlin Heidelberg}, author={Wagner, Tobias and Trautmann, Heike and Brockhoff, Dimo}, editor={Purshouse, Robin C. and Fleming, Peter J. and Fonseca, Carlos M. and Greco, Salvatore and Shaw, Jane}, year={2013}, pages={81–95} }","short":"T. Wagner, H. Trautmann, D. Brockhoff, in: R.C. Purshouse, P.J. Fleming, C.M. Fonseca, S. Greco, J. Shaw (Eds.), Evolutionary Multi-Criterion Optimization, Springer Berlin Heidelberg, Berlin, Heidelberg, 2013, pp. 81–95."},"place":"Berlin, Heidelberg","year":"2013","publication_identifier":{"isbn":["978-3-642-37140-0"]},"title":"Preference Articulation by Means of the R2 Indicator","date_created":"2023-08-04T15:47:49Z","author":[{"first_name":"Tobias","last_name":"Wagner","full_name":"Wagner, Tobias"},{"orcid":"0000-0002-9788-8282","last_name":"Trautmann","full_name":"Trautmann, Heike","id":"100740","first_name":"Heike"},{"first_name":"Dimo","last_name":"Brockhoff","full_name":"Brockhoff, Dimo"}],"date_updated":"2023-10-16T13:47:58Z","publisher":"Springer Berlin Heidelberg"},{"page":"70–74","citation":{"ama":"Trautmann H, Wagner T, Brockhoff D. R2-EMOA: Focused Multiobjective Search Using R2-Indicator-Based Selection. In: Nicosia G, Pardalos P, eds. Learning and Intelligent Optimization. Springer Berlin Heidelberg; 2013:70–74.","chicago":"Trautmann, Heike, Tobias Wagner, and Dimo Brockhoff. “R2-EMOA: Focused Multiobjective Search Using R2-Indicator-Based Selection.” In Learning and Intelligent Optimization, edited by Giuseppe Nicosia and Panos Pardalos, 70–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.","ieee":"H. Trautmann, T. Wagner, and D. Brockhoff, “R2-EMOA: Focused Multiobjective Search Using R2-Indicator-Based Selection,” in Learning and Intelligent Optimization, 2013, pp. 70–74.","apa":"Trautmann, H., Wagner, T., & Brockhoff, D. (2013). R2-EMOA: Focused Multiobjective Search Using R2-Indicator-Based Selection. In G. Nicosia & P. Pardalos (Eds.), Learning and Intelligent Optimization (pp. 70–74). Springer Berlin Heidelberg.","bibtex":"@inproceedings{Trautmann_Wagner_Brockhoff_2013, place={Berlin, Heidelberg}, title={R2-EMOA: Focused Multiobjective Search Using R2-Indicator-Based Selection}, booktitle={Learning and Intelligent Optimization}, publisher={Springer Berlin Heidelberg}, author={Trautmann, Heike and Wagner, Tobias and Brockhoff, Dimo}, editor={Nicosia, Giuseppe and Pardalos, Panos}, year={2013}, pages={70–74} }","mla":"Trautmann, Heike, et al. “R2-EMOA: Focused Multiobjective Search Using R2-Indicator-Based Selection.” Learning and Intelligent Optimization, edited by Giuseppe Nicosia and Panos Pardalos, Springer Berlin Heidelberg, 2013, pp. 70–74.","short":"H. Trautmann, T. Wagner, D. Brockhoff, in: G. Nicosia, P. Pardalos (Eds.), Learning and Intelligent Optimization, Springer Berlin Heidelberg, Berlin, Heidelberg, 2013, pp. 70–74."},"place":"Berlin, Heidelberg","year":"2013","publication_identifier":{"isbn":["978-3-642-44973-4"]},"title":"R2-EMOA: Focused Multiobjective Search Using R2-Indicator-Based Selection","author":[{"last_name":"Trautmann","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike","id":"100740","first_name":"Heike"},{"last_name":"Wagner","full_name":"Wagner, Tobias","first_name":"Tobias"},{"last_name":"Brockhoff","full_name":"Brockhoff, Dimo","first_name":"Dimo"}],"date_created":"2023-08-04T15:47:00Z","date_updated":"2023-10-16T13:47:41Z","publisher":"Springer Berlin Heidelberg","status":"public","abstract":[{"text":"An indicator-based evolutionary multiobjective optimization algorithm (EMOA) is introduced which incorporates the contribution to the unary R2-indicator as the secondary selection criterion. First experiments indicate that the R2-EMOA accurately approximates the Pareto front of the considered continuous multiobjective optimization problems. Furthermore, decision makers’ preferences can be included by adjusting the weight vector distributions of the indicator which results in a focused search behavior.","lang":"eng"}],"editor":[{"first_name":"Giuseppe","full_name":"Nicosia, Giuseppe","last_name":"Nicosia"},{"full_name":"Pardalos, Panos","last_name":"Pardalos","first_name":"Panos"}],"publication":"Learning and Intelligent Optimization","type":"conference","language":[{"iso":"eng"}],"department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","_id":"46392"},{"title":"A Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling Salesperson Problem","doi":"10.1007/s10472-013-9341-2","date_updated":"2023-12-13T10:50:41Z","date_created":"2023-11-14T15:58:59Z","author":[{"first_name":"Olaf","last_name":"Mersmann","full_name":"Mersmann, Olaf"},{"first_name":"Bernd","full_name":"Bischl, Bernd","last_name":"Bischl"},{"full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike"},{"first_name":"Markus","full_name":"Wagner, Markus","last_name":"Wagner"},{"first_name":"Jakob","id":"102979","full_name":"Bossek, Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek"},{"first_name":"Frank","last_name":"Neumann","full_name":"Neumann, Frank"}],"volume":69,"year":"2013","citation":{"apa":"Mersmann, O., Bischl, B., Trautmann, H., Wagner, M., Bossek, J., & Neumann, F. (2013). A Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling Salesperson Problem. Annals of Mathematics and Artificial Intelligence, 69(2), 151–182. https://doi.org/10.1007/s10472-013-9341-2","mla":"Mersmann, Olaf, et al. “A Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling Salesperson Problem.” Annals of Mathematics and Artificial Intelligence, vol. 69, no. 2, 2013, pp. 151–182, doi:10.1007/s10472-013-9341-2.","bibtex":"@article{Mersmann_Bischl_Trautmann_Wagner_Bossek_Neumann_2013, title={A Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling Salesperson Problem}, volume={69}, DOI={10.1007/s10472-013-9341-2}, number={2}, journal={Annals of Mathematics and Artificial Intelligence}, author={Mersmann, Olaf and Bischl, Bernd and Trautmann, Heike and Wagner, Markus and Bossek, Jakob and Neumann, Frank}, year={2013}, pages={151–182} }","short":"O. Mersmann, B. Bischl, H. Trautmann, M. Wagner, J. Bossek, F. Neumann, Annals of Mathematics and Artificial Intelligence 69 (2013) 151–182.","ieee":"O. Mersmann, B. Bischl, H. Trautmann, M. Wagner, J. Bossek, and F. Neumann, “A Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling Salesperson Problem,” Annals of Mathematics and Artificial Intelligence, vol. 69, no. 2, pp. 151–182, 2013, doi: 10.1007/s10472-013-9341-2.","chicago":"Mersmann, Olaf, Bernd Bischl, Heike Trautmann, Markus Wagner, Jakob Bossek, and Frank Neumann. “A Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling Salesperson Problem.” Annals of Mathematics and Artificial Intelligence 69, no. 2 (2013): 151–182. https://doi.org/10.1007/s10472-013-9341-2.","ama":"Mersmann O, Bischl B, Trautmann H, Wagner M, Bossek J, Neumann F. A Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling Salesperson Problem. Annals of Mathematics and Artificial Intelligence. 2013;69(2):151–182. doi:10.1007/s10472-013-9341-2"},"intvolume":" 69","page":"151–182","publication_identifier":{"issn":["1012-2443"]},"issue":"2","keyword":["2-opt","90B06","Classification","Feature selection","MARS","TSP"],"language":[{"iso":"eng"}],"_id":"48889","user_id":"102979","department":[{"_id":"819"}],"abstract":[{"lang":"eng","text":"Meta-heuristics are frequently used to tackle NP-hard combinatorial optimization problems. With this paper we contribute to the understanding of the success of 2-opt based local search algorithms for solving the traveling salesperson 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."}],"status":"public","type":"journal_article","publication":"Annals of Mathematics and Artificial Intelligence"},{"language":[{"iso":"eng"}],"_id":"46394","department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","abstract":[{"text":"Meta-heuristics are frequently used to tackle NP-hard combinatorial optimization problems. With this paper we contribute to the understanding of the success of 2-opt based local search algorithms for solving the traveling salesperson 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"}],"status":"public","publication":"Annals of Mathematics and Artificial Intelligence","type":"journal_article","title":"A Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling Salesman Problem","date_updated":"2024-06-10T11:57:43Z","volume":69,"date_created":"2023-08-04T15:48:57Z","author":[{"full_name":"Mersmann, O","last_name":"Mersmann","first_name":"O"},{"first_name":"B","last_name":"Bischl","full_name":"Bischl, B"},{"last_name":"Trautmann","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike","id":"100740","first_name":"Heike"},{"last_name":"Wagner","full_name":"Wagner, M","first_name":"M"},{"orcid":"0000-0002-4121-4668","last_name":"Bossek","id":"102979","full_name":"Bossek, Jakob","first_name":"Jakob"},{"last_name":"Neumann","full_name":"Neumann, F","first_name":"F"}],"year":"2013","page":"151–182","intvolume":" 69","citation":{"ieee":"O. Mersmann, B. Bischl, H. Trautmann, M. Wagner, J. Bossek, and F. Neumann, “A Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling Salesman Problem,” Annals of Mathematics and Artificial Intelligence, vol. 69, pp. 151–182, 2013.","chicago":"Mersmann, O, B Bischl, Heike Trautmann, M Wagner, Jakob Bossek, and F Neumann. “A Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling Salesman Problem.” Annals of Mathematics and Artificial Intelligence 69 (2013): 151–182.","short":"O. Mersmann, B. Bischl, H. Trautmann, M. Wagner, J. Bossek, F. Neumann, Annals of Mathematics and Artificial Intelligence 69 (2013) 151–182.","bibtex":"@article{Mersmann_Bischl_Trautmann_Wagner_Bossek_Neumann_2013, title={A Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling Salesman Problem}, volume={69}, journal={Annals of Mathematics and Artificial Intelligence}, author={Mersmann, O and Bischl, B and Trautmann, Heike and Wagner, M and Bossek, Jakob and Neumann, F}, year={2013}, pages={151–182} }","mla":"Mersmann, O., et al. “A Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling Salesman Problem.” Annals of Mathematics and Artificial Intelligence, vol. 69, 2013, pp. 151–182.","ama":"Mersmann O, Bischl B, Trautmann H, Wagner M, Bossek J, Neumann F. A Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling Salesman Problem. Annals of Mathematics and Artificial Intelligence. 2013;69:151–182.","apa":"Mersmann, O., Bischl, B., Trautmann, H., Wagner, M., Bossek, J., & Neumann, F. (2013). A Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling Salesman Problem. Annals of Mathematics and Artificial Intelligence, 69, 151–182."}},{"publication_identifier":{"isbn":["9781450311779"]},"year":"2012","place":"New York, NY, USA","page":"465–472","citation":{"ieee":"D. Brockhoff, T. Wagner, and H. Trautmann, “On the Properties of the R2 Indicator,” in Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, 2012, pp. 465–472, doi: 10.1145/2330163.2330230.","chicago":"Brockhoff, Dimo, Tobias Wagner, and Heike Trautmann. “On the Properties of the R2 Indicator.” In Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, 465–472. GECCO ’12. New York, NY, USA: Association for Computing Machinery, 2012. https://doi.org/10.1145/2330163.2330230.","ama":"Brockhoff D, Wagner T, Trautmann H. On the Properties of the R2 Indicator. In: Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation. GECCO ’12. Association for Computing Machinery; 2012:465–472. doi:10.1145/2330163.2330230","apa":"Brockhoff, D., Wagner, T., & Trautmann, H. (2012). On the Properties of the R2 Indicator. Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, 465–472. https://doi.org/10.1145/2330163.2330230","mla":"Brockhoff, Dimo, et al. “On the Properties of the R2 Indicator.” Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, Association for Computing Machinery, 2012, pp. 465–472, doi:10.1145/2330163.2330230.","bibtex":"@inproceedings{Brockhoff_Wagner_Trautmann_2012, place={New York, NY, USA}, series={GECCO ’12}, title={On the Properties of the R2 Indicator}, DOI={10.1145/2330163.2330230}, booktitle={Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation}, publisher={Association for Computing Machinery}, author={Brockhoff, Dimo and Wagner, Tobias and Trautmann, Heike}, year={2012}, pages={465–472}, collection={GECCO ’12} }","short":"D. Brockhoff, T. Wagner, H. Trautmann, in: Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, Association for Computing Machinery, New York, NY, USA, 2012, pp. 465–472."},"date_updated":"2023-10-16T13:47:23Z","publisher":"Association for Computing Machinery","date_created":"2023-08-04T15:52:42Z","author":[{"first_name":"Dimo","last_name":"Brockhoff","full_name":"Brockhoff, Dimo"},{"first_name":"Tobias","last_name":"Wagner","full_name":"Wagner, Tobias"},{"first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","id":"100740","full_name":"Trautmann, Heike"}],"title":"On the Properties of the R2 Indicator","doi":"10.1145/2330163.2330230","publication":"Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation","type":"conference","abstract":[{"lang":"eng","text":"In multiobjective optimization, set-based performance indicators are commonly used to assess the quality of a Pareto front approximation. Based on the scalarization obtained by these indicators, a performance comparison of multiobjective optimization algorithms becomes possible. The R2 and the Hypervolume (HV) indicator represent two recommended approaches which have shown a correlated behavior in recent empirical studies. Whereas the HV indicator has been comprehensively analyzed in the last years, almost no studies on the R2 indicator exist. In this paper, we thus perform a comprehensive investigation of the properties of the R2 indicator in a theoretical and empirical way. The influence of the number and distribution of the weight vectors on the optimal distribution of μ solutions is analyzed. Based on a comparative analysis, specific characteristics and differences of the R2 and HV indicator are presented."}],"status":"public","_id":"46397","department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","series_title":"GECCO ’12","keyword":["hypervolume indicator","multiobjective optimization","performance assessment","r2 indicator"],"language":[{"iso":"eng"}]},{"publication_identifier":{"isbn":["9781450311779"]},"page":"313–320","citation":{"ama":"Bischl B, Mersmann O, Trautmann H, Preuß M. Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning. In: Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation. GECCO ’12. Association for Computing Machinery; 2012:313–320. doi:10.1145/2330163.2330209","ieee":"B. Bischl, O. Mersmann, H. Trautmann, and M. Preuß, “Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning,” in Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, 2012, pp. 313–320, doi: 10.1145/2330163.2330209.","chicago":"Bischl, Bernd, Olaf Mersmann, Heike Trautmann, and Mike Preuß. “Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning.” In Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, 313–320. GECCO ’12. New York, NY, USA: Association for Computing Machinery, 2012. https://doi.org/10.1145/2330163.2330209.","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={10.1145/2330163.2330209}, 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.” Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, Association for Computing Machinery, 2012, pp. 313–320, doi:10.1145/2330163.2330209.","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.","apa":"Bischl, B., Mersmann, O., Trautmann, H., & Preuß, M. (2012). Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning. Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, 313–320. https://doi.org/10.1145/2330163.2330209"},"place":"New York, NY, USA","year":"2012","date_created":"2023-08-04T15:51:56Z","author":[{"last_name":"Bischl","full_name":"Bischl, Bernd","first_name":"Bernd"},{"last_name":"Mersmann","full_name":"Mersmann, Olaf","first_name":"Olaf"},{"first_name":"Heike","full_name":"Trautmann, Heike","id":"100740","last_name":"Trautmann","orcid":"0000-0002-9788-8282"},{"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","publication":"Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation","type":"conference","status":"public","abstract":[{"lang":"eng","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."}],"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"]},{"user_id":"15504","department":[{"_id":"34"},{"_id":"819"}],"_id":"46399","language":[{"iso":"eng"}],"type":"journal_article","publication":"Evolutionary Computation Journal","status":"public","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"}],"date_created":"2023-08-04T15:54:41Z","author":[{"last_name":"Bischl","full_name":"Bischl, B","first_name":"B"},{"full_name":"Mersmann, O","last_name":"Mersmann","first_name":"O"},{"first_name":"Heike","full_name":"Trautmann, Heike","id":"100740","last_name":"Trautmann","orcid":"0000-0002-9788-8282"},{"first_name":"C","last_name":"Weihs","full_name":"Weihs, C"}],"volume":20,"date_updated":"2023-10-16T13:53:58Z","doi":"10.1162/EVCO_a_00069","title":"Resampling Methods in Model Validation","issue":"2","citation":{"ama":"Bischl B, Mersmann O, Trautmann H, Weihs C. Resampling Methods in Model Validation. Evolutionary Computation Journal. 2012;20(2):249–275. doi:10.1162/EVCO_a_00069","ieee":"B. Bischl, O. Mersmann, H. Trautmann, and C. Weihs, “Resampling Methods in Model Validation,” Evolutionary Computation Journal, vol. 20, no. 2, pp. 249–275, 2012, doi: 10.1162/EVCO_a_00069.","chicago":"Bischl, B, O Mersmann, Heike Trautmann, and C Weihs. “Resampling Methods in Model Validation.” Evolutionary Computation Journal 20, no. 2 (2012): 249–275. https://doi.org/10.1162/EVCO_a_00069.","short":"B. Bischl, O. Mersmann, H. Trautmann, C. Weihs, Evolutionary Computation Journal 20 (2012) 249–275.","mla":"Bischl, B., et al. “Resampling Methods in Model Validation.” Evolutionary Computation Journal, vol. 20, no. 2, 2012, pp. 249–275, doi:10.1162/EVCO_a_00069.","bibtex":"@article{Bischl_Mersmann_Trautmann_Weihs_2012, title={Resampling Methods in Model Validation}, volume={20}, DOI={10.1162/EVCO_a_00069}, 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., & Weihs, C. (2012). Resampling Methods in Model Validation. Evolutionary Computation Journal, 20(2), 249–275. https://doi.org/10.1162/EVCO_a_00069"},"intvolume":" 20","page":"249–275","year":"2012"},{"_id":"46400","user_id":"15504","department":[{"_id":"34"},{"_id":"819"}],"language":[{"iso":"eng"}],"type":"journal_article","publication":"at-Automatisierungstechnik","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"}],"status":"public","date_updated":"2023-10-16T13:54:17Z","author":[{"first_name":"G","full_name":"Rudolph, G","last_name":"Rudolph"},{"first_name":"Heike","id":"100740","full_name":"Trautmann, Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282"},{"first_name":"O","full_name":"Schütze, O","last_name":"Schütze"}],"date_created":"2023-08-04T15:55:34Z","volume":60,"title":"Homogene Approximation der Paretofront bei mehrkriteriellen Kontrollproblemen","doi":"10.1524/auto.2012.1033","year":"2012","citation":{"apa":"Rudolph, G., Trautmann, H., & Schütze, O. (2012). Homogene Approximation der Paretofront bei mehrkriteriellen Kontrollproblemen. At-Automatisierungstechnik, 60, 610–621. https://doi.org/10.1524/auto.2012.1033","bibtex":"@article{Rudolph_Trautmann_Schütze_2012, title={Homogene Approximation der Paretofront bei mehrkriteriellen Kontrollproblemen}, volume={60}, DOI={10.1524/auto.2012.1033}, journal={at-Automatisierungstechnik}, author={Rudolph, G and Trautmann, Heike and Schütze, O}, year={2012}, pages={610–621} }","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.” At-Automatisierungstechnik, vol. 60, 2012, pp. 610–621, doi:10.1524/auto.2012.1033.","ama":"Rudolph G, Trautmann H, Schütze O. Homogene Approximation der Paretofront bei mehrkriteriellen Kontrollproblemen. at-Automatisierungstechnik. 2012;60:610–621. doi:10.1524/auto.2012.1033","chicago":"Rudolph, G, Heike Trautmann, and O Schütze. “Homogene Approximation Der Paretofront Bei Mehrkriteriellen Kontrollproblemen.” At-Automatisierungstechnik 60 (2012): 610–621. https://doi.org/10.1524/auto.2012.1033.","ieee":"G. Rudolph, H. Trautmann, and O. Schütze, “Homogene Approximation der Paretofront bei mehrkriteriellen Kontrollproblemen,” at-Automatisierungstechnik, vol. 60, pp. 610–621, 2012, doi: 10.1524/auto.2012.1033."},"page":"610–621","intvolume":" 60"},{"title":"Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness","date_updated":"2023-12-13T10:48:58Z","publisher":"Springer-Verlag","author":[{"full_name":"Mersmann, Olaf","last_name":"Mersmann","first_name":"Olaf"},{"full_name":"Bischl, Bernd","last_name":"Bischl","first_name":"Bernd"},{"first_name":"Jakob","full_name":"Bossek, Jakob","id":"102979","orcid":"0000-0002-4121-4668","last_name":"Bossek"},{"first_name":"Heike","full_name":"Trautmann, Heike","last_name":"Trautmann"},{"first_name":"Markus","full_name":"Wagner, Markus","last_name":"Wagner"},{"last_name":"Neumann","full_name":"Neumann, Frank","first_name":"Frank"}],"date_created":"2023-11-14T15:58:59Z","year":"2012","place":"Berlin, Heidelberg","citation":{"apa":"Mersmann, O., Bischl, B., Bossek, J., Trautmann, H., Wagner, M., & Neumann, F. (2012). Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness. Revised Selected Papers of the 6th International Conference on Learning and Intelligent Optimization - Volume 7219, 115–129.","bibtex":"@inproceedings{Mersmann_Bischl_Bossek_Trautmann_Wagner_Neumann_2012, place={Berlin, Heidelberg}, series={LION 6}, title={Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness}, booktitle={Revised Selected Papers of the 6th International Conference on Learning and Intelligent Optimization - Volume 7219}, publisher={Springer-Verlag}, author={Mersmann, Olaf and Bischl, Bernd and Bossek, Jakob and Trautmann, Heike and Wagner, Markus and Neumann, Frank}, year={2012}, pages={115–129}, collection={LION 6} }","short":"O. Mersmann, B. Bischl, J. Bossek, H. Trautmann, M. Wagner, F. Neumann, in: Revised Selected Papers of the 6th International Conference on Learning and Intelligent Optimization - Volume 7219, Springer-Verlag, Berlin, Heidelberg, 2012, pp. 115–129.","mla":"Mersmann, Olaf, et al. “Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness.” Revised Selected Papers of the 6th International Conference on Learning and Intelligent Optimization - Volume 7219, Springer-Verlag, 2012, pp. 115–129.","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 Revised Selected Papers of the 6th International Conference on Learning and Intelligent Optimization - Volume 7219, 115–129. LION 6. Berlin, Heidelberg: Springer-Verlag, 2012.","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 Revised Selected Papers of the 6th International Conference on Learning and Intelligent Optimization - Volume 7219, 2012, pp. 115–129.","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: Revised Selected Papers of the 6th International Conference on Learning and Intelligent Optimization - Volume 7219. LION 6. Springer-Verlag; 2012:115–129."},"page":"115–129","publication_identifier":{"isbn":["978-3-642-34412-1"]},"keyword":["2-opt","Classification","Feature Selection","MARS","TSP"],"language":[{"iso":"eng"}],"extern":"1","_id":"48890","user_id":"102979","series_title":"LION 6","department":[{"_id":"819"}],"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"}],"status":"public","type":"conference","publication":"Revised Selected Papers of the 6th International Conference on Learning and Intelligent Optimization - Volume 7219"},{"_id":"48888","user_id":"102979","department":[{"_id":"819"}],"language":[{"iso":"eng"}],"extern":"1","type":"book_chapter","publication":"Learning and Intelligent Optimization","abstract":[{"lang":"eng","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."}],"status":"public","publisher":"Springer Berlin Heidelberg","date_updated":"2023-12-13T10:49:15Z","date_created":"2023-11-14T15:58:59Z","author":[{"last_name":"Mersmann","full_name":"Mersmann, Olaf","first_name":"Olaf"},{"first_name":"Bernd","last_name":"Bischl","full_name":"Bischl, Bernd"},{"first_name":"Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","full_name":"Bossek, Jakob","id":"102979"},{"first_name":"Heike","last_name":"Trautmann","full_name":"Trautmann, Heike"},{"full_name":"Wagner, Markus","last_name":"Wagner","first_name":"Markus"},{"full_name":"Neumann, Frank","last_name":"Neumann","first_name":"Frank"}],"volume":7219,"title":"Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness","doi":"10.1007/978-3-642-34413-8_9","publication_identifier":{"isbn":["978-3-642-34412-1 978-3-642-34413-8"]},"year":"2012","place":"Berlin, Heidelberg","citation":{"apa":"Mersmann, O., Bischl, B., Bossek, J., Trautmann, H., Wagner, M., & Neumann, F. (2012). Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness. In Learning and Intelligent Optimization (Vol. 7219, pp. 115–129). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-34413-8_9","short":"O. Mersmann, B. Bischl, J. Bossek, H. Trautmann, M. Wagner, F. Neumann, in: Learning and Intelligent Optimization, Springer Berlin Heidelberg, Berlin, Heidelberg, 2012, pp. 115–129.","bibtex":"@inbook{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}, volume={7219}, DOI={10.1007/978-3-642-34413-8_9}, 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}, year={2012}, pages={115–129} }","mla":"Mersmann, Olaf, et al. “Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness.” Learning and Intelligent Optimization, vol. 7219, Springer Berlin Heidelberg, 2012, pp. 115–129, doi:10.1007/978-3-642-34413-8_9.","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: Learning and Intelligent Optimization. Vol 7219. Springer Berlin Heidelberg; 2012:115–129. doi:10.1007/978-3-642-34413-8_9","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 Learning and Intelligent Optimization, vol. 7219, Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 115–129.","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 Learning and Intelligent Optimization, 7219:115–129. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. https://doi.org/10.1007/978-3-642-34413-8_9."},"intvolume":" 7219","page":"115–129"},{"publication_identifier":{"isbn":["978-3-642-34413-8"]},"page":"115–129","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 Learning and Intelligent Optimization, 2012, pp. 115–129, doi: https://doi.org/10.1007/978-3-642-34413-8_9.","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 Learning and Intelligent Optimization, edited by Youssef Hamadi and Marc Schoenauer, 115–129. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. https://doi.org/10.1007/978-3-642-34413-8_9.","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. Learning and Intelligent Optimization. Springer Berlin Heidelberg; 2012:115–129. doi:https://doi.org/10.1007/978-3-642-34413-8_9","mla":"Mersmann, Olaf, et al. “Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness.” Learning and Intelligent Optimization, edited by Youssef Hamadi and Marc Schoenauer, Springer Berlin Heidelberg, 2012, pp. 115–129, doi:https://doi.org/10.1007/978-3-642-34413-8_9.","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={https://doi.org/10.1007/978-3-642-34413-8_9}, 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.","apa":"Mersmann, O., Bischl, B., Bossek, J., Trautmann, H., Wagner, M., & Neumann, F. (2012). Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness. In Y. Hamadi & M. Schoenauer (Eds.), Learning and Intelligent Optimization (pp. 115–129). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-34413-8_9"},"place":"Berlin, Heidelberg","year":"2012","author":[{"first_name":"Olaf","full_name":"Mersmann, Olaf","last_name":"Mersmann"},{"last_name":"Bischl","full_name":"Bischl, Bernd","first_name":"Bernd"},{"first_name":"Jakob","full_name":"Bossek, Jakob","id":"102979","orcid":"0000-0002-4121-4668","last_name":"Bossek"},{"full_name":"Trautmann, Heike","id":"100740","last_name":"Trautmann","orcid":"0000-0002-9788-8282","first_name":"Heike"},{"full_name":"Wagner, Markus","last_name":"Wagner","first_name":"Markus"},{"last_name":"Neumann","full_name":"Neumann, Frank","first_name":"Frank"}],"date_created":"2023-08-04T15:53:33Z","date_updated":"2024-06-10T11:57:32Z","publisher":"Springer Berlin Heidelberg","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","publication":"Learning and Intelligent Optimization","type":"conference","status":"public","editor":[{"full_name":"Hamadi, Youssef","last_name":"Hamadi","first_name":"Youssef"},{"first_name":"Marc","last_name":"Schoenauer","full_name":"Schoenauer, Marc"}],"abstract":[{"lang":"eng","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."}],"department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","_id":"46398","language":[{"iso":"eng"}]},{"publication_identifier":{"isbn":["9781450305570"]},"year":"2011","place":"New York, NY, USA","page":"829–836","citation":{"apa":"Mersmann, O., Bischl, B., Trautmann, H., Preuss, M., Weihs, C., & Rudolph, G. (2011). Exploratory Landscape Analysis. Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, 829–836. https://doi.org/10.1145/2001576.2001690","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={10.1145/2001576.2001690}, 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.” Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, Association for Computing Machinery, 2011, pp. 829–836, doi:10.1145/2001576.2001690.","ieee":"O. Mersmann, B. Bischl, H. Trautmann, M. Preuss, C. Weihs, and G. Rudolph, “Exploratory Landscape Analysis,” in Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, 2011, pp. 829–836, doi: 10.1145/2001576.2001690.","chicago":"Mersmann, Olaf, Bernd Bischl, Heike Trautmann, Mike Preuss, Claus Weihs, and Günter Rudolph. “Exploratory Landscape Analysis.” In Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, 829–836. GECCO ’11. New York, NY, USA: Association for Computing Machinery, 2011. https://doi.org/10.1145/2001576.2001690.","ama":"Mersmann O, Bischl B, Trautmann H, Preuss M, Weihs C, Rudolph G. Exploratory Landscape Analysis. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation. GECCO ’11. Association for Computing Machinery; 2011:829–836. doi:10.1145/2001576.2001690"},"date_updated":"2023-10-16T13:54:34Z","publisher":"Association for Computing Machinery","date_created":"2023-08-04T15:58:22Z","author":[{"first_name":"Olaf","last_name":"Mersmann","full_name":"Mersmann, Olaf"},{"last_name":"Bischl","full_name":"Bischl, Bernd","first_name":"Bernd"},{"orcid":"0000-0002-9788-8282","last_name":"Trautmann","full_name":"Trautmann, Heike","id":"100740","first_name":"Heike"},{"first_name":"Mike","last_name":"Preuss","full_name":"Preuss, Mike"},{"first_name":"Claus","full_name":"Weihs, Claus","last_name":"Weihs"},{"first_name":"Günter","last_name":"Rudolph","full_name":"Rudolph, Günter"}],"title":"Exploratory Landscape Analysis","doi":"10.1145/2001576.2001690","publication":"Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation","type":"conference","abstract":[{"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.","lang":"eng"}],"status":"public","_id":"46401","department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","series_title":"GECCO ’11","keyword":["exploratory landscape analysis","evolutionary optimization","fitness landscape","benchmarking","BBOB test set"],"language":[{"iso":"eng"}]},{"user_id":"15504","department":[{"_id":"34"},{"_id":"819"}],"_id":"46402","language":[{"iso":"eng"}],"type":"conference","publication":"Evolutionary Multi-Criterion Optimization","status":"public","editor":[{"first_name":"Ricardo H. C.","last_name":"Takahashi","full_name":"Takahashi, Ricardo H. C."},{"first_name":"Kalyanmoy","full_name":"Deb, Kalyanmoy","last_name":"Deb"},{"first_name":"Elizabeth F.","full_name":"Wanner, Elizabeth F.","last_name":"Wanner"},{"last_name":"Greco","full_name":"Greco, Salvatore","first_name":"Salvatore"}],"abstract":[{"lang":"eng","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."}],"date_created":"2023-08-04T15:59:18Z","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"},{"first_name":"Luis","last_name":"Martí","full_name":"Martí, Luis"}],"publisher":"Springer Berlin Heidelberg","date_updated":"2023-10-16T13:54:50Z","doi":"https://doi.org/10.1007/978-3-642-19893-9_2","title":"A Taxonomy of Online Stopping Criteria for Multi-Objective Evolutionary Algorithms","publication_identifier":{"isbn":["978-3-642-19893-9"]},"citation":{"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. Evolutionary Multi-Criterion Optimization. Springer Berlin Heidelberg; 2011:16–30. doi:https://doi.org/10.1007/978-3-642-19893-9_2","chicago":"Wagner, Tobias, Heike Trautmann, and Luis Martí. “A Taxonomy of Online Stopping Criteria for Multi-Objective Evolutionary Algorithms.” In Evolutionary Multi-Criterion Optimization, edited by Ricardo H. C. Takahashi, Kalyanmoy Deb, Elizabeth F. Wanner, and Salvatore Greco, 16–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. https://doi.org/10.1007/978-3-642-19893-9_2.","ieee":"T. Wagner, H. Trautmann, and L. Martí, “A Taxonomy of Online Stopping Criteria for Multi-Objective Evolutionary Algorithms,” in Evolutionary Multi-Criterion Optimization, 2011, pp. 16–30, doi: https://doi.org/10.1007/978-3-642-19893-9_2.","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.","mla":"Wagner, Tobias, et al. “A Taxonomy of Online Stopping Criteria for Multi-Objective Evolutionary Algorithms.” Evolutionary Multi-Criterion Optimization, edited by Ricardo H. C. Takahashi et al., Springer Berlin Heidelberg, 2011, pp. 16–30, doi:https://doi.org/10.1007/978-3-642-19893-9_2.","bibtex":"@inproceedings{Wagner_Trautmann_Martí_2011, place={Berlin, Heidelberg}, title={A Taxonomy of Online Stopping Criteria for Multi-Objective Evolutionary Algorithms}, DOI={https://doi.org/10.1007/978-3-642-19893-9_2}, 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} }","apa":"Wagner, T., Trautmann, H., & Martí, L. (2011). A Taxonomy of Online Stopping Criteria for Multi-Objective Evolutionary Algorithms. In R. H. C. Takahashi, K. Deb, E. F. Wanner, & S. Greco (Eds.), Evolutionary Multi-Criterion Optimization (pp. 16–30). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-19893-9_2"},"page":"16–30","place":"Berlin, Heidelberg","year":"2011"},{"_id":"46403","user_id":"15504","department":[{"_id":"34"},{"_id":"819"}],"language":[{"iso":"eng"}],"type":"journal_article","publication":"Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering","abstract":[{"lang":"eng","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. "}],"status":"public","date_updated":"2023-10-16T13:55:07Z","author":[{"full_name":"Naujoks, B","last_name":"Naujoks","first_name":"B"},{"orcid":"0000-0002-9788-8282","last_name":"Trautmann","id":"100740","full_name":"Trautmann, Heike","first_name":"Heike"},{"first_name":"S","full_name":"Wessing, S","last_name":"Wessing"},{"last_name":"Weihs","full_name":"Weihs, C","first_name":"C"}],"date_created":"2023-08-04T16:00:28Z","volume":225,"title":"Advanced concepts for multi-objective evolutionary optimization in aircraft industry","doi":"10.1177/0954410011414120","issue":"10","year":"2011","citation":{"ama":"Naujoks B, Trautmann H, Wessing S, Weihs C. Advanced concepts for multi-objective evolutionary optimization in aircraft industry. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering. 2011;225(10):1081-1096. doi:10.1177/0954410011414120","chicago":"Naujoks, B, Heike Trautmann, S Wessing, and C Weihs. “Advanced Concepts for Multi-Objective Evolutionary Optimization in Aircraft Industry.” Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 225, no. 10 (2011): 1081–96. https://doi.org/10.1177/0954410011414120.","ieee":"B. Naujoks, H. Trautmann, S. Wessing, and C. Weihs, “Advanced concepts for multi-objective evolutionary optimization in aircraft industry,” Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, vol. 225, no. 10, pp. 1081–1096, 2011, doi: 10.1177/0954410011414120.","mla":"Naujoks, B., et al. “Advanced Concepts for Multi-Objective Evolutionary Optimization in Aircraft Industry.” Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, vol. 225, no. 10, 2011, pp. 1081–96, doi:10.1177/0954410011414120.","bibtex":"@article{Naujoks_Trautmann_Wessing_Weihs_2011, title={Advanced concepts for multi-objective evolutionary optimization in aircraft industry}, volume={225}, DOI={10.1177/0954410011414120}, 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} }","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.","apa":"Naujoks, B., Trautmann, H., Wessing, S., & Weihs, C. (2011). Advanced concepts for multi-objective evolutionary optimization in aircraft industry. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 225(10), 1081–1096. https://doi.org/10.1177/0954410011414120"},"intvolume":" 225","page":"1081-1096"},{"date_created":"2023-08-04T16:06:43Z","author":[{"last_name":"Mostaghim","full_name":"Mostaghim, Sanaz","first_name":"Sanaz"},{"id":"100740","full_name":"Trautmann, Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","first_name":"Heike"},{"full_name":"Mersmann, Olaf","last_name":"Mersmann","first_name":"Olaf"}],"publisher":"Springer Berlin Heidelberg","date_updated":"2023-10-16T13:56:31Z","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 Parallel Problem Solving from Nature, PPSN XI, edited by Robert Schaefer, Carlos Cotta, Joanna Kołodziej, and Günter Rudolph, 101–110. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. https://doi.org/10.1007/978-3-642-15871-1_11.","ieee":"S. Mostaghim, H. Trautmann, and O. Mersmann, “Preference-Based Multi-Objective Particle Swarm Optimization Using Desirabilities,” in Parallel Problem Solving from Nature, PPSN XI, 2010, pp. 101–110, doi: https://doi.org/10.1007/978-3-642-15871-1_11.","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. Parallel Problem Solving from Nature, PPSN XI. Springer Berlin Heidelberg; 2010:101–110. doi:https://doi.org/10.1007/978-3-642-15871-1_11","apa":"Mostaghim, S., Trautmann, H., & Mersmann, O. (2010). Preference-Based Multi-Objective Particle Swarm Optimization Using Desirabilities. In R. Schaefer, C. Cotta, J. Kołodziej, & G. Rudolph (Eds.), Parallel Problem Solving from Nature, PPSN XI (pp. 101–110). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-15871-1_11","mla":"Mostaghim, Sanaz, et al. “Preference-Based Multi-Objective Particle Swarm Optimization Using Desirabilities.” Parallel Problem Solving from Nature, PPSN XI, edited by Robert Schaefer et al., Springer Berlin Heidelberg, 2010, pp. 101–110, doi:https://doi.org/10.1007/978-3-642-15871-1_11.","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.","bibtex":"@inproceedings{Mostaghim_Trautmann_Mersmann_2010, place={Berlin, Heidelberg}, title={Preference-Based Multi-Objective Particle Swarm Optimization Using Desirabilities}, DOI={https://doi.org/10.1007/978-3-642-15871-1_11}, 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} }"},"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":[{"full_name":"Schaefer, Robert","last_name":"Schaefer","first_name":"Robert"},{"full_name":"Cotta, Carlos","last_name":"Cotta","first_name":"Carlos"},{"last_name":"Kołodziej","full_name":"Kołodziej, Joanna","first_name":"Joanna"},{"last_name":"Rudolph","full_name":"Rudolph, Günter","first_name":"Günter"}]},{"_id":"46405","department":[{"_id":"34"},{"_id":"819"}],"user_id":"15504","series_title":"PPSN’10","keyword":["benchmarking","multidimensional scaling","consensus ranking","evolutionary optimization","BBOB test set"],"language":[{"iso":"eng"}],"publication":"Proceedings of the 11th International Conference on Parallel Problem Solving from Nature: Part I","type":"conference","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"}],"status":"public","publisher":"Springer-Verlag","date_updated":"2023-10-16T13:55:43Z","date_created":"2023-08-04T16:02:28Z","author":[{"full_name":"Mersmann, Olaf","last_name":"Mersmann","first_name":"Olaf"},{"last_name":"Preuss","full_name":"Preuss, Mike","first_name":"Mike"},{"first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","id":"100740","full_name":"Trautmann, Heike"}],"title":"Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis","publication_identifier":{"isbn":["3642158439"]},"place":"Berlin, Heidelberg","year":"2010","page":"73–82","citation":{"ieee":"O. Mersmann, M. Preuss, and H. Trautmann, “Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis,” in Proceedings of the 11th International Conference on Parallel Problem Solving from Nature: Part I, 2010, pp. 73–82.","chicago":"Mersmann, Olaf, Mike Preuss, and Heike Trautmann. “Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis.” In Proceedings of the 11th International Conference on Parallel Problem Solving from Nature: Part I, 73–82. PPSN’10. Berlin, Heidelberg: Springer-Verlag, 2010.","ama":"Mersmann O, Preuss M, Trautmann H. Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis. In: Proceedings of the 11th International Conference on Parallel Problem Solving from Nature: Part I. PPSN’10. Springer-Verlag; 2010:73–82.","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.","mla":"Mersmann, Olaf, et al. “Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis.” Proceedings of the 11th International Conference on Parallel Problem Solving from Nature: Part I, Springer-Verlag, 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} }","apa":"Mersmann, O., Preuss, M., & Trautmann, H. (2010). Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis. Proceedings of the 11th International Conference on Parallel Problem Solving from Nature: Part I, 73–82."}},{"date_updated":"2023-10-16T13:55:59Z","publisher":"Springer","volume":6073,"author":[{"first_name":"O","last_name":"Mersmann","full_name":"Mersmann, O"},{"first_name":"Heike","full_name":"Trautmann, Heike","id":"100740","orcid":"0000-0002-9788-8282","last_name":"Trautmann"},{"last_name":"Naujoks","full_name":"Naujoks, B","first_name":"B"},{"first_name":"C","last_name":"Weihs","full_name":"Weihs, C"}],"date_created":"2023-08-04T16:03:45Z","title":"On the Distribution of EMOA Hypervolumes","year":"2010","intvolume":" 6073","page":"333–337","citation":{"ieee":"O. Mersmann, H. Trautmann, B. Naujoks, and C. Weihs, “On the Distribution of EMOA Hypervolumes,” in Learning and Intelligent Optimization, 4$^th$ International Conference, LION 4, Venice, Italy, 2010, vol. 6073, pp. 333–337.","chicago":"Mersmann, O, Heike Trautmann, B Naujoks, and C Weihs. “On the Distribution of EMOA Hypervolumes.” In Learning and Intelligent Optimization, 4$^th$ International Conference, LION 4, Venice, Italy, edited by C Blum and R Battiti, 6073:333–337. Lecture Notes in Computer Science. Springer, 2010.","ama":"Mersmann O, Trautmann H, Naujoks B, Weihs C. On the Distribution of EMOA Hypervolumes. In: Blum C, Battiti R, eds. Learning and Intelligent Optimization, 4$^th$ International Conference, LION 4, Venice, Italy. Vol 6073. Lecture Notes in Computer Science. Springer; 2010:333–337.","apa":"Mersmann, O., Trautmann, H., Naujoks, B., & Weihs, C. (2010). On the Distribution of EMOA Hypervolumes. In C. Blum & R. Battiti (Eds.), Learning and Intelligent Optimization, 4$^th$ International Conference, LION 4, Venice, Italy (Vol. 6073, pp. 333–337). Springer.","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} }","mla":"Mersmann, O., et al. “On the Distribution of EMOA Hypervolumes.” Learning and Intelligent Optimization, 4$^th$ International Conference, LION 4, Venice, Italy, edited by C Blum and R Battiti, vol. 6073, Springer, 2010, pp. 333–337.","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."},"_id":"46406","department":[{"_id":"34"},{"_id":"819"}],"series_title":"Lecture Notes in Computer Science","user_id":"15504","language":[{"iso":"eng"}],"publication":"Learning and Intelligent Optimization, 4$^th$ International Conference, LION 4, Venice, Italy","type":"conference","editor":[{"full_name":"Blum, C","last_name":"Blum","first_name":"C"},{"full_name":"Battiti, R","last_name":"Battiti","first_name":"R"}],"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"}]