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The localization of dipolar sources in the brain based on EEG or MEG data is a frequent problem in the neurosciences. Especially deterministic approaches often have problems in finding the global optimum of the associated non-linear optimization function, when two or more di poles are to be reconstructed. In such cases, probabilistic approaches turned out to be superior, but their applicability in neuromagnetic source localizations is not yet satisfactory. The objective of this study was the design of multi-level evolution strategies that perform better in such applications. We newly created nested fast evolution strategies which realize a combination of locally searching inner evolution strategies and globally searching outer fast evolution strategies. They were benchmarked and compared to single-level fast evolution strategy by conducting a two dipole fit with a MEG data set from a neuropsychological experiment. In the comparison, fast nested evolution strategies showed superior performance.