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/algorithms/mod.rs

@@ -17,6 +17,7 @@ pub mod random_search;
 pub mod simulated_annealing;
 pub mod spea2;
 pub mod tabu_search;
+pub mod umda;
 
 pub use ant_colony_tsp::*;
 pub use cma_es::*;
@@ -34,3 +35,4 @@ pub use random_search::*;
 pub use simulated_annealing::*;
 pub use spea2::*;
 pub use tabu_search::*;
+pub use umda::*;