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.
Class or Function Names plot_beeswarm(study, *, params=None, target=None, target_name="Objective Value", color_map="RdBu_r", ax=None)
study: An Optuna study with completed trials. params: A list of parameter names to include. If None, all parameters across completed trials are used. target: A callable that extracts a scalar value from a FrozenTrial. Defaults to trial.value. target_name: Label for the x-axis. Defaults to "Objective Value". color_map: Matplotlib colormap name. Defaults to "RdBu_r" (blue for low, red for high).