feat(algorithms): add Grea (Grid-based Evolutionary Algorithm)

Yang, Li, Liu & Zheng 2013 GrEA: many-objective MOEA whose secondary
ranking is a grid-based diversity score instead of crowding distance
or reference vectors.

Each generation:
- NSGA-II-like loop with offspring + non_dominated_sort
- For the splitting front:
  - Translate by ideal/nadir; partition objective space into a
    (`grid_divisions` per axis) grid
  - For every member compute three grid scores:
    - GR (grid rank)         = sum of grid coordinates (closer to ideal = lower)
    - GCD (grid crowding distance) = #neighbors within 1 grid unit (in any axis)
    - GCPD (grid coordinate point distance) = max coord - min coord
  - Sort F_l ascending by GR, then by GCD, then by GCPD
  - Take the top `n - already_selected` survivors

GrEA's grid-based niching is a different lens from NSGA-III's reference
points and RVEA's reference vectors — particularly effective on
non-convex fronts where reference-vector approaches struggle.

src/prelude.rs

@@ -24,7 +24,7 @@ pub use crate::operators::{
 pub use crate::algorithms::{
     AgeMoea, AgeMoeaConfig, AntColonyTsp, AntColonyTspConfig, CmaEs, CmaEsConfig, DifferentialEvolution,
     DifferentialEvolutionConfig, EpsilonMoea, EpsilonMoeaConfig,
-    GeneticAlgorithm, GeneticAlgorithmConfig, HillClimber, HillClimberConfig, Hype,
+    GeneticAlgorithm, GeneticAlgorithmConfig, Grea, GreaConfig, HillClimber, HillClimberConfig, Hype,
     HypeConfig, Ibea, IbeaConfig, Moead, MoeadConfig, Mopso, MopsoConfig, Nsga2,
     Nsga2Config, Nsga3, Nsga3Config, Paes, PaesConfig, ParticleSwarm, PesaII, PesaIIConfig,
     ParticleSwarmConfig, RandomSearch, RandomSearchConfig, Rvea, RveaConfig,