feat(algorithms): add Umda Univariate Marginal Distribution EDA for binary problems

Mühlenbein 1997 UMDA: simplest Estimation-of-Distribution Algorithm for
`Vec<bool>` problems. Each generation:
- Evaluate the current population
- Select the top μ members by fitness
- Estimate per-bit marginal probability p_i = (count of 1s at bit i in
  the μ-best) / μ
- Sample population_size new individuals from the resulting product-of-
  Bernoullis distribution

Single-objective only. Bit-wise probabilities are clamped to
`[1 / (2 · μ), 1 - 1 / (2 · μ)]` to keep the population from collapsing
to a deterministic single string before convergence is meaningful
(standard Laplace-style smoothing for UMDA).

Tests: solves OneMax (maximize Σ bits) on a 20-bit instance,
deterministic reruns, panic on multi-objective.

src/prelude.rs

@@ -27,5 +27,6 @@ pub use crate::algorithms::{
     GeneticAlgorithm, GeneticAlgorithmConfig, HillClimber, HillClimberConfig, Ibea,
     IbeaConfig, Moead, MoeadConfig, Mopso, MopsoConfig, Nsga2, Nsga2Config, Nsga3, Nsga3Config, Paes, PaesConfig, ParticleSwarm,
     ParticleSwarmConfig, RandomSearch, RandomSearchConfig, SimulatedAnnealing,
-    SimulatedAnnealingConfig, Spea2, Spea2Config, TabuSearch, TabuSearchConfig,
+    SimulatedAnnealingConfig, Spea2, Spea2Config, TabuSearch, TabuSearchConfig, Umda,
+    UmdaConfig,
 };