Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers
J. Bossek, H. Trautmann, in: P. Festa, M. Sellmann, J. Vanschoren (Eds.), Learning and Intelligent Optimization, Springer International Publishing, Cham, 2016, pp. 48–59.
Download
No fulltext has been uploaded.
Conference Paper
| Published
| English
Author
Bossek, JakobLibreCat 
;
Trautmann, Heike
;
Trautmann, HeikeEditor
Festa, Paola;
Sellmann, Meinolf;
Vanschoren, Joaquin
Department
Abstract
Despite the intrinsic hardness of the Traveling Salesperson Problem (TSP) heuristic solvers, e.g., LKH+restart and EAX+restart, are remarkably successful in generating satisfactory or even optimal solutions. However, the reasons for their success are not yet fully understood. Recent approaches take an analytical viewpoint and try to identify instance features, which make an instance hard or easy to solve. We contribute to this area by generating instance sets for couples of TSP algorithms A and B by maximizing/minimizing their performance difference in order to generate instances which are easier to solve for one solver and much harder to solve for the other. This instance set offers the potential to identify key features which allow to distinguish between the problem hardness classes of both algorithms.
Keywords
Publishing Year
Proceedings Title
Learning and Intelligent Optimization
forms.conference.field.series_title_volume.label
Lecture Notes in Computer Science
Page
48–59
ISBN
LibreCat-ID
Cite this
Bossek J, Trautmann H. Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers. In: Festa P, Sellmann M, Vanschoren J, eds. Learning and Intelligent Optimization. Lecture Notes in Computer Science. Springer International Publishing; 2016:48–59. doi:10.1007/978-3-319-50349-3_4
Bossek, J., & Trautmann, H. (2016). Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers. In P. Festa, M. Sellmann, & J. Vanschoren (Eds.), Learning and Intelligent Optimization (pp. 48–59). Springer International Publishing. https://doi.org/10.1007/978-3-319-50349-3_4
@inproceedings{Bossek_Trautmann_2016, place={Cham}, series={Lecture Notes in Computer Science}, title={Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers}, DOI={10.1007/978-3-319-50349-3_4}, booktitle={Learning and Intelligent Optimization}, publisher={Springer International Publishing}, author={Bossek, Jakob and Trautmann, Heike}, editor={Festa, Paola and Sellmann, Meinolf and Vanschoren, Joaquin}, year={2016}, pages={48–59}, collection={Lecture Notes in Computer Science} }
Bossek, Jakob, and Heike Trautmann. “Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers.” In Learning and Intelligent Optimization, edited by Paola Festa, Meinolf Sellmann, and Joaquin Vanschoren, 48–59. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2016. https://doi.org/10.1007/978-3-319-50349-3_4.
J. Bossek and H. Trautmann, “Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers,” in Learning and Intelligent Optimization, 2016, pp. 48–59, doi: 10.1007/978-3-319-50349-3_4.
Bossek, Jakob, and Heike Trautmann. “Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers.” Learning and Intelligent Optimization, edited by Paola Festa et al., Springer International Publishing, 2016, pp. 48–59, doi:10.1007/978-3-319-50349-3_4.
Google Scholar
ISBN Search