Knowledge-based self-adaptation in evolutionary programming using cultural algorithms

We investigate knowledge-based self-adaptation in evolutionary programming (EP) using cultural algorithms for 22 function optimization problems. The results suggest that the use of a cultural framework for self-adaptation in EP can produce substantial performance improvements as expressed in terms of CPU time. The nature of these improvements and the type of knowledge that is most effective in producing them will depend on the structure of the problem.

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