Abstract
Genetic algorithms (GA) can be exploited for optimal graph matching. Graphs represent powerful method of a pattern formal description. Globally optimal graph matching is a NP-complete problem. Pattern distortions and noise increase an optimal search difficulty which could be tackled using GA. This paper describes results of simple GA applied on a graph matching problem. As a conclusion, the suitable GA for an optimal graph `isomorphism' and `monomorphism' is proposed. Used coding resembles the Travelling Salesman Problem (TSP). Consequently, performance of ordering operators has been tested. In contrast to the TSP, the fitness function depends on chromosome value positioning not ordering. It results in differences between optimal GA configuration for graph matching and for TSP.
| Original language | English (US) |
|---|---|
| Pages | 3872-3876 |
| Number of pages | 5 |
| State | Published - 1994 |
| Externally published | Yes |
| Event | Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA Duration: Jun 27 1994 → Jun 29 1994 |
Other
| Other | Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) |
|---|---|
| City | Orlando, FL, USA |
| Period | 6/27/94 → 6/29/94 |
All Science Journal Classification (ASJC) codes
- Software