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.
|Number of pages
|International Journal of Neural Systems
|Published - Feb 2008
|7h International Conference on Intelligent Data Engineering and Automated Learning - Burgos, Spain
Duration: 20 Sept 2006 → 23 Sept 2006
Bibliographical noteThis research was supported by the Vietnamese Overseas Scholarship Program, coded 322, and partly by the School of Computer Science, University of Birmingham. The authors are grateful to colleagues from CERCIA and other institutions for their fruitful discussions. The authors are also grateful to the anonymous reviewers for their valuable suggestions and comments.
- Continuous optimization
- Cultural Algorithms
- Global optimization
- Iterated Local Search