Differential evolution is a stochastic parallel direct search evolution strategy optimization method that is fairly fast and reasonably robust. Differential evolution is implemented in the Wolfram Language as NMinimize[f, vars, Method -> "DifferentialEvolution"] and NMaximize[f, vars, Method -> "DifferentialEvolution"].
Differential evolution is capable of handling nondifferentiable, nonlinear and multimodal objective functions. It has been used to train neural networks having real and constrained integer weights.
In a population of potential solutions within an 
-dimensional search space, a fixed number of vectors are randomly
initialized, then evolved over time to explore the search space and to locate the
minima of the objective function.
At each iteration, called a generation, new vectors are generated by the combination of vectors randomly chosen from the current population (mutation). The outcoming vectors are then mixed with a predetermined target vector. This operation is called recombination and produces the trial vector. Finally, the trial vector is accepted for the next generation if and only if it yields a reduction in the value of the objective function. This last operator is referred to as a selection.
