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, Ischia, Italy, 2016, pp. 48–59.
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Conference Paper
| English
Author
Bossek, J;
Trautmann, HeikeLibreCat 
Editor
Festa, P;
Sellmann, M;
Vanschoren, J
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.
Publishing Year
Proceedings Title
Learning and Intelligent Optimization
forms.conference.field.series_title_volume.label
Lecture Notes in Computer Science
Volume
10079
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. Vol 10079. 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 (Vol. 10079, pp. 48–59). Springer International Publishing. https://doi.org/10.1007/978-3-319-50349-3_4
@inproceedings{Bossek_Trautmann_2016, place={Ischia, Italy}, series={Lecture Notes in Computer Science}, title={Evolving Instances for Maximizing Performance Differences of State-of-The-Art Inexact TSP Solvers}, volume={10079}, DOI={10.1007/978-3-319-50349-3_4}, booktitle={Learning and Intelligent Optimization}, publisher={Springer International Publishing}, author={Bossek, J and Trautmann, Heike}, editor={Festa, P and Sellmann, M and Vanschoren, J}, year={2016}, pages={48–59}, collection={Lecture Notes in Computer Science} }
Bossek, J, and Heike Trautmann. “Evolving Instances for Maximizing Performance Differences of State-of-The-Art Inexact TSP Solvers.” In Learning and Intelligent Optimization, edited by P Festa, M Sellmann, and J Vanschoren, 10079:48–59. Lecture Notes in Computer Science. Ischia, Italy: 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, vol. 10079, pp. 48–59, doi: 10.1007/978-3-319-50349-3_4.
Bossek, J., and Heike Trautmann. “Evolving Instances for Maximizing Performance Differences of State-of-The-Art Inexact TSP Solvers.” Learning and Intelligent Optimization, edited by P Festa et al., vol. 10079, Springer International Publishing, 2016, pp. 48–59, doi:10.1007/978-3-319-50349-3_4.
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