An experimental study of hybridizing cultural algorithms and local search

Trung Thanh NGUYEN, Xin YAO*

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

27 Citations (Scopus)


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 languageEnglish
Pages (from-to)1-17
Number of pages17
JournalInternational Journal of Neural Systems
Issue number1
Publication statusPublished - Feb 2008
Externally publishedYes
Event7h International Conference on Intelligent Data Engineering and Automated Learning - Burgos, Spain
Duration: 20 Sept 200623 Sept 2006

Bibliographical note

This 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
  • Meta-heuristic


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