select

fun select(survivors: List<Solution>, sampleMore: (InputMap, Int) -> Solution): Solution(source)

Selects the best survivor. With deltaC > 0 runs the Rinott two-stage indifference-zone procedure: each survivor is sampled up to N_i = max(n0, ⌈(h·S_i/δ_C)²⌉) replications (with n0 the smallest current replication count among the survivors and h the Rinott constant), then the smallest second-stage sample mean is selected. The per-survivor second-stage size is clamped to maxReplicationsPerSystem; when the clamp binds the procedure logs a warning and the correct-selection guarantee becomes best-effort. With deltaC == 0 (degraded) no extra sampling is done and the survivor with the smallest mean is returned.

Return

the selected best solution (with any second-stage replications merged in)

Parameters

survivors

the screened candidates (must be non-empty)

sampleMore

obtains a Solution carrying the requested number of additional replications for the given point; the result is merged into the running total via mergeSolutions