Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference

Bossek J, Trautmann H


Abstract
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.



Publication type
Conference Paper

Peer reviewed
Yes

Publication status
Published

Year
2016

Conference
XVth International Conference of the Italian Association for Artificial Intelligence

Venue
Genova, Italy

Book title
AI*IA 2016 Advances in Artificial Intelligence

Editor
Adorni G, Cagnoni S, Gori M, Maratea M

Start page
3

End page
12

Pages range
3-12

Volume
10037

Title of series
Lecture Notes in Computer Science

Publisher
Springer

Place
Cham

ISBN
978-3-319-49129-5

DOI