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/ant_colony_tsp.rs

@@ -197,7 +197,7 @@ fn build_tour(
     for _ in 1..n {
         let current = *tour.last().unwrap();
         // Build a probability vector over the unvisited candidates.
-        let mut probs: Vec<(usize, f64)> = (0..n)
+        let probs: Vec<(usize, f64)> = (0..n)
             .filter(|&j| !visited[j])
             .map(|j| {
                 let p = pheromone[current][j].max(0.0).powf(alpha) * eta[current][j].powf(beta);