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/prelude.rs

@@ -30,7 +30,7 @@ pub use crate::algorithms::{
     NelderMead, NelderMeadConfig, Nsga2,
     Nsga2Config, Nsga3, Nsga3Config, OnePlusOneEs, OnePlusOneEsConfig, Paes, PaesConfig, ParticleSwarm, PesaII, PesaIIConfig,
     ParticleSwarmConfig, RandomSearch, RandomSearchConfig, Rvea, RveaConfig,
-    SimulatedAnnealing,
+    SeparableNes, SeparableNesConfig, SimulatedAnnealing,
     SimulatedAnnealingConfig, SmsEmoa, SmsEmoaConfig, Spea2, Spea2Config, TabuSearch,
     TabuSearchConfig, Tlbo, TlboConfig, Umda,
     UmdaConfig,