[{"author":[{"last_name":"Mersmann","first_name":"O","full_name":"Mersmann, O"},{"full_name":"Preuss, M","first_name":"M","last_name":"Preuss"},{"full_name":"Trautmann, Heike","first_name":"Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","id":"100740"},{"full_name":"Bischl, B","first_name":"B","last_name":"Bischl"},{"first_name":"C","last_name":"Weihs","full_name":"Weihs, C"}],"year":"2015","title":"Analyzing the BBOB Results by Means of Benchmarking Concepts","status":"public","intvolume":"        23","date_updated":"2023-10-16T13:43:06Z","language":[{"iso":"eng"}],"_id":"46380","page":"161–185","volume":23,"user_id":"15504","citation":{"mla":"Mersmann, O., et al. “Analyzing the BBOB Results by Means of Benchmarking Concepts.” <i>Evolutionary Computation Journal</i>, vol. 23, no. 1, 2015, pp. 161–185.","bibtex":"@article{Mersmann_Preuss_Trautmann_Bischl_Weihs_2015, title={Analyzing the BBOB Results by Means of Benchmarking Concepts}, volume={23}, number={1}, journal={Evolutionary Computation Journal}, author={Mersmann, O and Preuss, M and Trautmann, Heike and Bischl, B and Weihs, C}, year={2015}, pages={161–185} }","ama":"Mersmann O, Preuss M, Trautmann H, Bischl B, Weihs C. Analyzing the BBOB Results by Means of Benchmarking Concepts. <i>Evolutionary Computation Journal</i>. 2015;23(1):161–185.","ieee":"O. Mersmann, M. Preuss, H. Trautmann, B. Bischl, and C. Weihs, “Analyzing the BBOB Results by Means of Benchmarking Concepts,” <i>Evolutionary Computation Journal</i>, vol. 23, no. 1, pp. 161–185, 2015.","apa":"Mersmann, O., Preuss, M., Trautmann, H., Bischl, B., &#38; Weihs, C. (2015). Analyzing the BBOB Results by Means of Benchmarking Concepts. <i>Evolutionary Computation Journal</i>, <i>23</i>(1), 161–185.","chicago":"Mersmann, O, M Preuss, Heike Trautmann, B Bischl, and C Weihs. “Analyzing the BBOB Results by Means of Benchmarking Concepts.” <i>Evolutionary Computation Journal</i> 23, no. 1 (2015): 161–185.","short":"O. Mersmann, M. Preuss, H. Trautmann, B. Bischl, C. Weihs, Evolutionary Computation Journal 23 (2015) 161–185."},"issue":"1","publication":"Evolutionary Computation Journal","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 is: which algorithm should I use for my real-world problem? Both are connected and neither is easy to answer. We present a theoretical framework for designing and analyzing the raw data of such benchmark experiments. This represents a first step in answering the aforementioned questions. The 2009 and 2010 BBOB benchmark results are analyzed by means of this framework and we derive insight regarding the answers to the two questions. Furthermore, we discuss how to properly aggregate rankings from algorithm evaluations on individual problems into a consensus, its theoretical background and which common pitfalls should be avoided. Finally, we address the grouping of test problems into sets with similar optimizer rankings and investigate whether these are reflected by already proposed test problem characteristics, finding that this is not always the case.","lang":"eng"}],"date_created":"2023-08-04T15:30:11Z","department":[{"_id":"34"},{"_id":"819"}],"type":"journal_article"},{"abstract":[{"lang":"eng","text":"The majority of algorithms can be controlled or adjusted by parameters. Their values can substantially affect the algorithms’ performance. Since the manual exploration of the parameter space is tedious – even for few parameters – several automatic procedures for parameter tuning have been proposed. Recent approaches also take into account some characteristic properties of the problem instances, frequently termed instance features. Our contribution is the proposal of a novel concept for feature-based algorithm parameter tuning, which applies an approximating surrogate model for learning the continuous feature-parameter mapping. To accomplish this, we learn a joint model of the algorithm performance based on both the algorithm parameters and the instance features. The required data is gathered using a recently proposed acquisition function for model refinement in surrogate-based optimization: the profile expected improvement. This function provides an avenue for maximizing the information required for the feature-parameter mapping, i.e., the mapping from instance features to the corresponding optimal algorithm parameters. The approach is validated by applying the tuner to exemplary evolutionary algorithms and problems, for which theoretically grounded or heuristically determined feature-parameter mappings are available."}],"extern":"1","publication":"Proceedings of the Genetic and Evolutionary Computation Conference","type":"conference","keyword":["evolutionary algorithms","model-based optimization","parameter tuning"],"department":[{"_id":"819"}],"date_created":"2023-11-14T15:58:51Z","date_updated":"2023-12-13T10:40:30Z","publication_status":"published","year":"2015","title":"Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement","author":[{"orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob","full_name":"Bossek, Jakob","id":"102979"},{"full_name":"Bischl, Bernd","first_name":"Bernd","last_name":"Bischl"},{"last_name":"Wagner","first_name":"Tobias","full_name":"Wagner, Tobias"},{"first_name":"Günter","last_name":"Rudolph","full_name":"Rudolph, Günter"}],"publication_identifier":{"isbn":["978-1-4503-3472-3"]},"doi":"10.1145/2739480.2754673","series_title":"GECCO ’15","language":[{"iso":"eng"}],"citation":{"bibtex":"@inproceedings{Bossek_Bischl_Wagner_Rudolph_2015, place={New York, NY, USA}, series={GECCO ’15}, title={Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement}, DOI={<a href=\"https://doi.org/10.1145/2739480.2754673\">10.1145/2739480.2754673</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Association for Computing Machinery}, author={Bossek, Jakob and Bischl, Bernd and Wagner, Tobias and Rudolph, Günter}, year={2015}, pages={1319–1326}, collection={GECCO ’15} }","ama":"Bossek J, Bischl B, Wagner T, Rudolph G. Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>. GECCO ’15. Association for Computing Machinery; 2015:1319–1326. doi:<a href=\"https://doi.org/10.1145/2739480.2754673\">10.1145/2739480.2754673</a>","mla":"Bossek, Jakob, et al. “Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement.” <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, Association for Computing Machinery, 2015, pp. 1319–1326, doi:<a href=\"https://doi.org/10.1145/2739480.2754673\">10.1145/2739480.2754673</a>.","short":"J. Bossek, B. Bischl, T. Wagner, G. Rudolph, in: Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, New York, NY, USA, 2015, pp. 1319–1326.","chicago":"Bossek, Jakob, Bernd Bischl, Tobias Wagner, and Günter Rudolph. “Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 1319–1326. GECCO ’15. New York, NY, USA: Association for Computing Machinery, 2015. <a href=\"https://doi.org/10.1145/2739480.2754673\">https://doi.org/10.1145/2739480.2754673</a>.","ieee":"J. Bossek, B. Bischl, T. Wagner, and G. Rudolph, “Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 2015, pp. 1319–1326, doi: <a href=\"https://doi.org/10.1145/2739480.2754673\">10.1145/2739480.2754673</a>.","apa":"Bossek, J., Bischl, B., Wagner, T., &#38; Rudolph, G. (2015). Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement. <i>Proceedings of the Genetic and Evolutionary Computation Conference</i>, 1319–1326. <a href=\"https://doi.org/10.1145/2739480.2754673\">https://doi.org/10.1145/2739480.2754673</a>"},"place":"New York, NY, USA","status":"public","user_id":"102979","page":"1319–1326","_id":"48838","publisher":"Association for Computing Machinery"},{"department":[{"_id":"819"}],"type":"conference","keyword":["combinatorial optimization","metaheuristics","multi-objective optimization","online algorithms","transportation"],"date_created":"2023-11-14T15:58:59Z","extern":"1","abstract":[{"text":"We evaluate the performance of a multi-objective evolutionary algorithm on a class of dynamic routing problems with a single vehicle. In particular we focus on relating algorithmic performance to the most prominent characteristics of problem instances. The routing problem considers two types of customers: mandatory customers must be visited whereas optional customers do not necessarily have to be visited. Moreover, mandatory customers are known prior to the start of the tour whereas optional customers request for service at later points in time with the vehicle already being on its way. The multi-objective optimization problem then results as maximizing the number of visited customers while simultaneously minimizing total travel time. As an a-posteriori evaluation tool, the evolutionary algorithm aims at approximating the related Pareto set for specifically designed benchmarking instances differing in terms of number of customers, geographical layout, fraction of mandatory customers, and request times of optional customers. Conceptional and experimental comparisons to online heuristic procedures are provided.","lang":"eng"}],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference ","doi":"10.1145/2739480.2754705","series_title":"GECCO’15","language":[{"iso":"eng"}],"date_updated":"2023-12-13T10:49:06Z","author":[{"last_name":"Meisel","first_name":"Stephan","full_name":"Meisel, Stephan"},{"last_name":"Grimme","first_name":"Christian","full_name":"Grimme, Christian"},{"id":"102979","first_name":"Jakob","last_name":"Bossek","orcid":"0000-0002-4121-4668","full_name":"Bossek, Jakob"},{"first_name":"Martin","last_name":"Wölck","full_name":"Wölck, Martin"},{"first_name":"Günter","last_name":"Rudolph","full_name":"Rudolph, Günter"},{"first_name":"Heike","last_name":"Trautmann","full_name":"Trautmann, Heike"}],"publication_identifier":{"isbn":["978-1-4503-3472-3"]},"title":"Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle","year":"2015","place":"New York, NY, USA","citation":{"bibtex":"@inproceedings{Meisel_Grimme_Bossek_Wölck_Rudolph_Trautmann_2015, place={New York, NY, USA}, series={GECCO’15}, title={Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle}, DOI={<a href=\"https://doi.org/10.1145/2739480.2754705\">10.1145/2739480.2754705</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference }, publisher={Association for Computing Machinery}, author={Meisel, Stephan and Grimme, Christian and Bossek, Jakob and Wölck, Martin and Rudolph, Günter and Trautmann, Heike}, year={2015}, pages={425–432}, collection={GECCO’15} }","chicago":"Meisel, Stephan, Christian Grimme, Jakob Bossek, Martin Wölck, Günter Rudolph, and Heike Trautmann. “Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference </i>, 425–432. GECCO’15. New York, NY, USA: Association for Computing Machinery, 2015. <a href=\"https://doi.org/10.1145/2739480.2754705\">https://doi.org/10.1145/2739480.2754705</a>.","short":"S. Meisel, C. Grimme, J. Bossek, M. Wölck, G. Rudolph, H. Trautmann, in: Proceedings of the Genetic and Evolutionary Computation Conference , Association for Computing Machinery, New York, NY, USA, 2015, pp. 425–432.","ama":"Meisel S, Grimme C, Bossek J, Wölck M, Rudolph G, Trautmann H. Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference </i>. GECCO’15. Association for Computing Machinery; 2015:425–432. doi:<a href=\"https://doi.org/10.1145/2739480.2754705\">10.1145/2739480.2754705</a>","ieee":"S. Meisel, C. Grimme, J. Bossek, M. Wölck, G. Rudolph, and H. Trautmann, “Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference </i>, 2015, pp. 425–432, doi: <a href=\"https://doi.org/10.1145/2739480.2754705\">10.1145/2739480.2754705</a>.","apa":"Meisel, S., Grimme, C., Bossek, J., Wölck, M., Rudolph, G., &#38; Trautmann, H. (2015). Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle. <i>Proceedings of the Genetic and Evolutionary Computation Conference </i>, 425–432. <a href=\"https://doi.org/10.1145/2739480.2754705\">https://doi.org/10.1145/2739480.2754705</a>","mla":"Meisel, Stephan, et al. “Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle.” <i>Proceedings of the Genetic and Evolutionary Computation Conference </i>, Association for Computing Machinery, 2015, pp. 425–432, doi:<a href=\"https://doi.org/10.1145/2739480.2754705\">10.1145/2739480.2754705</a>."},"user_id":"102979","publisher":"Association for Computing Machinery","_id":"48887","page":"425–432","status":"public"},{"author":[{"first_name":"Stephan","last_name":"Meisel","full_name":"Meisel, Stephan"},{"full_name":"Grimme, Christian","last_name":"Grimme","first_name":"Christian"},{"orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob","full_name":"Bossek, Jakob","id":"102979"},{"full_name":"Wölck, Martin","first_name":"Martin","last_name":"Wölck"},{"full_name":"Rudolph, Guenter","first_name":"Guenter","last_name":"Rudolph"},{"first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","full_name":"Trautmann, Heike","id":"100740"}],"publication_identifier":{"isbn":["978-1-4503-3472-3"]},"title":"Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle","status":"public","year":"2015","date_updated":"2024-06-10T11:57:57Z","language":[{"iso":"eng"}],"_id":"46377","page":"425–432","user_id":"15504","doi":"10.1145/2739480.2754705","citation":{"chicago":"Meisel, Stephan, Christian Grimme, Jakob Bossek, Martin Wölck, Guenter Rudolph, and Heike Trautmann. “Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’15)</i>, 425–432. Madrid, Spain, 2015. <a href=\"https://doi.org/10.1145/2739480.2754705\">https://doi.org/10.1145/2739480.2754705</a>.","short":"S. Meisel, C. Grimme, J. Bossek, M. Wölck, G. Rudolph, H. Trautmann, in: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’15), Madrid, Spain, 2015, pp. 425–432.","ieee":"S. Meisel, C. Grimme, J. Bossek, M. Wölck, G. Rudolph, and H. Trautmann, “Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’15)</i>, 2015, pp. 425–432, doi: <a href=\"https://doi.org/10.1145/2739480.2754705\">10.1145/2739480.2754705</a>.","apa":"Meisel, S., Grimme, C., Bossek, J., Wölck, M., Rudolph, G., &#38; Trautmann, H. (2015). Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle. <i>Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’15)</i>, 425–432. <a href=\"https://doi.org/10.1145/2739480.2754705\">https://doi.org/10.1145/2739480.2754705</a>","bibtex":"@inproceedings{Meisel_Grimme_Bossek_Wölck_Rudolph_Trautmann_2015, place={Madrid, Spain}, title={Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle}, DOI={<a href=\"https://doi.org/10.1145/2739480.2754705\">10.1145/2739480.2754705</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’15)}, author={Meisel, Stephan and Grimme, Christian and Bossek, Jakob and Wölck, Martin and Rudolph, Guenter and Trautmann, Heike}, year={2015}, pages={425–432} }","ama":"Meisel S, Grimme C, Bossek J, Wölck M, Rudolph G, Trautmann H. Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’15)</i>. ; 2015:425–432. doi:<a href=\"https://doi.org/10.1145/2739480.2754705\">10.1145/2739480.2754705</a>","mla":"Meisel, Stephan, et al. “Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle.” <i>Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’15)</i>, 2015, pp. 425–432, doi:<a href=\"https://doi.org/10.1145/2739480.2754705\">10.1145/2739480.2754705</a>."},"publication":"Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’15)","abstract":[{"lang":"eng","text":"We evaluate the performance of a multi-objective evolutionary algorithm on a class of dynamic routing problems with a single vehicle. In particular we focus on relating algorithmic performance to the most prominent characteristics of problem instances. The routing problem considers two types of customers: mandatory customers must be visited whereas optional customers do not necessarily have to be visited. Moreover, mandatory customers are known prior to the start of the tour whereas optional customers request for service at later points in time with the vehicle already being on its way. The multi-objective optimization problem then results as maximizing the number of visited customers while simultaneously minimizing total travel time. As an a-posteriori evaluation tool, the evolutionary algorithm aims at approximating the related Pareto set for specifically designed benchmarking instances differing in terms of number of customers, geographical layout, fraction of mandatory customers, and request times of optional customers. Conceptional and experimental comparisons to online heuristic procedures are provided."}],"date_created":"2023-08-04T15:24:41Z","place":"Madrid, Spain","department":[{"_id":"34"},{"_id":"819"}],"type":"conference"},{"place":"Cham","citation":{"bibtex":"@inbook{Kerschke_Preuss_Hernández_Schütze_Sun_Grimme_Rudolph_Bischl_Trautmann_2014, place={Cham}, series={Advances in Intelligent Systems and Computing}, title={Cell Mapping Techniques for Exploratory Landscape Analysis}, volume={288}, DOI={<a href=\"https://doi.org/10.1007/978-3-319-07494-8_9\">10.1007/978-3-319-07494-8_9</a>}, booktitle={EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V}, publisher={Springer International Publishing}, author={Kerschke, Pascal and Preuss, Mike and Hernández, Carlos and Schütze, Oliver and Sun, Jian-Qiao and Grimme, Christian and Rudolph, Günter and Bischl, Bernd and Trautmann, Heike}, editor={Tantar, Alexandru-Adrian and Tantar, Emilia and Sun, Jian-Qiao and Zhang, Wei and Ding, Qian and Schütze, Oliver and Emmerich, Michael T M and Legrand, Pierrick and Del, Moral Pierre and Coello, Coello Carlos A}, year={2014}, pages={115–131}, collection={Advances in Intelligent Systems and Computing} }","ama":"Kerschke P, Preuss M, Hernández C, et al. Cell Mapping Techniques for Exploratory Landscape Analysis. In: Tantar A-A, Tantar E, Sun J-Q, et al., eds. <i>EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V</i>. Vol 288. Advances in Intelligent Systems and Computing. Springer International Publishing; 2014:115–131. doi:<a href=\"https://doi.org/10.1007/978-3-319-07494-8_9\">10.1007/978-3-319-07494-8_9</a>","mla":"Kerschke, Pascal, et al. “Cell Mapping Techniques for Exploratory Landscape Analysis.” <i>EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V</i>, edited by Alexandru-Adrian Tantar et al., vol. 288, Springer International Publishing, 2014, pp. 115–131, doi:<a href=\"https://doi.org/10.1007/978-3-319-07494-8_9\">10.1007/978-3-319-07494-8_9</a>.","chicago":"Kerschke, Pascal, Mike Preuss, Carlos Hernández, Oliver Schütze, Jian-Qiao Sun, Christian Grimme, Günter Rudolph, Bernd Bischl, and Heike Trautmann. “Cell Mapping Techniques for Exploratory Landscape Analysis.” In <i>EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V</i>, edited by Alexandru-Adrian Tantar, Emilia Tantar, Jian-Qiao Sun, Wei Zhang, Qian Ding, Oliver Schütze, Michael T M Emmerich, Pierrick Legrand, Moral Pierre Del, and Coello Carlos A Coello, 288:115–131. Advances in Intelligent Systems and Computing. Cham: Springer International Publishing, 2014. <a href=\"https://doi.org/10.1007/978-3-319-07494-8_9\">https://doi.org/10.1007/978-3-319-07494-8_9</a>.","short":"P. Kerschke, M. Preuss, C. Hernández, O. Schütze, J.-Q. Sun, C. Grimme, G. Rudolph, B. Bischl, H. Trautmann, in: A.-A. Tantar, E. Tantar, J.-Q. Sun, W. Zhang, Q. Ding, O. Schütze, M.T.M. Emmerich, P. Legrand, M.P. Del, C.C.A. Coello (Eds.), EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V, Springer International Publishing, Cham, 2014, pp. 115–131.","ieee":"P. Kerschke <i>et al.</i>, “Cell Mapping Techniques for Exploratory Landscape Analysis,” in <i>EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V</i>, vol. 288, A.-A. Tantar, E. Tantar, J.-Q. Sun, W. Zhang, Q. Ding, O. Schütze, M. T. M. Emmerich, P. Legrand, M. P. Del, and C. C. A. Coello, Eds. Cham: Springer International Publishing, 2014, pp. 115–131.","apa":"Kerschke, P., Preuss, M., Hernández, C., Schütze, O., Sun, J.-Q., Grimme, C., Rudolph, G., Bischl, B., &#38; Trautmann, H. (2014). Cell Mapping Techniques for Exploratory Landscape Analysis. In A.-A. Tantar, E. Tantar, J.-Q. Sun, W. Zhang, Q. Ding, O. Schütze, M. T. M. Emmerich, P. Legrand, M. P. Del, &#38; C. C. A. Coello (Eds.), <i>EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V</i> (Vol. 288, pp. 115–131). Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-319-07494-8_9\">https://doi.org/10.1007/978-3-319-07494-8_9</a>"},"user_id":"15504","editor":[{"full_name":"Tantar, Alexandru-Adrian","first_name":"Alexandru-Adrian","last_name":"Tantar"},{"full_name":"Tantar, Emilia","last_name":"Tantar","first_name":"Emilia"},{"last_name":"Sun","first_name":"Jian-Qiao","full_name":"Sun, Jian-Qiao"},{"full_name":"Zhang, Wei","last_name":"Zhang","first_name":"Wei"},{"last_name":"Ding","first_name":"Qian","full_name":"Ding, Qian"},{"first_name":"Oliver","last_name":"Schütze","full_name":"Schütze, Oliver"},{"full_name":"Emmerich, Michael T M","first_name":"Michael T M","last_name":"Emmerich"},{"full_name":"Legrand, Pierrick","first_name":"Pierrick","last_name":"Legrand"},{"first_name":"Moral Pierre","last_name":"Del","full_name":"Del, Moral Pierre"},{"full_name":"Coello, Coello Carlos A","last_name":"Coello","first_name":"Coello Carlos A"}],"volume":288,"page":"115–131","publisher":"Springer International Publishing","_id":"46381","status":"public","type":"book_chapter","department":[{"_id":"34"},{"_id":"819"}],"date_created":"2023-08-04T15:31:52Z","abstract":[{"text":"Exploratory Landscape Analysis is an effective and sophisticated approach to characterize the properties of continuous optimization problems. The overall aim is to exploit this knowledge to give recommendations of the individually best suited algorithm for unseen optimization problems. Recent research revealed a high potential of this methodology in this respect based on a set of well-defined, computable features which only requires a quite small sample of function evaluations. In this paper, new features based on the cell mapping concept are introduced and shown to improve the existing feature set in terms of predicting expert-designed high-level properties, such as the degree of multimodality or the global structure, for 2-dimensional single objective optimization problems.","lang":"eng"}],"publication":"EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V","doi":"10.1007/978-3-319-07494-8_9","language":[{"iso":"eng"}],"series_title":"Advances in Intelligent Systems and Computing","date_updated":"2023-10-16T13:43:42Z","intvolume":"       288","year":"2014","title":"Cell Mapping Techniques for Exploratory Landscape Analysis","author":[{"first_name":"Pascal","last_name":"Kerschke","full_name":"Kerschke, Pascal"},{"first_name":"Mike","last_name":"Preuss","full_name":"Preuss, Mike"},{"full_name":"Hernández, Carlos","first_name":"Carlos","last_name":"Hernández"},{"full_name":"Schütze, Oliver","last_name":"Schütze","first_name":"Oliver"},{"last_name":"Sun","first_name":"Jian-Qiao","full_name":"Sun, Jian-Qiao"},{"full_name":"Grimme, Christian","last_name":"Grimme","first_name":"Christian"},{"last_name":"Rudolph","first_name":"Günter","full_name":"Rudolph, Günter"},{"first_name":"Bernd","last_name":"Bischl","full_name":"Bischl, Bernd"},{"id":"100740","orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike","full_name":"Trautmann, Heike"}],"publication_identifier":{"isbn":["978-3-319-07493-1"]}},{"doi":"10.1007/978-3-319-07494-8_18","language":[{"iso":"eng"}],"series_title":"Advances in Intelligent Systems and Computing","date_updated":"2023-10-16T13:43:23Z","intvolume":"       288","title":"A Multiobjective Evolutionary Algorithm Guided by Averaged Hausdorff Distance to Aspiration Sets","year":"2014","publication_identifier":{"isbn":["978-3-319-07493-1"]},"author":[{"full_name":"Rudolph, G","last_name":"Rudolph","first_name":"G"},{"last_name":"Schütze","first_name":"O","full_name":"Schütze, O"},{"last_name":"Grimme","first_name":"C","full_name":"Grimme, C"},{"id":"100740","last_name":"Trautmann","first_name":"Heike","orcid":"0000-0002-9788-8282","full_name":"Trautmann, Heike"}],"type":"book_chapter","department":[{"_id":"34"},{"_id":"819"}],"date_created":"2023-08-04T15:33:57Z","abstract":[{"text":"The incorporation of expert knowledge into multiobjective optimization is an important issue which in this paper is reflected in terms of an aspiration set consisting of multiple reference points. The behaviour of the recently introduced evolutionary multiobjective algorithm AS-EMOA is analysed in detail and comparatively studied for bi-objective optimization problems w.r.t. R-NSGA2 and a respective variant. It will be shown that the averaged Hausdorff distance, integrated into AS-EMOA, is an efficient means to accurately approximate the desired aspiration set.","lang":"eng"}],"publication":"EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V","user_id":"15504","volume":288,"editor":[{"first_name":"A","last_name":"Tantar","full_name":"Tantar, A"},{"full_name":"Tantar, E","last_name":"Tantar","first_name":"E"},{"last_name":"Sun","first_name":"J","full_name":"Sun, J"},{"full_name":"Zhang, W","last_name":"Zhang","first_name":"W"},{"full_name":"Ding, Q","last_name":"Ding","first_name":"Q"},{"full_name":"Schütze, O","last_name":"Schütze","first_name":"O"},{"full_name":"Emmerich, M","last_name":"Emmerich","first_name":"M"},{"full_name":"Legrand, P","first_name":"P","last_name":"Legrand"},{"last_name":"Del","first_name":"Moral P","full_name":"Del, Moral P"},{"full_name":"Coello, Coello CA","last_name":"Coello","first_name":"Coello CA"}],"page":"261–273","publisher":"Springer International Publishing","_id":"46382","status":"public","citation":{"chicago":"Rudolph, G, O Schütze, C Grimme, and Heike Trautmann. “A Multiobjective Evolutionary Algorithm Guided by Averaged Hausdorff Distance to Aspiration Sets.” In <i>EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V</i>, edited by A Tantar, E Tantar, J Sun, W Zhang, Q Ding, O Schütze, M Emmerich, P Legrand, Moral P Del, and Coello CA Coello, 288:261–273. Advances in Intelligent Systems and Computing. Springer International Publishing, 2014. <a href=\"https://doi.org/10.1007/978-3-319-07494-8_18\">https://doi.org/10.1007/978-3-319-07494-8_18</a>.","short":"G. Rudolph, O. Schütze, C. Grimme, H. Trautmann, in: A. Tantar, E. Tantar, J. Sun, W. Zhang, Q. Ding, O. Schütze, M. Emmerich, P. Legrand, M.P. Del, C.C. Coello (Eds.), EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V, Springer International Publishing, 2014, pp. 261–273.","ieee":"G. Rudolph, O. Schütze, C. Grimme, and H. Trautmann, “A Multiobjective Evolutionary Algorithm Guided by Averaged Hausdorff Distance to Aspiration Sets,” in <i>EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V</i>, vol. 288, A. Tantar, E. Tantar, J. Sun, W. Zhang, Q. Ding, O. Schütze, M. Emmerich, P. Legrand, M. P. Del, and C. C. Coello, Eds. Springer International Publishing, 2014, pp. 261–273.","apa":"Rudolph, G., Schütze, O., Grimme, C., &#38; Trautmann, H. (2014). A Multiobjective Evolutionary Algorithm Guided by Averaged Hausdorff Distance to Aspiration Sets. In A. Tantar, E. Tantar, J. Sun, W. Zhang, Q. Ding, O. Schütze, M. Emmerich, P. Legrand, M. P. Del, &#38; C. C. Coello (Eds.), <i>EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V</i> (Vol. 288, pp. 261–273). Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-319-07494-8_18\">https://doi.org/10.1007/978-3-319-07494-8_18</a>","bibtex":"@inbook{Rudolph_Schütze_Grimme_Trautmann_2014, series={Advances in Intelligent Systems and Computing}, title={A Multiobjective Evolutionary Algorithm Guided by Averaged Hausdorff Distance to Aspiration Sets}, volume={288}, DOI={<a href=\"https://doi.org/10.1007/978-3-319-07494-8_18\">10.1007/978-3-319-07494-8_18</a>}, booktitle={EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V}, publisher={Springer International Publishing}, author={Rudolph, G and Schütze, O and Grimme, C and Trautmann, Heike}, editor={Tantar, A and Tantar, E and Sun, J and Zhang, W and Ding, Q and Schütze, O and Emmerich, M and Legrand, P and Del, Moral P and Coello, Coello CA}, year={2014}, pages={261–273}, collection={Advances in Intelligent Systems and Computing} }","ama":"Rudolph G, Schütze O, Grimme C, Trautmann H. A Multiobjective Evolutionary Algorithm Guided by Averaged Hausdorff Distance to Aspiration Sets. In: Tantar A, Tantar E, Sun J, et al., eds. <i>EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V</i>. Vol 288. Advances in Intelligent Systems and Computing. Springer International Publishing; 2014:261–273. doi:<a href=\"https://doi.org/10.1007/978-3-319-07494-8_18\">10.1007/978-3-319-07494-8_18</a>","mla":"Rudolph, G., et al. “A Multiobjective Evolutionary Algorithm Guided by Averaged Hausdorff Distance to Aspiration Sets.” <i>EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V</i>, edited by A Tantar et al., vol. 288, Springer International Publishing, 2014, pp. 261–273, doi:<a href=\"https://doi.org/10.1007/978-3-319-07494-8_18\">10.1007/978-3-319-07494-8_18</a>."}},{"status":"public","volume":8426,"user_id":"15504","_id":"46383","publisher":"Springer","page":"153–156","citation":{"mla":"Rudolph, Günter, et al. “An Aspiration Set EMOA Based on Averaged Hausdorff Distances.” <i>Proceedings of the Learning and Intelligent OptimizatioN Conference (LION 8)</i>, vol. 8426, Springer, 2014, pp. 153–156.","bibtex":"@inproceedings{Rudolph_Grimme_Schütze_Trautmann_2014, place={Gainesville, Florida, USA}, series={Lecture Notes in Computer Science}, title={An Aspiration Set EMOA Based on Averaged Hausdorff Distances}, volume={8426}, booktitle={Proceedings of the Learning and Intelligent OptimizatioN Conference (LION 8)}, publisher={Springer}, author={Rudolph, Günter and Grimme, Christian and Schütze, Oliver and Trautmann, Heike}, year={2014}, pages={153–156}, collection={Lecture Notes in Computer Science} }","ama":"Rudolph G, Grimme C, Schütze O, Trautmann H. An Aspiration Set EMOA Based on Averaged Hausdorff Distances. In: <i>Proceedings of the Learning and Intelligent OptimizatioN Conference (LION 8)</i>. Vol 8426. Lecture Notes in Computer Science. Springer; 2014:153–156.","ieee":"G. Rudolph, C. Grimme, O. Schütze, and H. Trautmann, “An Aspiration Set EMOA Based on Averaged Hausdorff Distances,” in <i>Proceedings of the Learning and Intelligent OptimizatioN Conference (LION 8)</i>, 2014, vol. 8426, pp. 153–156.","apa":"Rudolph, G., Grimme, C., Schütze, O., &#38; Trautmann, H. (2014). An Aspiration Set EMOA Based on Averaged Hausdorff Distances. <i>Proceedings of the Learning and Intelligent OptimizatioN Conference (LION 8)</i>, <i>8426</i>, 153–156.","short":"G. Rudolph, C. Grimme, O. Schütze, H. Trautmann, in: Proceedings of the Learning and Intelligent OptimizatioN Conference (LION 8), Springer, Gainesville, Florida, USA, 2014, pp. 153–156.","chicago":"Rudolph, Günter, Christian Grimme, Oliver Schütze, and Heike Trautmann. “An Aspiration Set EMOA Based on Averaged Hausdorff Distances.” In <i>Proceedings of the Learning and Intelligent OptimizatioN Conference (LION 8)</i>, 8426:153–156. Lecture Notes in Computer Science. Gainesville, Florida, USA: Springer, 2014."},"place":"Gainesville, Florida, USA","intvolume":"      8426","date_updated":"2023-10-16T13:43:59Z","author":[{"full_name":"Rudolph, Günter","first_name":"Günter","last_name":"Rudolph"},{"first_name":"Christian","last_name":"Grimme","full_name":"Grimme, Christian"},{"first_name":"Oliver","last_name":"Schütze","full_name":"Schütze, Oliver"},{"id":"100740","full_name":"Trautmann, Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","first_name":"Heike"}],"year":"2014","title":"An Aspiration Set EMOA Based on Averaged Hausdorff Distances","series_title":"Lecture Notes in Computer Science","language":[{"iso":"eng"}],"abstract":[{"lang":"eng","text":"We propose an evolutionary multiobjective algorithm that approximates multiple reference points (the aspiration set) in a single run using the concept of the averaged Hausdorff distance."}],"publication":"Proceedings of the Learning and Intelligent OptimizatioN Conference (LION 8)","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","date_created":"2023-08-04T15:34:44Z"},{"_id":"46384","publisher":"Springer","page":"141–150","volume":8672,"editor":[{"first_name":"T","last_name":"Bartz-Beielstein","full_name":"Bartz-Beielstein, T"},{"first_name":"J","last_name":"Branke","full_name":"Branke, J"},{"full_name":"Filipic, B","last_name":"Filipic","first_name":"B"},{"full_name":"Smith, J","first_name":"J","last_name":"Smith"}],"user_id":"15504","status":"public","place":"Ljubljana, Slovenia","citation":{"mla":"Wessing, S., et al. “Stopping Criteria for Multimodal Optimization.” <i>Proceedings of the Parallel Problem Solving from Nature — PPSN XIII</i>, edited by T Bartz-Beielstein et al., vol. 8672, Springer, 2014, pp. 141–150, doi:<a href=\"https://doi.org/10.1007/978-3-319-10762-2_14\">10.1007/978-3-319-10762-2_14</a>.","bibtex":"@inproceedings{Wessing_Preuss_Trautmann_2014, place={Ljubljana, Slovenia}, series={Lecture Notes in Computer Science}, title={Stopping Criteria for Multimodal Optimization}, volume={8672}, DOI={<a href=\"https://doi.org/10.1007/978-3-319-10762-2_14\">10.1007/978-3-319-10762-2_14</a>}, booktitle={Proceedings of the Parallel Problem Solving from Nature — PPSN XIII}, publisher={Springer}, author={Wessing, S and Preuss, M and Trautmann, Heike}, editor={Bartz-Beielstein, T and Branke, J and Filipic, B and Smith, J}, year={2014}, pages={141–150}, collection={Lecture Notes in Computer Science} }","ama":"Wessing S, Preuss M, Trautmann H. Stopping Criteria for Multimodal Optimization. In: Bartz-Beielstein T, Branke J, Filipic B, Smith J, eds. <i>Proceedings of the Parallel Problem Solving from Nature — PPSN XIII</i>. Vol 8672. Lecture Notes in Computer Science. Springer; 2014:141–150. doi:<a href=\"https://doi.org/10.1007/978-3-319-10762-2_14\">10.1007/978-3-319-10762-2_14</a>","ieee":"S. Wessing, M. Preuss, and H. Trautmann, “Stopping Criteria for Multimodal Optimization,” in <i>Proceedings of the Parallel Problem Solving from Nature — PPSN XIII</i>, 2014, vol. 8672, pp. 141–150, doi: <a href=\"https://doi.org/10.1007/978-3-319-10762-2_14\">10.1007/978-3-319-10762-2_14</a>.","apa":"Wessing, S., Preuss, M., &#38; Trautmann, H. (2014). Stopping Criteria for Multimodal Optimization. In T. Bartz-Beielstein, J. Branke, B. Filipic, &#38; J. Smith (Eds.), <i>Proceedings of the Parallel Problem Solving from Nature — PPSN XIII</i> (Vol. 8672, pp. 141–150). Springer. <a href=\"https://doi.org/10.1007/978-3-319-10762-2_14\">https://doi.org/10.1007/978-3-319-10762-2_14</a>","short":"S. Wessing, M. Preuss, H. Trautmann, in: T. Bartz-Beielstein, J. Branke, B. Filipic, J. Smith (Eds.), Proceedings of the Parallel Problem Solving from Nature — PPSN XIII, Springer, Ljubljana, Slovenia, 2014, pp. 141–150.","chicago":"Wessing, S, M Preuss, and Heike Trautmann. “Stopping Criteria for Multimodal Optimization.” In <i>Proceedings of the Parallel Problem Solving from Nature — PPSN XIII</i>, edited by T Bartz-Beielstein, J Branke, B Filipic, and J Smith, 8672:141–150. Lecture Notes in Computer Science. Ljubljana, Slovenia: Springer, 2014. <a href=\"https://doi.org/10.1007/978-3-319-10762-2_14\">https://doi.org/10.1007/978-3-319-10762-2_14</a>."},"language":[{"iso":"eng"}],"series_title":"Lecture Notes in Computer Science","doi":"10.1007/978-3-319-10762-2_14","author":[{"first_name":"S","last_name":"Wessing","full_name":"Wessing, S"},{"last_name":"Preuss","first_name":"M","full_name":"Preuss, M"},{"first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","full_name":"Trautmann, Heike","id":"100740"}],"year":"2014","title":"Stopping Criteria for Multimodal Optimization","intvolume":"      8672","date_updated":"2023-10-16T13:44:15Z","date_created":"2023-08-04T15:36:01Z","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","publication":"Proceedings of the Parallel Problem Solving from Nature — PPSN XIII","abstract":[{"lang":"eng","text":"Multimodal optimization requires maintenance of a good search space coverage and approximation of several optima at the same time. We analyze two constitutive optimization algorithms and show that in many cases, a phase transition occurs at some point, so that either diversity collapses or optimization stagnates. But how to derive suitable stopping criteria for multimodal optimization? Experimental results indicate that an algorithm’s population contains sufficient information to estimate the point in time when several performance indicators reach their optimum. Thus, stopping criteria are formulated based on summary characteristics employing objective values and mutation strength."}]},{"title":"The Directed Search Method for Pareto Front Approximations with Maximum Dominated Hypervolume","year":"2013","author":[{"full_name":"Sosa, Hernández V","first_name":"Hernández V","last_name":"Sosa"},{"full_name":"Schütze, O","first_name":"O","last_name":"Schütze"},{"first_name":"G","last_name":"Rudolph","full_name":"Rudolph, G"},{"full_name":"Trautmann, Heike","first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","id":"100740"}],"publication_identifier":{"isbn":["978-3-319-01127-1"]},"date_updated":"2023-10-16T13:44:50Z","intvolume":"       227","series_title":"Advances in Intelligent Systems and Computing","language":[{"iso":"eng"}],"doi":"10.1007/978-3-319-01128-8_13","publication":"EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV","abstract":[{"text":"In many applications one is faced with the problem that multiple objectives have to be optimized at the same time. Since typically the solution set of such multi-objective optimization problems forms a manifold which cannot be computed analytically, one is in many cases interested in a suitable finite size approximation of this set. One widely used approach is to find a representative set that maximizes the dominated hypervolume that is defined by the images in objective space of these solutions and a given reference point.\r\n\r\nIn this paper, we propose a new point-wise iterative search procedure, Hypervolume Directed Search (HVDS), that aims to increase the hypervolume of a given point in an archive for bi-objective unconstrained optimization problems. We present the HVDS both as a standalone algorithm and as a local searcher within a specialized evolutionary algorithm. Numerical results confirm the strength of the novel approach.","lang":"eng"}],"date_created":"2023-08-04T15:37:00Z","type":"book_chapter","department":[{"_id":"34"},{"_id":"819"}],"status":"public","page":"189–205","publisher":"Springer International Publishing","_id":"46385","user_id":"15504","editor":[{"full_name":"Emmerich, M","last_name":"Emmerich","first_name":"M"},{"first_name":"A","last_name":"Deutz","full_name":"Deutz, A"},{"last_name":"Schuetze","first_name":"O","full_name":"Schuetze, O"},{"full_name":"Bäck, T","first_name":"T","last_name":"Bäck"},{"full_name":"Tantar, A","first_name":"A","last_name":"Tantar"},{"full_name":"Moral, PD","last_name":"Moral","first_name":"PD"},{"last_name":"Legrand","first_name":"P","full_name":"Legrand, P"},{"last_name":"Bouvry","first_name":"P","full_name":"Bouvry, P"},{"first_name":"CA","last_name":"Coello","full_name":"Coello, CA"}],"volume":227,"citation":{"ama":"Sosa HV, Schütze O, Rudolph G, Trautmann H. The Directed Search Method for Pareto Front Approximations with Maximum Dominated Hypervolume. In: Emmerich M, Deutz A, Schuetze O, et al., eds. <i>EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV</i>. Vol 227. Advances in Intelligent Systems and Computing. Springer International Publishing; 2013:189–205. doi:<a href=\"https://doi.org/10.1007/978-3-319-01128-8_13\">10.1007/978-3-319-01128-8_13</a>","bibtex":"@inbook{Sosa_Schütze_Rudolph_Trautmann_2013, series={Advances in Intelligent Systems and Computing}, title={The Directed Search Method for Pareto Front Approximations with Maximum Dominated Hypervolume}, volume={227}, DOI={<a href=\"https://doi.org/10.1007/978-3-319-01128-8_13\">10.1007/978-3-319-01128-8_13</a>}, booktitle={EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV}, publisher={Springer International Publishing}, author={Sosa, Hernández V and Schütze, O and Rudolph, G and Trautmann, Heike}, editor={Emmerich, M and Deutz, A and Schuetze, O and Bäck, T and Tantar, A and Moral, PD and Legrand, P and Bouvry, P and Coello, CA}, year={2013}, pages={189–205}, collection={Advances in Intelligent Systems and Computing} }","mla":"Sosa, Hernández V., et al. “The Directed Search Method for Pareto Front Approximations with Maximum Dominated Hypervolume.” <i>EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV</i>, edited by M Emmerich et al., vol. 227, Springer International Publishing, 2013, pp. 189–205, doi:<a href=\"https://doi.org/10.1007/978-3-319-01128-8_13\">10.1007/978-3-319-01128-8_13</a>.","chicago":"Sosa, Hernández V, O Schütze, G Rudolph, and Heike Trautmann. “The Directed Search Method for Pareto Front Approximations with Maximum Dominated Hypervolume.” In <i>EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV</i>, edited by M Emmerich, A Deutz, O Schuetze, T Bäck, A Tantar, PD Moral, P Legrand, P Bouvry, and CA Coello, 227:189–205. Advances in Intelligent Systems and Computing. Springer International Publishing, 2013. <a href=\"https://doi.org/10.1007/978-3-319-01128-8_13\">https://doi.org/10.1007/978-3-319-01128-8_13</a>.","short":"H.V. Sosa, O. Schütze, G. Rudolph, H. Trautmann, in: M. Emmerich, A. Deutz, O. Schuetze, T. Bäck, A. Tantar, P. Moral, P. Legrand, P. Bouvry, C. Coello (Eds.), EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV, Springer International Publishing, 2013, pp. 189–205.","apa":"Sosa, H. V., Schütze, O., Rudolph, G., &#38; Trautmann, H. (2013). The Directed Search Method for Pareto Front Approximations with Maximum Dominated Hypervolume. In M. Emmerich, A. Deutz, O. Schuetze, T. Bäck, A. Tantar, P. Moral, P. Legrand, P. Bouvry, &#38; C. Coello (Eds.), <i>EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV</i> (Vol. 227, pp. 189–205). Springer International Publishing. <a href=\"https://doi.org/10.1007/978-3-319-01128-8_13\">https://doi.org/10.1007/978-3-319-01128-8_13</a>","ieee":"H. V. Sosa, O. Schütze, G. Rudolph, and H. Trautmann, “The Directed Search Method for Pareto Front Approximations with Maximum Dominated Hypervolume,” in <i>EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV</i>, vol. 227, M. Emmerich, A. Deutz, O. Schuetze, T. Bäck, A. Tantar, P. Moral, P. Legrand, P. Bouvry, and C. Coello, Eds. Springer International Publishing, 2013, pp. 189–205."}},{"author":[{"full_name":"Trautmann, Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","first_name":"Heike","id":"100740"},{"full_name":"Rudolph, G","first_name":"G","last_name":"Rudolph"},{"first_name":"C","last_name":"Dominguez-Medina","full_name":"Dominguez-Medina, C"},{"first_name":"O","last_name":"Schütze","full_name":"Schütze, O"}],"publication_identifier":{"isbn":["978-3-642-31518-3"]},"title":"Finding Evenly Spaced Pareto Fronts for Three-Objective Optimization Problems","year":"2013","intvolume":"       175","date_updated":"2023-10-16T13:45:12Z","language":[{"iso":"eng"}],"series_title":"Advances in Intelligent Systems and Computing","doi":"10.1007/978-3-642-31519-0_6","publication":"EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II","abstract":[{"text":"The averaged Hausdorff distance Δ p is a performance indicator in multi-objective evolutionary optimization which simultaneously takes into account proximity to the true Pareto front and uniform spread of solutions. Recently, the multi-objective evolutionary algorithm Δ p -EMOA was introduced which successfully generates evenly spaced Pareto front approximations for bi-objective problems by integrating an external archiving strategy into the SMS-EMOA based on Δ p . In this work a conceptual generalization of the Δ p -EMOA for higher objective space dimensions is presented and experimentally compared to state-of-the art EMOA as well as specialized EMOA variants on three-dimensional optimization problems.","lang":"eng"}],"date_created":"2023-08-04T15:38:25Z","department":[{"_id":"34"},{"_id":"819"}],"type":"book_chapter","status":"public","publisher":"Springer Berlin Heidelberg","_id":"46386","page":"89–105","volume":175,"editor":[{"first_name":"O","last_name":"Schütze","full_name":"Schütze, O"},{"first_name":"Coello CA","last_name":"Coello","full_name":"Coello, Coello CA"},{"first_name":"A","last_name":"Tantar","full_name":"Tantar, A"},{"full_name":"Tantar, E","first_name":"E","last_name":"Tantar"},{"full_name":"Bouvry, P","last_name":"Bouvry","first_name":"P"},{"first_name":"Moral P","last_name":"Del","full_name":"Del, Moral P"},{"last_name":"Legrand","first_name":"P","full_name":"Legrand, P"}],"user_id":"15504","citation":{"ieee":"H. Trautmann, G. Rudolph, C. Dominguez-Medina, and O. Schütze, “Finding Evenly Spaced Pareto Fronts for Three-Objective Optimization Problems,” in <i>EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II</i>, vol. 175, O. Schütze, C. C. Coello, A. Tantar, E. Tantar, P. Bouvry, M. P. Del, and P. Legrand, Eds. Springer Berlin Heidelberg, 2013, pp. 89–105.","apa":"Trautmann, H., Rudolph, G., Dominguez-Medina, C., &#38; Schütze, O. (2013). Finding Evenly Spaced Pareto Fronts for Three-Objective Optimization Problems. In O. Schütze, C. C. Coello, A. Tantar, E. Tantar, P. Bouvry, M. P. Del, &#38; P. Legrand (Eds.), <i>EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II</i> (Vol. 175, pp. 89–105). Springer Berlin Heidelberg. <a href=\"https://doi.org/10.1007/978-3-642-31519-0_6\">https://doi.org/10.1007/978-3-642-31519-0_6</a>","chicago":"Trautmann, Heike, G Rudolph, C Dominguez-Medina, and O Schütze. “Finding Evenly Spaced Pareto Fronts for Three-Objective Optimization Problems.” In <i>EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II</i>, edited by O Schütze, Coello CA Coello, A Tantar, E Tantar, P Bouvry, Moral P Del, and P Legrand, 175:89–105. Advances in Intelligent Systems and Computing. Springer Berlin Heidelberg, 2013. <a href=\"https://doi.org/10.1007/978-3-642-31519-0_6\">https://doi.org/10.1007/978-3-642-31519-0_6</a>.","short":"H. Trautmann, G. Rudolph, C. Dominguez-Medina, O. Schütze, in: O. Schütze, C.C. Coello, A. Tantar, E. Tantar, P. Bouvry, M.P. Del, P. Legrand (Eds.), EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II, Springer Berlin Heidelberg, 2013, pp. 89–105.","mla":"Trautmann, Heike, et al. “Finding Evenly Spaced Pareto Fronts for Three-Objective Optimization Problems.” <i>EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II</i>, edited by O Schütze et al., vol. 175, Springer Berlin Heidelberg, 2013, pp. 89–105, doi:<a href=\"https://doi.org/10.1007/978-3-642-31519-0_6\">10.1007/978-3-642-31519-0_6</a>.","bibtex":"@inbook{Trautmann_Rudolph_Dominguez-Medina_Schütze_2013, series={Advances in Intelligent Systems and Computing}, title={Finding Evenly Spaced Pareto Fronts for Three-Objective Optimization Problems}, volume={175}, DOI={<a href=\"https://doi.org/10.1007/978-3-642-31519-0_6\">10.1007/978-3-642-31519-0_6</a>}, booktitle={EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II}, publisher={Springer Berlin Heidelberg}, author={Trautmann, Heike and Rudolph, G and Dominguez-Medina, C and Schütze, O}, editor={Schütze, O and Coello, Coello CA and Tantar, A and Tantar, E and Bouvry, P and Del, Moral P and Legrand, P}, year={2013}, pages={89–105}, collection={Advances in Intelligent Systems and Computing} }","ama":"Trautmann H, Rudolph G, Dominguez-Medina C, Schütze O. Finding Evenly Spaced Pareto Fronts for Three-Objective Optimization Problems. In: Schütze O, Coello CC, Tantar A, et al., eds. <i>EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II</i>. Vol 175. Advances in Intelligent Systems and Computing. Springer Berlin Heidelberg; 2013:89–105. doi:<a href=\"https://doi.org/10.1007/978-3-642-31519-0_6\">10.1007/978-3-642-31519-0_6</a>"}},{"language":[{"iso":"eng"}],"series_title":"FOGA XII ’13","doi":"10.1145/2460239.2460253","title":"A Feature-Based Comparison of Local Search and the Christofides Algorithm for the Travelling Salesperson Problem","year":"2013","author":[{"full_name":"Nallaperuma, Samadhi","last_name":"Nallaperuma","first_name":"Samadhi"},{"first_name":"Markus","last_name":"Wagner","full_name":"Wagner, Markus"},{"last_name":"Neumann","first_name":"Frank","full_name":"Neumann, Frank"},{"full_name":"Bischl, Bernd","first_name":"Bernd","last_name":"Bischl"},{"full_name":"Mersmann, Olaf","last_name":"Mersmann","first_name":"Olaf"},{"orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike","full_name":"Trautmann, Heike","id":"100740"}],"publication_identifier":{"isbn":["9781450319904"]},"date_updated":"2023-10-16T13:45:53Z","date_created":"2023-08-04T15:42:03Z","type":"conference","keyword":["approximation algorithms","local search","traveling salesperson problem","feature selection","prediction","classification"],"department":[{"_id":"34"},{"_id":"819"}],"publication":"Proceedings of the Twelfth Workshop on Foundations of Genetic Algorithms XII","abstract":[{"text":"Understanding the behaviour of well-known algorithms for classical NP-hard optimisation problems is still a difficult task. With this paper, we contribute to this research direction and carry out a feature based comparison of local search and the well-known Christofides approximation algorithm for the Traveling Salesperson Problem. We use an evolutionary algorithm approach to construct easy and hard instances for the Christofides algorithm, where we measure hardness in terms of approximation ratio. Our results point out important features and lead to hard and easy instances for this famous algorithm. Furthermore, our cross-comparison gives new insights on the complementary benefits of the different approaches.","lang":"eng"}],"page":"147–160","_id":"46388","publisher":"Association for Computing Machinery","user_id":"15504","status":"public","place":"New York, NY, USA","citation":{"short":"S. Nallaperuma, M. Wagner, F. Neumann, B. Bischl, O. Mersmann, H. Trautmann, in: Proceedings of the Twelfth Workshop on Foundations of Genetic Algorithms XII, Association for Computing Machinery, New York, NY, USA, 2013, pp. 147–160.","chicago":"Nallaperuma, Samadhi, Markus Wagner, Frank Neumann, Bernd Bischl, Olaf Mersmann, and Heike Trautmann. “A Feature-Based Comparison of Local Search and the Christofides Algorithm for the Travelling Salesperson Problem.” In <i>Proceedings of the Twelfth Workshop on Foundations of Genetic Algorithms XII</i>, 147–160. FOGA XII ’13. New York, NY, USA: Association for Computing Machinery, 2013. <a href=\"https://doi.org/10.1145/2460239.2460253\">https://doi.org/10.1145/2460239.2460253</a>.","ieee":"S. Nallaperuma, M. Wagner, F. Neumann, B. Bischl, O. Mersmann, and H. Trautmann, “A Feature-Based Comparison of Local Search and the Christofides Algorithm for the Travelling Salesperson Problem,” in <i>Proceedings of the Twelfth Workshop on Foundations of Genetic Algorithms XII</i>, 2013, pp. 147–160, doi: <a href=\"https://doi.org/10.1145/2460239.2460253\">10.1145/2460239.2460253</a>.","apa":"Nallaperuma, S., Wagner, M., Neumann, F., Bischl, B., Mersmann, O., &#38; Trautmann, H. (2013). A Feature-Based Comparison of Local Search and the Christofides Algorithm for the Travelling Salesperson Problem. <i>Proceedings of the Twelfth Workshop on Foundations of Genetic Algorithms XII</i>, 147–160. <a href=\"https://doi.org/10.1145/2460239.2460253\">https://doi.org/10.1145/2460239.2460253</a>","bibtex":"@inproceedings{Nallaperuma_Wagner_Neumann_Bischl_Mersmann_Trautmann_2013, place={New York, NY, USA}, series={FOGA XII ’13}, title={A Feature-Based Comparison of Local Search and the Christofides Algorithm for the Travelling Salesperson Problem}, DOI={<a href=\"https://doi.org/10.1145/2460239.2460253\">10.1145/2460239.2460253</a>}, booktitle={Proceedings of the Twelfth Workshop on Foundations of Genetic Algorithms XII}, publisher={Association for Computing Machinery}, author={Nallaperuma, Samadhi and Wagner, Markus and Neumann, Frank and Bischl, Bernd and Mersmann, Olaf and Trautmann, Heike}, year={2013}, pages={147–160}, collection={FOGA XII ’13} }","ama":"Nallaperuma S, Wagner M, Neumann F, Bischl B, Mersmann O, Trautmann H. A Feature-Based Comparison of Local Search and the Christofides Algorithm for the Travelling Salesperson Problem. In: <i>Proceedings of the Twelfth Workshop on Foundations of Genetic Algorithms XII</i>. FOGA XII ’13. Association for Computing Machinery; 2013:147–160. doi:<a href=\"https://doi.org/10.1145/2460239.2460253\">10.1145/2460239.2460253</a>","mla":"Nallaperuma, Samadhi, et al. “A Feature-Based Comparison of Local Search and the Christofides Algorithm for the Travelling Salesperson Problem.” <i>Proceedings of the Twelfth Workshop on Foundations of Genetic Algorithms XII</i>, Association for Computing Machinery, 2013, pp. 147–160, doi:<a href=\"https://doi.org/10.1145/2460239.2460253\">10.1145/2460239.2460253</a>."}},{"citation":{"bibtex":"@inproceedings{Rudolph_Trautmann_Sengupta_Schütze_2013, series={Lecture Notes in Computer Science}, title={Evenly Spaced Pareto Front Approximations for Tricriteria Problems Based on Triangulation}, volume={7811}, DOI={<a href=\"https://doi.org/10.1007/978-3-642-37140-0_34\">https://doi.org/10.1007/978-3-642-37140-0_34</a>}, booktitle={Evolutionary Multi-Criterion Optimization — 7$^th$ International Conference, EMO 2013, Sheffield, UK, Proceedings}, publisher={Springer}, author={Rudolph, G and Trautmann, Heike and Sengupta, S and Schütze, O}, editor={Purshouse, RC and Fleming, PJ and Fonseca, CM and Greco, S and Shaw, J}, year={2013}, pages={443–458}, collection={Lecture Notes in Computer Science} }","ama":"Rudolph G, Trautmann H, Sengupta S, Schütze O. Evenly Spaced Pareto Front Approximations for Tricriteria Problems Based on Triangulation. In: Purshouse R, Fleming P, Fonseca C, Greco S, Shaw J, eds. <i>Evolutionary Multi-Criterion Optimization — 7$^th$ International Conference, EMO 2013, Sheffield, UK, Proceedings</i>. Vol 7811. Lecture Notes in Computer Science. Springer; 2013:443–458. doi:<a href=\"https://doi.org/10.1007/978-3-642-37140-0_34\">https://doi.org/10.1007/978-3-642-37140-0_34</a>","mla":"Rudolph, G., et al. “Evenly Spaced Pareto Front Approximations for Tricriteria Problems Based on Triangulation.” <i>Evolutionary Multi-Criterion Optimization — 7$^th$ International Conference, EMO 2013, Sheffield, UK, Proceedings</i>, edited by RC Purshouse et al., vol. 7811, Springer, 2013, pp. 443–458, doi:<a href=\"https://doi.org/10.1007/978-3-642-37140-0_34\">https://doi.org/10.1007/978-3-642-37140-0_34</a>.","short":"G. Rudolph, H. Trautmann, S. Sengupta, O. Schütze, in: R. Purshouse, P. Fleming, C. Fonseca, S. Greco, J. Shaw (Eds.), Evolutionary Multi-Criterion Optimization — 7$^th$ International Conference, EMO 2013, Sheffield, UK, Proceedings, Springer, 2013, pp. 443–458.","chicago":"Rudolph, G, Heike Trautmann, S Sengupta, and O Schütze. “Evenly Spaced Pareto Front Approximations for Tricriteria Problems Based on Triangulation.” In <i>Evolutionary Multi-Criterion Optimization — 7$^th$ International Conference, EMO 2013, Sheffield, UK, Proceedings</i>, edited by RC Purshouse, PJ Fleming, CM Fonseca, S Greco, and J Shaw, 7811:443–458. Lecture Notes in Computer Science. Springer, 2013. <a href=\"https://doi.org/10.1007/978-3-642-37140-0_34\">https://doi.org/10.1007/978-3-642-37140-0_34</a>.","ieee":"G. Rudolph, H. Trautmann, S. Sengupta, and O. Schütze, “Evenly Spaced Pareto Front Approximations for Tricriteria Problems Based on Triangulation,” in <i>Evolutionary Multi-Criterion Optimization — 7$^th$ International Conference, EMO 2013, Sheffield, UK, Proceedings</i>, 2013, vol. 7811, pp. 443–458, doi: <a href=\"https://doi.org/10.1007/978-3-642-37140-0_34\">https://doi.org/10.1007/978-3-642-37140-0_34</a>.","apa":"Rudolph, G., Trautmann, H., Sengupta, S., &#38; Schütze, O. (2013). Evenly Spaced Pareto Front Approximations for Tricriteria Problems Based on Triangulation. In R. Purshouse, P. Fleming, C. Fonseca, S. Greco, &#38; J. Shaw (Eds.), <i>Evolutionary Multi-Criterion Optimization — 7$^th$ International Conference, EMO 2013, Sheffield, UK, Proceedings</i> (Vol. 7811, pp. 443–458). Springer. <a href=\"https://doi.org/10.1007/978-3-642-37140-0_34\">https://doi.org/10.1007/978-3-642-37140-0_34</a>"},"editor":[{"full_name":"Purshouse, RC","last_name":"Purshouse","first_name":"RC"},{"full_name":"Fleming, PJ","last_name":"Fleming","first_name":"PJ"},{"full_name":"Fonseca, CM","last_name":"Fonseca","first_name":"CM"},{"full_name":"Greco, S","last_name":"Greco","first_name":"S"},{"first_name":"J","last_name":"Shaw","full_name":"Shaw, J"}],"volume":7811,"user_id":"15504","_id":"46390","publisher":"Springer","page":"443–458","status":"public","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","date_created":"2023-08-04T15:43:38Z","abstract":[{"lang":"eng","text":"In some technical applications like multiobjective online control an evenly spaced approximation of the Pareto front is desired. Since standard evolutionary multiobjective optimization (EMO) algorithms have not been designed for that kind of approximation we propose an archive-based plug-in method that builds an evenly spaced approximation using averaged Hausdorff measure between archive and reference front. In case of three objectives this reference font is constructed from a triangulated approximation of the Pareto front from a previous experiment. The plug-in can be deployed in online or offline mode for any kind of EMO algorithm."}],"publication":"Evolutionary Multi-Criterion Optimization — 7$^th$ International Conference, EMO 2013, Sheffield, UK, Proceedings","doi":"https://doi.org/10.1007/978-3-642-37140-0_34","series_title":"Lecture Notes in Computer Science","language":[{"iso":"eng"}],"intvolume":"      7811","date_updated":"2023-10-16T13:46:35Z","author":[{"last_name":"Rudolph","first_name":"G","full_name":"Rudolph, G"},{"full_name":"Trautmann, Heike","first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","id":"100740"},{"full_name":"Sengupta, S","last_name":"Sengupta","first_name":"S"},{"full_name":"Schütze, O","last_name":"Schütze","first_name":"O"}],"title":"Evenly Spaced Pareto Front Approximations for Tricriteria Problems Based on Triangulation","year":"2013"},{"publication":"Proceeding of the Fifteenth Annual Conference Companion on Genetic and Evolutionary Computation Conference Companion","citation":{"mla":"Sosa-Hernandez, VA, et al. “Directed Search Method for Indicator-Based Multi-Objective Evolutionary Algorithms.” <i>Proceeding of the Fifteenth Annual Conference Companion on Genetic and Evolutionary Computation Conference Companion</i>, ACM, 2013, pp. 1699–1702, doi:<a href=\"https://doi.org/10.1145/2464576.2482756\">10.1145/2464576.2482756</a>.","ama":"Sosa-Hernandez V, Schütze O, Rudoph G, Trautmann H. Directed Search Method for Indicator-based Multi-objective Evolutionary Algorithms. In: <i>Proceeding of the Fifteenth Annual Conference Companion on Genetic and Evolutionary Computation Conference Companion</i>. GECCO ’13 Companion. ACM; 2013:1699–1702. doi:<a href=\"https://doi.org/10.1145/2464576.2482756\">10.1145/2464576.2482756</a>","bibtex":"@inproceedings{Sosa-Hernandez_Schütze_Rudoph_Trautmann_2013, place={New York, NY, USA}, series={GECCO ’13 Companion}, title={Directed Search Method for Indicator-based Multi-objective Evolutionary Algorithms}, DOI={<a href=\"https://doi.org/10.1145/2464576.2482756\">10.1145/2464576.2482756</a>}, booktitle={Proceeding of the Fifteenth Annual Conference Companion on Genetic and Evolutionary Computation Conference Companion}, publisher={ACM}, author={Sosa-Hernandez, VA and Schütze, O and Rudoph, G and Trautmann, Heike}, year={2013}, pages={1699–1702}, collection={GECCO ’13 Companion} }","apa":"Sosa-Hernandez, V., Schütze, O., Rudoph, G., &#38; Trautmann, H. (2013). Directed Search Method for Indicator-based Multi-objective Evolutionary Algorithms. <i>Proceeding of the Fifteenth Annual Conference Companion on Genetic and Evolutionary Computation Conference Companion</i>, 1699–1702. <a href=\"https://doi.org/10.1145/2464576.2482756\">https://doi.org/10.1145/2464576.2482756</a>","ieee":"V. Sosa-Hernandez, O. Schütze, G. Rudoph, and H. Trautmann, “Directed Search Method for Indicator-based Multi-objective Evolutionary Algorithms,” in <i>Proceeding of the Fifteenth Annual Conference Companion on Genetic and Evolutionary Computation Conference Companion</i>, 2013, pp. 1699–1702, doi: <a href=\"https://doi.org/10.1145/2464576.2482756\">10.1145/2464576.2482756</a>.","chicago":"Sosa-Hernandez, VA, O Schütze, G Rudoph, and Heike Trautmann. “Directed Search Method for Indicator-Based Multi-Objective Evolutionary Algorithms.” In <i>Proceeding of the Fifteenth Annual Conference Companion on Genetic and Evolutionary Computation Conference Companion</i>, 1699–1702. GECCO ’13 Companion. New York, NY, USA: ACM, 2013. <a href=\"https://doi.org/10.1145/2464576.2482756\">https://doi.org/10.1145/2464576.2482756</a>.","short":"V. Sosa-Hernandez, O. Schütze, G. Rudoph, H. Trautmann, in: Proceeding of the Fifteenth Annual Conference Companion on Genetic and Evolutionary Computation Conference Companion, ACM, New York, NY, USA, 2013, pp. 1699–1702."},"abstract":[{"text":"Indicator based evolutionary algorithms have caught the interest of many researchers for the treatment of multi-objective optimization problems in the recent past since they deliver the desired approximation of the solution set and due to a usually better performance compared to dominance based algorithms. Nevertheless, these methods still suffer the drawback that many function evaluations are required to obtain a suitable representation of the solution set. The aim of this study is to present the Directed Search (DS) Method as local searcher within global indicator based optimization algorithms. For this, we will present the DS in the context of hypervolume maximization leading to both a new local search algorithm and a new memetic algorithm. Further, we will present first attempts to adapt the DS to a class of parameter dependent problems.","lang":"eng"}],"place":"New York, NY, USA","date_created":"2023-08-04T15:45:26Z","type":"conference","department":[{"_id":"34"},{"_id":"819"}],"status":"public","title":"Directed Search Method for Indicator-based Multi-objective Evolutionary Algorithms","year":"2013","author":[{"full_name":"Sosa-Hernandez, VA","first_name":"VA","last_name":"Sosa-Hernandez"},{"first_name":"O","last_name":"Schütze","full_name":"Schütze, O"},{"first_name":"G","last_name":"Rudoph","full_name":"Rudoph, G"},{"id":"100740","full_name":"Trautmann, Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","first_name":"Heike"}],"date_updated":"2023-10-16T13:46:54Z","page":"1699–1702","language":[{"iso":"eng"}],"_id":"46391","series_title":"GECCO ’13 Companion","publisher":"ACM","doi":"10.1145/2464576.2482756","user_id":"15504"},{"doi":"https://doi.org/10.1109/CEC.2013.6557960","user_id":"15504","page":"3190–3197","_id":"46387","language":[{"iso":"eng"}],"date_updated":"2023-10-16T13:45:34Z","year":"2013","title":"Evenly spaced Pareto fronts of quad-objective problems using PSA partitioning technique","status":"public","author":[{"first_name":"C","last_name":"Dominguez-Medina","full_name":"Dominguez-Medina, C"},{"full_name":"Rudolph, G","last_name":"Rudolph","first_name":"G"},{"full_name":"Schütze, O","first_name":"O","last_name":"Schütze"},{"id":"100740","full_name":"Trautmann, Heike","last_name":"Trautmann","orcid":"0000-0002-9788-8282","first_name":"Heike"}],"type":"conference","department":[{"_id":"34"},{"_id":"819"}],"place":"Cancun, Mexico","date_created":"2023-08-04T15:40:15Z","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"}],"publication":"Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC)","citation":{"apa":"Dominguez-Medina, C., Rudolph, G., Schütze, O., &#38; Trautmann, H. (2013). Evenly spaced Pareto fronts of quad-objective problems using PSA partitioning technique. <i>Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC)</i>, 3190–3197. <a href=\"https://doi.org/10.1109/CEC.2013.6557960\">https://doi.org/10.1109/CEC.2013.6557960</a>","ieee":"C. Dominguez-Medina, G. Rudolph, O. Schütze, and H. Trautmann, “Evenly spaced Pareto fronts of quad-objective problems using PSA partitioning technique,” in <i>Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC)</i>, 2013, pp. 3190–3197, doi: <a href=\"https://doi.org/10.1109/CEC.2013.6557960\">https://doi.org/10.1109/CEC.2013.6557960</a>.","chicago":"Dominguez-Medina, C, G Rudolph, O Schütze, and Heike Trautmann. “Evenly Spaced Pareto Fronts of Quad-Objective Problems Using PSA Partitioning Technique.” In <i>Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC)</i>, 3190–3197. Cancun, Mexico, 2013. <a href=\"https://doi.org/10.1109/CEC.2013.6557960\">https://doi.org/10.1109/CEC.2013.6557960</a>.","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.” <i>Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC)</i>, 2013, pp. 3190–3197, doi:<a href=\"https://doi.org/10.1109/CEC.2013.6557960\">https://doi.org/10.1109/CEC.2013.6557960</a>.","ama":"Dominguez-Medina C, Rudolph G, Schütze O, Trautmann H. Evenly spaced Pareto fronts of quad-objective problems using PSA partitioning technique. In: <i>Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC)</i>. ; 2013:3190–3197. doi:<a href=\"https://doi.org/10.1109/CEC.2013.6557960\">https://doi.org/10.1109/CEC.2013.6557960</a>","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={<a href=\"https://doi.org/10.1109/CEC.2013.6557960\">https://doi.org/10.1109/CEC.2013.6557960</a>}, 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} }"}},{"department":[{"_id":"34"},{"_id":"819"}],"type":"conference","date_created":"2023-08-04T15:42:58Z","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"}],"citation":{"ama":"Preuss M, Kozakowski D, Hagelbäck J, Trautmann H. Reactive strategy choice in StarCraft by means of Fuzzy Control. In: <i>2013 IEEE Conference on Computational Inteligence in Games (CIG)</i>. ; 2013:1-8. doi:<a href=\"https://doi.org/10.1109/CIG.2013.6633627\">10.1109/CIG.2013.6633627</a>","bibtex":"@inproceedings{Preuss_Kozakowski_Hagelbäck_Trautmann_2013, title={Reactive strategy choice in StarCraft by means of Fuzzy Control}, DOI={<a href=\"https://doi.org/10.1109/CIG.2013.6633627\">10.1109/CIG.2013.6633627</a>}, 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.” <i>2013 IEEE Conference on Computational Inteligence in Games (CIG)</i>, 2013, pp. 1–8, doi:<a href=\"https://doi.org/10.1109/CIG.2013.6633627\">10.1109/CIG.2013.6633627</a>.","chicago":"Preuss, Mike, Daniel Kozakowski, Johan Hagelbäck, and Heike Trautmann. “Reactive Strategy Choice in StarCraft by Means of Fuzzy Control.” In <i>2013 IEEE Conference on Computational Inteligence in Games (CIG)</i>, 1–8, 2013. <a href=\"https://doi.org/10.1109/CIG.2013.6633627\">https://doi.org/10.1109/CIG.2013.6633627</a>.","short":"M. Preuss, D. Kozakowski, J. Hagelbäck, H. Trautmann, in: 2013 IEEE Conference on Computational Inteligence in Games (CIG), 2013, pp. 1–8.","apa":"Preuss, M., Kozakowski, D., Hagelbäck, J., &#38; Trautmann, H. (2013). Reactive strategy choice in StarCraft by means of Fuzzy Control. <i>2013 IEEE Conference on Computational Inteligence in Games (CIG)</i>, 1–8. <a href=\"https://doi.org/10.1109/CIG.2013.6633627\">https://doi.org/10.1109/CIG.2013.6633627</a>","ieee":"M. Preuss, D. Kozakowski, J. Hagelbäck, and H. Trautmann, “Reactive strategy choice in StarCraft by means of Fuzzy Control,” in <i>2013 IEEE Conference on Computational Inteligence in Games (CIG)</i>, 2013, pp. 1–8, doi: <a href=\"https://doi.org/10.1109/CIG.2013.6633627\">10.1109/CIG.2013.6633627</a>."},"publication":"2013 IEEE Conference on Computational Inteligence in Games (CIG)","user_id":"15504","doi":"10.1109/CIG.2013.6633627","_id":"46389","language":[{"iso":"eng"}],"page":"1-8","date_updated":"2023-10-16T13:46:13Z","author":[{"first_name":"Mike","last_name":"Preuss","full_name":"Preuss, Mike"},{"full_name":"Kozakowski, Daniel","first_name":"Daniel","last_name":"Kozakowski"},{"full_name":"Hagelbäck, Johan","first_name":"Johan","last_name":"Hagelbäck"},{"full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike","orcid":"0000-0002-9788-8282","id":"100740"}],"title":"Reactive strategy choice in StarCraft by means of Fuzzy Control","status":"public","year":"2013"},{"intvolume":"        20","date_updated":"2023-10-16T13:48:31Z","author":[{"first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","full_name":"Trautmann, Heike","id":"100740"},{"first_name":"T","last_name":"Wagner","full_name":"Wagner, T"},{"first_name":"D","last_name":"Biermann","full_name":"Biermann, D"},{"full_name":"Weihs, C","first_name":"C","last_name":"Weihs"}],"year":"2013","title":"Indicator-based Selection in Evolutionary Multiobjective Optimization Algorithms Based On the Desirability Index","status":"public","volume":20,"user_id":"15504","doi":"https://doi.org/10.1002/mcda.1503","_id":"46395","language":[{"iso":"eng"}],"page":"319–337","abstract":[{"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.","lang":"eng"}],"citation":{"mla":"Trautmann, Heike, et al. “Indicator-Based Selection in Evolutionary Multiobjective Optimization Algorithms Based On the Desirability Index.” <i>Journal of Multi-Criteria Decision Analysis</i>, vol. 20, no. 5–6, 2013, pp. 319–337, doi:<a href=\"https://doi.org/10.1002/mcda.1503\">https://doi.org/10.1002/mcda.1503</a>.","ama":"Trautmann H, Wagner T, Biermann D, Weihs C. Indicator-based Selection in Evolutionary Multiobjective Optimization Algorithms Based On the Desirability Index. <i>Journal of Multi-Criteria Decision Analysis</i>. 2013;20(5-6):319–337. doi:<a href=\"https://doi.org/10.1002/mcda.1503\">https://doi.org/10.1002/mcda.1503</a>","bibtex":"@article{Trautmann_Wagner_Biermann_Weihs_2013, title={Indicator-based Selection in Evolutionary Multiobjective Optimization Algorithms Based On the Desirability Index}, volume={20}, DOI={<a href=\"https://doi.org/10.1002/mcda.1503\">https://doi.org/10.1002/mcda.1503</a>}, 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., &#38; Weihs, C. (2013). Indicator-based Selection in Evolutionary Multiobjective Optimization Algorithms Based On the Desirability Index. <i>Journal of Multi-Criteria Decision Analysis</i>, <i>20</i>(5–6), 319–337. <a href=\"https://doi.org/10.1002/mcda.1503\">https://doi.org/10.1002/mcda.1503</a>","ieee":"H. Trautmann, T. Wagner, D. Biermann, and C. Weihs, “Indicator-based Selection in Evolutionary Multiobjective Optimization Algorithms Based On the Desirability Index,” <i>Journal of Multi-Criteria Decision Analysis</i>, vol. 20, no. 5–6, pp. 319–337, 2013, doi: <a href=\"https://doi.org/10.1002/mcda.1503\">https://doi.org/10.1002/mcda.1503</a>.","chicago":"Trautmann, Heike, T Wagner, D Biermann, and C Weihs. “Indicator-Based Selection in Evolutionary Multiobjective Optimization Algorithms Based On the Desirability Index.” <i>Journal of Multi-Criteria Decision Analysis</i> 20, no. 5–6 (2013): 319–337. <a href=\"https://doi.org/10.1002/mcda.1503\">https://doi.org/10.1002/mcda.1503</a>.","short":"H. Trautmann, T. Wagner, D. Biermann, C. Weihs, Journal of Multi-Criteria Decision Analysis 20 (2013) 319–337."},"issue":"5-6","publication":"Journal of Multi-Criteria Decision Analysis","department":[{"_id":"34"},{"_id":"819"}],"type":"journal_article","date_created":"2023-08-04T15:50:03Z"},{"date_updated":"2023-10-16T13:47:58Z","title":"Preference Articulation by Means of the R2 Indicator","year":"2013","status":"public","author":[{"first_name":"Tobias","last_name":"Wagner","full_name":"Wagner, Tobias"},{"id":"100740","full_name":"Trautmann, Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann","first_name":"Heike"},{"full_name":"Brockhoff, Dimo","last_name":"Brockhoff","first_name":"Dimo"}],"publication_identifier":{"isbn":["978-3-642-37140-0"]},"user_id":"15504","editor":[{"first_name":"Robin C.","last_name":"Purshouse","full_name":"Purshouse, Robin C."},{"last_name":"Fleming","first_name":"Peter J.","full_name":"Fleming, Peter J."},{"full_name":"Fonseca, Carlos M.","last_name":"Fonseca","first_name":"Carlos M."},{"full_name":"Greco, Salvatore","first_name":"Salvatore","last_name":"Greco"},{"full_name":"Shaw, Jane","last_name":"Shaw","first_name":"Jane"}],"page":"81–95","publisher":"Springer Berlin Heidelberg","_id":"46393","language":[{"iso":"eng"}],"abstract":[{"lang":"eng","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."}],"publication":"Evolutionary Multi-Criterion Optimization","citation":{"mla":"Wagner, Tobias, et al. “Preference Articulation by Means of the R2 Indicator.” <i>Evolutionary Multi-Criterion Optimization</i>, 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} }","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. <i>Evolutionary Multi-Criterion Optimization</i>. Springer Berlin Heidelberg; 2013:81–95.","ieee":"T. Wagner, H. Trautmann, and D. Brockhoff, “Preference Articulation by Means of the R2 Indicator,” in <i>Evolutionary Multi-Criterion Optimization</i>, 2013, pp. 81–95.","apa":"Wagner, T., Trautmann, H., &#38; Brockhoff, D. (2013). Preference Articulation by Means of the R2 Indicator. In R. C. Purshouse, P. J. Fleming, C. M. Fonseca, S. Greco, &#38; J. Shaw (Eds.), <i>Evolutionary Multi-Criterion Optimization</i> (pp. 81–95). Springer Berlin Heidelberg.","chicago":"Wagner, Tobias, Heike Trautmann, and Dimo Brockhoff. “Preference Articulation by Means of the R2 Indicator.” In <i>Evolutionary Multi-Criterion Optimization</i>, edited by Robin C. Purshouse, Peter J. Fleming, Carlos M. Fonseca, Salvatore Greco, and Jane Shaw, 81–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.","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."},"type":"conference","department":[{"_id":"34"},{"_id":"819"}],"place":"Berlin, Heidelberg","date_created":"2023-08-04T15:47:49Z"},{"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"}],"citation":{"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.","chicago":"Trautmann, Heike, Tobias Wagner, and Dimo Brockhoff. “R2-EMOA: Focused Multiobjective Search Using R2-Indicator-Based Selection.” In <i>Learning and Intelligent Optimization</i>, edited by Giuseppe Nicosia and Panos Pardalos, 70–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.","apa":"Trautmann, H., Wagner, T., &#38; Brockhoff, D. (2013). R2-EMOA: Focused Multiobjective Search Using R2-Indicator-Based Selection. In G. Nicosia &#38; P. Pardalos (Eds.), <i>Learning and Intelligent Optimization</i> (pp. 70–74). Springer Berlin Heidelberg.","ieee":"H. Trautmann, T. Wagner, and D. Brockhoff, “R2-EMOA: Focused Multiobjective Search Using R2-Indicator-Based Selection,” in <i>Learning and Intelligent Optimization</i>, 2013, pp. 70–74.","ama":"Trautmann H, Wagner T, Brockhoff D. R2-EMOA: Focused Multiobjective Search Using R2-Indicator-Based Selection. In: Nicosia G, Pardalos P, eds. <i>Learning and Intelligent Optimization</i>. Springer Berlin Heidelberg; 2013:70–74.","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.” <i>Learning and Intelligent Optimization</i>, edited by Giuseppe Nicosia and Panos Pardalos, Springer Berlin Heidelberg, 2013, pp. 70–74."},"publication":"Learning and Intelligent Optimization","department":[{"_id":"34"},{"_id":"819"}],"type":"conference","date_created":"2023-08-04T15:47:00Z","place":"Berlin, Heidelberg","date_updated":"2023-10-16T13:47:41Z","author":[{"id":"100740","full_name":"Trautmann, Heike","first_name":"Heike","orcid":"0000-0002-9788-8282","last_name":"Trautmann"},{"full_name":"Wagner, Tobias","first_name":"Tobias","last_name":"Wagner"},{"last_name":"Brockhoff","first_name":"Dimo","full_name":"Brockhoff, Dimo"}],"publication_identifier":{"isbn":["978-3-642-44973-4"]},"year":"2013","title":"R2-EMOA: Focused Multiobjective Search Using R2-Indicator-Based Selection","status":"public","editor":[{"full_name":"Nicosia, Giuseppe","first_name":"Giuseppe","last_name":"Nicosia"},{"last_name":"Pardalos","first_name":"Panos","full_name":"Pardalos, Panos"}],"user_id":"15504","_id":"46392","publisher":"Springer Berlin Heidelberg","language":[{"iso":"eng"}],"page":"70–74"},{"language":[{"iso":"eng"}],"doi":"10.1007/s10472-013-9341-2","title":"A Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling Salesperson Problem","year":"2013","author":[{"full_name":"Mersmann, Olaf","last_name":"Mersmann","first_name":"Olaf"},{"full_name":"Bischl, Bernd","last_name":"Bischl","first_name":"Bernd"},{"last_name":"Trautmann","first_name":"Heike","full_name":"Trautmann, Heike"},{"last_name":"Wagner","first_name":"Markus","full_name":"Wagner, Markus"},{"full_name":"Bossek, Jakob","orcid":"0000-0002-4121-4668","last_name":"Bossek","first_name":"Jakob","id":"102979"},{"first_name":"Frank","last_name":"Neumann","full_name":"Neumann, Frank"}],"publication_identifier":{"issn":["1012-2443"]},"date_updated":"2023-12-13T10:50:41Z","intvolume":"        69","date_created":"2023-11-14T15:58:59Z","keyword":["2-opt","90B06","Classification","Feature selection","MARS","TSP"],"type":"journal_article","department":[{"_id":"819"}],"issue":"2","publication":"Annals of Mathematics and Artificial Intelligence","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."}],"page":"151–182","_id":"48889","user_id":"102979","volume":69,"status":"public","citation":{"mla":"Mersmann, Olaf, et al. “A Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling Salesperson Problem.” <i>Annals of Mathematics and Artificial Intelligence</i>, vol. 69, no. 2, 2013, pp. 151–182, doi:<a href=\"https://doi.org/10.1007/s10472-013-9341-2\">10.1007/s10472-013-9341-2</a>.","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. <i>Annals of Mathematics and Artificial Intelligence</i>. 2013;69(2):151–182. doi:<a href=\"https://doi.org/10.1007/s10472-013-9341-2\">10.1007/s10472-013-9341-2</a>","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={<a href=\"https://doi.org/10.1007/s10472-013-9341-2\">10.1007/s10472-013-9341-2</a>}, 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} }","apa":"Mersmann, O., Bischl, B., Trautmann, H., Wagner, M., Bossek, J., &#38; Neumann, F. (2013). A Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling Salesperson Problem. <i>Annals of Mathematics and Artificial Intelligence</i>, <i>69</i>(2), 151–182. <a href=\"https://doi.org/10.1007/s10472-013-9341-2\">https://doi.org/10.1007/s10472-013-9341-2</a>","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,” <i>Annals of Mathematics and Artificial Intelligence</i>, vol. 69, no. 2, pp. 151–182, 2013, doi: <a href=\"https://doi.org/10.1007/s10472-013-9341-2\">10.1007/s10472-013-9341-2</a>.","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.” <i>Annals of Mathematics and Artificial Intelligence</i> 69, no. 2 (2013): 151–182. <a href=\"https://doi.org/10.1007/s10472-013-9341-2\">https://doi.org/10.1007/s10472-013-9341-2</a>.","short":"O. Mersmann, B. Bischl, H. Trautmann, M. Wagner, J. Bossek, F. Neumann, Annals of Mathematics and Artificial Intelligence 69 (2013) 151–182."}},{"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"}],"citation":{"apa":"Mersmann, O., Bischl, B., Trautmann, H., Wagner, M., Bossek, J., &#38; Neumann, F. (2013). A Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling Salesman Problem. <i>Annals of Mathematics and Artificial Intelligence</i>, <i>69</i>, 151–182.","mla":"Mersmann, O., et al. “A Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling Salesman Problem.” <i>Annals of Mathematics and Artificial Intelligence</i>, vol. 69, 2013, pp. 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 Salesman Problem,” <i>Annals of Mathematics and Artificial Intelligence</i>, vol. 69, pp. 151–182, 2013.","short":"O. Mersmann, B. Bischl, H. Trautmann, M. Wagner, J. Bossek, F. Neumann, Annals of Mathematics and Artificial Intelligence 69 (2013) 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. <i>Annals of Mathematics and Artificial Intelligence</i>. 2013;69:151–182.","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.” <i>Annals of Mathematics and Artificial Intelligence</i> 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} }"},"publication":"Annals of Mathematics and Artificial Intelligence","department":[{"_id":"34"},{"_id":"819"}],"type":"journal_article","date_created":"2023-08-04T15:48:57Z","intvolume":"        69","date_updated":"2024-06-10T11:57:43Z","author":[{"last_name":"Mersmann","first_name":"O","full_name":"Mersmann, O"},{"last_name":"Bischl","first_name":"B","full_name":"Bischl, B"},{"id":"100740","full_name":"Trautmann, Heike","last_name":"Trautmann","first_name":"Heike","orcid":"0000-0002-9788-8282"},{"full_name":"Wagner, M","last_name":"Wagner","first_name":"M"},{"id":"102979","last_name":"Bossek","first_name":"Jakob","orcid":"0000-0002-4121-4668","full_name":"Bossek, Jakob"},{"first_name":"F","last_name":"Neumann","full_name":"Neumann, F"}],"title":"A Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling Salesman Problem","year":"2013","status":"public","volume":69,"user_id":"15504","language":[{"iso":"eng"}],"_id":"46394","page":"151–182"}]