Resumen de Dynamic multimodal function optimization using genetic algoritms
Many optimization techniques work well for unimodal functions. If applied to multimodal functions, they tend to converge to only one of the many peaks. Optimization of multimodal functions becomes even more difficult if the function parameters change dynamically. Genetic algorithms have been successfully applied by several investigators for static optimization of multimodal functions. This modest success is primarily due to the ability of genetic algorithms to locate more than one peak. In this paper we introduce a combination of selection and replacement operators that is suitable for multimodal function optimization in a dynamic environment using various test functions, performance of this new operator is studied. Utility of this new operator to multimodal function optimization in a dynamic environment is described.
