title: An experimental study of hybridizing cultural algorithms and local search.
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.
authors.affiliation: The Centre of Excellence for Research in Computational Intelligence, and Applications, University of Birmingham, Birmingham, B15 2TT, United Kingdom.
authors.forename: Trung Thanh
authors.initials: TT
authors.lastname: Nguyen
authors.forename: Xin
authors.initials: X
authors.lastname: Yao
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003469:Culture; D006801:Humans; D016247:Information Storage and Retrieval; D008962:Models, Theoretical