feat(algorithms): add SeparableNes (Natural Evolution Strategy)

Wierstra et al. 2008/2014 NES with the diagonal-covariance "separable"
variant (sNES). Different theoretical foundation from CMA-ES: rather
than tracking a full covariance matrix and adapting it through
evolution paths, sNES updates the sampling distribution's parameters
by following the natural gradient of expected fitness.

Each generation:
- Sample λ offspring from N(μ, diag(σ²))
- Rank-shape the fitnesses (utility weights from the standard NES table)
- Update μ along the natural gradient: μ ← μ + η_μ · σ · sum(u_i · z_i)
- Update σ multiplicatively: σ_j ← σ_j · exp(η_σ/2 · sum(u_i · (z_i,j² - 1)))

Vec<f64> decisions only, single-objective only. The diagonal covariance
makes per-step cost O(λ·n) instead of CMA-ES's O(λ·n²) — much faster on
high-dimensional problems where full-covariance tracking is expensive
or numerically fragile, at the cost of being unable to handle strongly
rotated landscapes.

src/algorithms/mod.rs

@@ -27,6 +27,7 @@ pub mod random_search;
 pub mod rvea;
 pub mod simulated_annealing;
 pub mod sms_emoa;
+pub mod snes;
 pub mod spea2;
 pub mod tabu_search;
 pub mod tlbo;
@@ -58,6 +59,7 @@ pub use random_search::*;
 pub use rvea::*;
 pub use simulated_annealing::*;
 pub use sms_emoa::*;
+pub use snes::*;
 pub use spea2::*;
 pub use tabu_search::*;
 pub use tlbo::*;