Abstract CMA-ES is the gold standard for continuous black-box optimization, but it has diminishing returns: after convergence, additional CMA-ES trials provide little improvement. This sampler addresses that by splitting the trial budget into three phases:
Sobol QMC (8 trials) — quasi-random space-filling initialization CMA-ES (132 trials) — covariance matrix adaptation for main optimization Quasi-random Gaussian refinement (60 trials) — targeted local search around the best point using Sobol-based perturbation vectors with exponentially decaying scale The refinement phase uses quasi-random Sobol sequences transformed via inverse CDF to generate Gaussian-distributed perturbation vectors.