Differential evolution (DE) is one of the most powerful continuous optimizers in the field of evolutionary computation. This work systematically benchmarks a classic DE algorithm (DE/rand/1/bin) on the CEC-2013 single-objective continuous optimization testbed. We report, for each test function at different problem dimensionality, the best achieved performance among a wide range of potentially effective parameter settings. It reflects the intrinsic optimization capability of DE/rand/1/bin on this testbed and can serve as a baseline for performance comparison in future research using this testbed. Furthermore, we conduct parameter sensitivity analysis using advanced non-parametric statistical tests to discover statistically significantly superior parameter settings. This analysis provides a statistically reliable rule of thumb for choosing the parameters of DE/rand/1/bin to solve unseen problems. Moreover, we report the performance of DE/rand/1/bin using one superior parameter setting advocated by parameter sensitivity analysis.
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