Corne, Jerram, Knowles & Oates 2001: divides objective space into a
hyperbox grid and uses per-box population counts to drive selection
toward sparsely-populated regions.
Each generation:
- Maintain an external archive of non-dominated members
- Build a hyperbox grid (`grid_divisions` per axis on the archive's
current axis ranges); count members per box
- Selection picks two parents by region-based tournament: choose two
random non-empty boxes and take a uniform-random member from the
one with fewer occupants
- Variation produces an offspring; insert into archive, dropping
dominated members and (if archive overflows) the most-crowded
occupant of the most-occupied box
Tests cover non-empty front on Schaffer N.1, deterministic reruns,
and panic on `archive_size == 0`.
Beume, Naujoks & Emmerich 2007: a steady-state MOEA that uses
hypervolume contribution as the secondary survival selection criterion.
Each generation:
- Generate ONE child via parent selection + variation + evaluation.
- Combine population + child, run non_dominated_sort.
- The discarded individual is the worst-front member with the
smallest hypervolume contribution (computed via the new
hypervolume_nd_from_evaluations helper).
Selection-quality is excellent at moderate objective counts (2–4) at
the cost of higher per-step compute (each survival selection requires
N+1 hypervolume evaluations of size ≤ N each). Best paired with a
tightly-bounded objective space — the user supplies a fixed reference
point in the config.
Tests: produces a non-empty front on Schaffer N.1, deterministic
reruns, panic on `population_size == 0`, panic on
`reference_point.len() != objectives.len()`.
swaits