Fuzzy approaches to acquiring experimental knowledge in cultural algorithms

Self-adaptation has been widely used in evolutionary computational models. Angeline (1995) defines three distinct adaptive levels, which are: population, individual, and component level. Cultural algorithms have been shown to provide a framework in which to model self-adaptation at all of these levels. Te authors examine the role that different forms of knowledge can play in the self-adaptation process at the population level for evolution-based function optimizers. An acceptance function using a fuzzy inference engine is employed to select acceptable individuals for forming the generalized knowledge in the belief space. Evolutionary programming is used to implement the population space. The results suggest that the use of a cultural framework can produce substantial performance improvements in execution time and accuracy for a given set of function minimization problems.