feat(algorithms): add GeneticAlgorithm — single-objective generational GA

Canonical generational GA with elitism: each generation runs binary
tournament selection (using `tournament_select_single_objective`) on
the current population, applies the variation operator pair-wise to
produce offspring, evaluates them, then replaces the population while
preserving the top `elitism` members from the previous generation
(elitism prevents fitness regression on a single seed).

Single-objective only. Generic over decision type — pair with
`SimulatedBinaryCrossover + PolynomialMutation` for real-valued,
single-point crossover + bit-flip for binary, etc.

Tests: convergence on Sphere1D, deterministic reruns, panic on
multi-objective, panic on `population_size < 2`, panic on
`elitism > population_size`.

src/prelude.rs

@@ -22,8 +22,8 @@ pub use crate::operators::{
 };
 
 pub use crate::algorithms::{
-    DifferentialEvolution, DifferentialEvolutionConfig, HillClimber, HillClimberConfig,
-    Moead, MoeadConfig, Nsga2, Nsga2Config, Nsga3, Nsga3Config, Paes, PaesConfig,
-    RandomSearch, RandomSearchConfig, SimulatedAnnealing, SimulatedAnnealingConfig,
-    Spea2, Spea2Config,
+    DifferentialEvolution, DifferentialEvolutionConfig, GeneticAlgorithm,
+    GeneticAlgorithmConfig, HillClimber, HillClimberConfig, Moead, MoeadConfig, Nsga2,
+    Nsga2Config, Nsga3, Nsga3Config, Paes, PaesConfig, RandomSearch, RandomSearchConfig,
+    SimulatedAnnealing, SimulatedAnnealingConfig, Spea2, Spea2Config,
 };