Differential Evolution › Advantages

DE obtains its perturbation scale from population differences rather than requiring a separate fixed standard deviation for every decision variable. The factor $F$, crossover rate $CR$, population size, initialization, and bounds still require choices. We will test some of those choices instead of assuming that a single setting works for every objective.

DE only needs objective values for proposed real-valued vectors, so its evolutionary phase does not require derivatives. This is useful for simulation outputs or discontinuous losses. A population can retain candidates in several regions at once, although it need not cover every basin and can lose that diversity.

In a deferred generation, every trial is formed from the same old population. Independent objective evaluations can then be batched or distributed before selection. The primitive implementation below evaluates trials serially so that each candidate has an explicit budget index. At a generation boundary, the population, its objective values, and the archived best retain the candidate history used by the next sweep. Reproducing that sweep also requires the objective, bounds, operator settings, remaining budget, and RNG state.