{"status":"public","_id":"46398","doi":"https://doi.org/10.1007/978-3-642-34413-8_9","date_created":"2023-08-04T15:53:33Z","user_id":"15504","publication_identifier":{"isbn":["978-3-642-34413-8"]},"author":[{"full_name":"Mersmann, Olaf","last_name":"Mersmann","first_name":"Olaf"},{"first_name":"Bernd","last_name":"Bischl","full_name":"Bischl, Bernd"},{"full_name":"Bossek, Jakob","last_name":"Bossek","first_name":"Jakob"},{"orcid":"0000-0002-9788-8282","id":"100740","first_name":"Heike","last_name":"Trautmann","full_name":"Trautmann, Heike"},{"last_name":"Wagner","full_name":"Wagner, Markus","first_name":"Markus"},{"last_name":"Neumann","full_name":"Neumann, Frank","first_name":"Frank"}],"year":"2012","citation":{"chicago":"Mersmann, Olaf, Bernd Bischl, Jakob Bossek, Heike Trautmann, Markus Wagner, and Frank Neumann. “Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness.” In Learning and Intelligent Optimization, edited by Youssef Hamadi and Marc Schoenauer, 115–129. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. https://doi.org/10.1007/978-3-642-34413-8_9.","mla":"Mersmann, Olaf, et al. “Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness.” Learning and Intelligent Optimization, edited by Youssef Hamadi and Marc Schoenauer, Springer Berlin Heidelberg, 2012, pp. 115–129, doi:https://doi.org/10.1007/978-3-642-34413-8_9.","apa":"Mersmann, O., Bischl, B., Bossek, J., Trautmann, H., Wagner, M., & Neumann, F. (2012). Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness. In Y. Hamadi & M. Schoenauer (Eds.), Learning and Intelligent Optimization (pp. 115–129). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-34413-8_9","short":"O. Mersmann, B. Bischl, J. Bossek, H. Trautmann, M. Wagner, F. Neumann, in: Y. Hamadi, M. Schoenauer (Eds.), Learning and Intelligent Optimization, Springer Berlin Heidelberg, Berlin, Heidelberg, 2012, pp. 115–129.","ama":"Mersmann O, Bischl B, Bossek J, Trautmann H, Wagner M, Neumann F. Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness. In: Hamadi Y, Schoenauer M, eds. Learning and Intelligent Optimization. Springer Berlin Heidelberg; 2012:115–129. doi:https://doi.org/10.1007/978-3-642-34413-8_9","ieee":"O. Mersmann, B. Bischl, J. Bossek, H. Trautmann, M. Wagner, and F. Neumann, “Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness,” in Learning and Intelligent Optimization, 2012, pp. 115–129, doi: https://doi.org/10.1007/978-3-642-34413-8_9.","bibtex":"@inproceedings{Mersmann_Bischl_Bossek_Trautmann_Wagner_Neumann_2012, place={Berlin, Heidelberg}, title={Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness}, DOI={https://doi.org/10.1007/978-3-642-34413-8_9}, booktitle={Learning and Intelligent Optimization}, publisher={Springer Berlin Heidelberg}, author={Mersmann, Olaf and Bischl, Bernd and Bossek, Jakob and Trautmann, Heike and Wagner, Markus and Neumann, Frank}, editor={Hamadi, Youssef and Schoenauer, Marc}, year={2012}, pages={115–129} }"},"type":"conference","publisher":"Springer Berlin Heidelberg","department":[{"_id":"34"},{"_id":"819"}],"page":"115–129","place":"Berlin, Heidelberg","language":[{"iso":"eng"}],"date_updated":"2023-10-16T13:53:41Z","editor":[{"full_name":"Hamadi, Youssef","last_name":"Hamadi","first_name":"Youssef"},{"first_name":"Marc","last_name":"Schoenauer","full_name":"Schoenauer, Marc"}],"title":"Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness","abstract":[{"lang":"eng","text":"With this paper we contribute to the understanding of the success of 2-opt based local search algorithms for solving the traveling salesman problem (TSP). Although 2-opt is widely used in practice, it is hard to understand its success from a theoretical perspective. We take a statistical approach and examine the features of TSP instances that make the problem either hard or easy to solve. As a measure of problem difficulty for 2-opt we use the approximation ratio that it achieves on a given instance. Our investigations point out important features that make TSP instances hard or easy to be approximated by 2-opt."}],"publication":"Learning and Intelligent Optimization"}