feat: add NSGA-III and MOEA/D samplers for many-objective optimization

Extract shared evolutionary algorithm infrastructure (genetic operators,
candidate management, Das-Dennis reference points) from NSGA-II into a
new genetic.rs module, then build two new multi-objective samplers on top:

- NSGA-III: reference-point-based niching for well-distributed fronts
  on 3+ objective problems (Das-Dennis structured points, normalization,
  perpendicular distance association, niching selection)

- MOEA/D: decomposition-based optimization with three scalarization
  methods (Tchebycheff, WeightedSum, PBI), weight-vector neighborhoods,
  and neighborhood-based mating selection

Both implement MultiObjectiveSampler with builder pattern, seeded RNG,
and SBX crossover / polynomial mutation via the shared genetic module.

src/sampler/mod.rs

@@ -4,11 +4,14 @@ pub mod bohb;
 #[cfg(feature = "cma-es")]
 pub mod cma_es;
 pub mod differential_evolution;
+pub(crate) mod genetic;
 #[cfg(feature = "gp")]
 pub mod gp;
 pub mod grid;
+pub mod moead;
 pub mod motpe;
 pub mod nsga2;
+pub mod nsga3;
 pub mod random;
 #[cfg(feature = "sobol")]
 pub mod sobol;