{"publication_identifier":{"isbn":["978-3-319-49129-5"]},"author":[{"full_name":"Bossek, Jakob","last_name":"Bossek","id":"102979","orcid":"0000-0002-4121-4668","first_name":"Jakob"},{"first_name":"Heike","last_name":"Trautmann","full_name":"Trautmann, Heike"}],"publisher":"Springer-Verlag","publication":"Proceedings of the XV International Conference of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037","citation":{"apa":"Bossek, J., & Trautmann, H. (2016). Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference. Proceedings of the XV International Conference of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037, 3–12. https://doi.org/10.1007/978-3-319-49130-1_1","ieee":"J. Bossek and H. Trautmann, “Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference,” in Proceedings of the XV International Conference of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037, 2016, pp. 3–12, doi: 10.1007/978-3-319-49130-1_1.","chicago":"Bossek, Jakob, and Heike Trautmann. “Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference.” In Proceedings of the XV International Conference of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037, 3–12. AI*IA 2016. Berlin, Heidelberg: Springer-Verlag, 2016. https://doi.org/10.1007/978-3-319-49130-1_1.","short":"J. Bossek, H. Trautmann, in: Proceedings of the XV International Conference of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037, Springer-Verlag, Berlin, Heidelberg, 2016, pp. 3–12.","ama":"Bossek J, Trautmann H. Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference. In: Proceedings of the XV International Conference of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037. AI*IA 2016. Springer-Verlag; 2016:3–12. doi:10.1007/978-3-319-49130-1_1","mla":"Bossek, Jakob, and Heike Trautmann. “Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference.” Proceedings of the XV International Conference of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037, Springer-Verlag, 2016, pp. 3–12, doi:10.1007/978-3-319-49130-1_1.","bibtex":"@inproceedings{Bossek_Trautmann_2016, place={Berlin, Heidelberg}, series={AI*IA 2016}, title={Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference}, DOI={10.1007/978-3-319-49130-1_1}, booktitle={Proceedings of the XV International Conference of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037}, publisher={Springer-Verlag}, author={Bossek, Jakob and Trautmann, Heike}, year={2016}, pages={3–12}, collection={AI*IA 2016} }"},"place":"Berlin, Heidelberg","doi":"10.1007/978-3-319-49130-1_1","series_title":"AI*IA 2016","extern":"1","year":"2016","title":"Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference","publication_status":"published","keyword":["Combinatorial optimization","Instance hardness","Metaheuristics","Transportation","TSP"],"user_id":"102979","language":[{"iso":"eng"}],"type":"conference","abstract":[{"text":"State of the Art inexact solvers of the NP-hard Traveling Salesperson Problem TSP are known to mostly yield high-quality solutions in reasonable computation times. With the purpose of understanding different levels of instance difficulties, instances for the current State of the Art heuristic TSP solvers LKH+restart and EAX+restart are presented which are evolved using a sophisticated evolutionary algorithm. More specifically, the performance differences of the respective solvers are maximized resulting in instances which are easier to solve for one solver and much more difficult for the other. Focusing on both optimization directions, instance features are identified which characterize both types of instances and increase the understanding of solver performance differences.","lang":"eng"}],"page":"3–12","_id":"48874","status":"public","date_updated":"2023-12-13T10:47:11Z","date_created":"2023-11-14T15:58:57Z"}