Noisy optimization problems - a particular challenge for differential evolution?

The popularity of search heuristics has lead to numerous new approaches in the last two decades. Since algorithm performance is problem dependent and parameter sensitive, it is difficult to consider any single approach as of greatest utility overall problems. In contrast, differential evolution (DE) is a numerical optimization approach that requires hardly any parameter tuning and is very efficient and reliable on both benchmark and real-world problems. However, the results presented in this paper demonstrate that standard methods of evolutionary optimization are able to outperform DE on noisy problems when the fitness of candidate solutions approaches the fitness variance caused by the noise.

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