Abstract
In this paper the performance of the Cultural Algorithms-Iterated Local Search (CA-ILS), a new continuous optimization algorithm, is empirically studied on multimodal test functions proposed in the Special Session on Real-Parameter Optimization of the 2005 Congress on Evolutionary Computation. It is compared with state-of-the-art methods attending the Session to find out whether the algorithm is effective in solving difficult problems. The test results show that CA-ILS may be a competitive method, at least in the tested problems. The results also reveal the classes of problems where CA-ILS can work well and/or not well. © 2008 World Scientific Publishing Company.
| Original language | English |
|---|---|
| Pages (from-to) | 1-17 |
| Number of pages | 17 |
| Journal | International Journal of Neural Systems |
| Volume | 18 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Feb 2008 |
| Externally published | Yes |
| Event | 7h International Conference on Intelligent Data Engineering and Automated Learning - Burgos, Spain Duration: 20 Sept 2006 → 23 Sept 2006 |
Funding
This research was supported by the Vietnamese Overseas Scholarship Program, coded 322, and partly by the School of Computer Science, University of Birmingham.
Keywords
- Continuous optimization
- Cultural Algorithms
- Global optimization
- Iterated Local Search
- Meta-heuristic