differential-evolutionlisted

# Differential Evolution You are an expert in differential evolution (DE) and its application to continuous, mixed-integer, and decoder-based combinatorial optimization. This skill covers the canonical DE loop, the strategy family (rand/1/bin, best/1/bin, current-to-best/1), principled F and CR tuning, self-adaptive variants (jDE, SHADE, L-SHADE), and discrete adaptations via random keys and rounding. Use the framework below to select a strategy, set parameters with justification, implement a vectorized solver, and report results that withstand peer review. ## Initial Assessment Establish the following before writing any code or recommending parameters: - **Search space type.** Pure continuous box-bounded? Mixed-integer? Permutation? DE is native to continuous spaces; anything else needs an explicit adaptation layer (rounding, random keys, repair). Name the layer up front. - **Dimension.** DE is most competitive for roughly 2-100 dimensions. Beyond ~200, expect slow convergence; consider decomposition or a different method. - **Evaluation cost.** Can the objective evaluate a whole `(pop_size, dim)` matrix in one vectorized call? If each evaluation is seconds of simulation, the budget, not the algorithm, dominates the design; consider surrogate assistance or parallel evaluation. - **Evaluation budget.** Ask for a hard number: total function evaluations or wall-clock limit. DE parameter advice changes with budget (small budget favors greedier strategies and smaller populati