Details
| Original language | English |
|---|---|
| Title of host publication | 2017 IEEE Congress on Evolutionary Computation (CEC) |
| Subtitle of host publication | Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1704-1711 |
| Number of pages | 8 |
| ISBN (electronic) | 9781509046010 |
| Publication status | Published - 7 Jul 2017 |
| Externally published | Yes |
| Event | 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Donostia-San Sebastian, Spain Duration: 5 Jun 2017 → 8 Jun 2017 |
Abstract
For the minimum vertex cover problem, a wide range of solvers has been proposed over the years. Most classical exact approaches are encountering run time issues on massive graphs that are considered nowadays. A straightforward alternative approach is then to use heuristics, which make assumptions about the structure of the studied graphs. These assumptions are typically hard-coded and are hoped to work well for a wide range of networks - which is in conflict with the nature of broad benchmark sets. With this article, we contribute in two ways. First, we identify a component in an existing solver that influences its performance depending on the class of graphs, and we then customize instances of this solver for different classes of graphs. Second, we create the first algorithm portfolio for the minimum vertex cover to further improve the performance of a single integrated approach to the minimum vertex cover problem.
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Computer Networks and Communications
- Computer Science(all)
- Computer Science Applications
- Computer Science(all)
- Signal Processing
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
2017 IEEE Congress on Evolutionary Computation (CEC): Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1704-1711.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Improving local search in a minimum vertex cover solver for classes of networks
AU - Wagner, Markus
AU - Friedrich, Tobias
AU - Lindauer, Marius
N1 - Funding information: M. Lindauer was supported by the DFG (German Research Foundation) under Emmy Noether grant HU 1900/2-1. M. Wagner was supported by the Australian Research Council (DE160100850).
PY - 2017/7/7
Y1 - 2017/7/7
N2 - For the minimum vertex cover problem, a wide range of solvers has been proposed over the years. Most classical exact approaches are encountering run time issues on massive graphs that are considered nowadays. A straightforward alternative approach is then to use heuristics, which make assumptions about the structure of the studied graphs. These assumptions are typically hard-coded and are hoped to work well for a wide range of networks - which is in conflict with the nature of broad benchmark sets. With this article, we contribute in two ways. First, we identify a component in an existing solver that influences its performance depending on the class of graphs, and we then customize instances of this solver for different classes of graphs. Second, we create the first algorithm portfolio for the minimum vertex cover to further improve the performance of a single integrated approach to the minimum vertex cover problem.
AB - For the minimum vertex cover problem, a wide range of solvers has been proposed over the years. Most classical exact approaches are encountering run time issues on massive graphs that are considered nowadays. A straightforward alternative approach is then to use heuristics, which make assumptions about the structure of the studied graphs. These assumptions are typically hard-coded and are hoped to work well for a wide range of networks - which is in conflict with the nature of broad benchmark sets. With this article, we contribute in two ways. First, we identify a component in an existing solver that influences its performance depending on the class of graphs, and we then customize instances of this solver for different classes of graphs. Second, we create the first algorithm portfolio for the minimum vertex cover to further improve the performance of a single integrated approach to the minimum vertex cover problem.
UR - http://www.scopus.com/inward/record.url?scp=85028499133&partnerID=8YFLogxK
U2 - 10.1109/cec.2017.7969507
DO - 10.1109/cec.2017.7969507
M3 - Conference contribution
AN - SCOPUS:85028499133
SP - 1704
EP - 1711
BT - 2017 IEEE Congress on Evolutionary Computation (CEC)
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Congress on Evolutionary Computation, CEC 2017
Y2 - 5 June 2017 through 8 June 2017
ER -