Instance-Based Algorithm Selection of Inexact TSP solvers
The Travelling Salesperson Problem (TSP) is arguably the most prominent NP-hard
combinatorial optimisation problem. Given a set of n cities and pairwise distances between those, the objective in the TSP is to find the shortest round-trip or tour through all cities, i.e., a sequence in which every city is visited exactly once, the start and end cities are identical, and the total length of the tour is minimal. The Euclidean TSP has important applications, e.g., in the fabrication of printed circuit boards as well as in transportation and logistics. We aim at constructing an instance-based algorithm selection model in order to improve the current state-of-the-art solver.
Project status |
definitely finished |
Project time |
01.01.2017- 31.12.2018 |
Funding source |
German Academic Exchange Service |
Project number |
57314626 |
Keywords |
Algorithm Selection; TSP; automated algorithm selection; inexact solvers; Information Systems; Statistics; Canada |
9th International Conference on Evolutionary Multi- Criterion Optimization, Münster 19. - 22.03.2017
EMO 2017 is the 9th International Conference on Evolutionary Multi- Criterion Optimization, aiming to continue the success of previous EMO conferences. We will bring together both the EMO and the multiple criteria decision making (MCDM) communities and moreover focus on solving real-world problems in government, business and industry. The classical EMO format will be supplemented by an EMO competition.
Project status |
definitely finished |
Project time |
19.03.2017- 22.03.2017 |
Website |
http://www.emo2017.org |
Funding source |
Participation / conference fees |
Project number |
TR 891/9-1 |
Keywords |
Evolutionary Multiobjective Optimization |