swaits
33a927d86d
Standard (μ+λ) NSGA-II with binary tournament parent selection on
(rank, crowding distance) and elitist survival selection on the combined
parent + offspring population (spec §12.3):
1. Initialize population_size random decisions.
2. Each generation: select parents by binary tournament (rank ↑ then
crowding ↓ then random), apply variation, evaluate offspring,
combine, non_dominated_sort, fill the next population front-by-front
trimming the partial last front by crowding distance descending.
3. Return final population, Pareto front, best (None for >1 objective),
evaluation count, and generation count.
Internal Nsga2Entry { candidate, rank, crowding_distance } stays
private. Panics with clear messages on `population_size == 0` or
empty `vary` output. Tests cover population length, evaluation count,
non-empty front, and full determinism with the same seed (spec §18.4).