{"status":"public","_id":"48888","doi":"10.1007/978-3-642-34413-8_9","date_created":"2023-11-14T15:58:59Z","user_id":"102979","publication_identifier":{"isbn":["978-3-642-34412-1 978-3-642-34413-8"]},"year":"2012","author":[{"full_name":"Mersmann, Olaf","last_name":"Mersmann","first_name":"Olaf"},{"first_name":"Bernd","full_name":"Bischl, Bernd","last_name":"Bischl"},{"orcid":"0000-0002-4121-4668","last_name":"Bossek","full_name":"Bossek, Jakob","id":"102979","first_name":"Jakob"},{"last_name":"Trautmann","full_name":"Trautmann, Heike","first_name":"Heike"},{"first_name":"Markus","full_name":"Wagner, Markus","last_name":"Wagner"},{"last_name":"Neumann","full_name":"Neumann, Frank","first_name":"Frank"}],"citation":{"short":"O. Mersmann, B. Bischl, J. Bossek, H. Trautmann, M. Wagner, F. Neumann, in: Learning and Intelligent Optimization, Springer Berlin Heidelberg, Berlin, Heidelberg, 2012, pp. 115–129.","mla":"Mersmann, Olaf, et al. “Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness.” <i>Learning and Intelligent Optimization</i>, vol. 7219, Springer Berlin Heidelberg, 2012, pp. 115–129, doi:<a href=\"https://doi.org/10.1007/978-3-642-34413-8_9\">10.1007/978-3-642-34413-8_9</a>.","chicago":"Mersmann, Olaf, Bernd Bischl, Jakob Bossek, Heike Trautmann, Markus Wagner, and Frank Neumann. “Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness.” In <i>Learning and Intelligent Optimization</i>, 7219:115–129. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. <a href=\"https://doi.org/10.1007/978-3-642-34413-8_9\">https://doi.org/10.1007/978-3-642-34413-8_9</a>.","ama":"Mersmann O, Bischl B, Bossek J, Trautmann H, Wagner M, Neumann F. Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness. In: <i>Learning and Intelligent Optimization</i>. Vol 7219. Springer Berlin Heidelberg; 2012:115–129. doi:<a href=\"https://doi.org/10.1007/978-3-642-34413-8_9\">10.1007/978-3-642-34413-8_9</a>","bibtex":"@inbook{Mersmann_Bischl_Bossek_Trautmann_Wagner_Neumann_2012, place={Berlin, Heidelberg}, title={Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness}, volume={7219}, DOI={<a href=\"https://doi.org/10.1007/978-3-642-34413-8_9\">10.1007/978-3-642-34413-8_9</a>}, booktitle={Learning and Intelligent Optimization}, publisher={Springer Berlin Heidelberg}, author={Mersmann, Olaf and Bischl, Bernd and Bossek, Jakob and Trautmann, Heike and Wagner, Markus and Neumann, Frank}, year={2012}, pages={115–129} }","apa":"Mersmann, O., Bischl, B., Bossek, J., Trautmann, H., Wagner, M., &#38; Neumann, F. (2012). Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness. In <i>Learning and Intelligent Optimization</i> (Vol. 7219, pp. 115–129). Springer Berlin Heidelberg. <a href=\"https://doi.org/10.1007/978-3-642-34413-8_9\">https://doi.org/10.1007/978-3-642-34413-8_9</a>","ieee":"O. Mersmann, B. Bischl, J. Bossek, H. Trautmann, M. Wagner, and F. Neumann, “Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness,” in <i>Learning and Intelligent Optimization</i>, vol. 7219, Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 115–129."},"type":"book_chapter","publisher":"Springer Berlin Heidelberg","volume":7219,"department":[{"_id":"819"}],"page":"115–129","place":"Berlin, Heidelberg","intvolume":"      7219","language":[{"iso":"eng"}],"date_updated":"2023-12-13T10:49:15Z","title":"Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness","extern":"1","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"}