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/algorithms/genetic_algorithm.rs

@@ -0,0 +1,279 @@
+//! `GeneticAlgorithm` — single-objective generational GA with elitism.
+
+use crate::algorithms::parallel_eval::evaluate_batch;
+use crate::core::candidate::Candidate;
+use crate::core::objective::Direction;
+use crate::core::population::Population;
+use crate::core::problem::Problem;
+use crate::core::result::OptimizationResult;
+use crate::core::rng::rng_from_seed;
+use crate::pareto::front::best_candidate;
+use crate::selection::tournament::tournament_select_single_objective;
+use crate::traits::{Initializer, Optimizer, Variation};
+
+/// Configuration for [`GeneticAlgorithm`].
+#[derive(Debug, Clone)]
+pub struct GeneticAlgorithmConfig {
+    /// Constant population size.
+    pub population_size: usize,
+    /// Number of generations.
+    pub generations: usize,
+    /// Tournament size for parent selection (typical: 2).
+    pub tournament_size: usize,
+    /// Number of elite members to carry over each generation (must be
+    /// `< population_size`).
+    pub elitism: usize,
+    /// Seed for the deterministic RNG.
+    pub seed: u64,
+}
+
+impl Default for GeneticAlgorithmConfig {
+    fn default() -> Self {
+        Self {
+            population_size: 100,
+            generations: 200,
+            tournament_size: 2,
+            elitism: 2,
+            seed: 42,
+        }
+    }
+}
+
+/// Single-objective generational genetic algorithm with elitism.
+///
+/// Each generation: binary tournament selection (on the configured
+/// `tournament_size`) chooses parent pairs, the variation operator
+/// produces offspring, those are evaluated, and the next population is
+/// the top `elitism` from the previous generation plus the best
+/// `population_size - elitism` offspring (by fitness).
+#[derive(Debug, Clone)]
+pub struct GeneticAlgorithm<I, V> {
+    /// Algorithm configuration.
+    pub config: GeneticAlgorithmConfig,
+    /// Initial-decision sampler.
+    pub initializer: I,
+    /// Offspring-producing variation operator.
+    pub variation: V,
+}
+
+impl<I, V> GeneticAlgorithm<I, V> {
+    /// Construct a `GeneticAlgorithm`.
+    pub fn new(config: GeneticAlgorithmConfig, initializer: I, variation: V) -> Self {
+        Self { config, initializer, variation }
+    }
+}
+
+impl<P, I, V> Optimizer<P> for GeneticAlgorithm<I, V>
+where
+    P: Problem + Sync,
+    P::Decision: Send,
+    I: Initializer<P::Decision>,
+    V: Variation<P::Decision>,
+{
+    fn run(&mut self, problem: &P) -> OptimizationResult<P::Decision> {
+        assert!(
+            self.config.population_size >= 2,
+            "GeneticAlgorithm population_size must be >= 2",
+        );
+        assert!(
+            self.config.tournament_size >= 1,
+            "GeneticAlgorithm tournament_size must be >= 1",
+        );
+        assert!(
+            self.config.elitism < self.config.population_size,
+            "GeneticAlgorithm elitism must be < population_size",
+        );
+        let n = self.config.population_size;
+        let objectives = problem.objectives();
+        assert!(
+            objectives.is_single_objective(),
+            "GeneticAlgorithm requires exactly one objective",
+        );
+        let direction = objectives.objectives[0].direction;
+        let mut rng = rng_from_seed(self.config.seed);
+
+        let initial_decisions = self.initializer.initialize(n, &mut rng);
+        let mut population: Vec<Candidate<P::Decision>> =
+            evaluate_batch(problem, initial_decisions);
+        let mut evaluations = population.len();
+
+        for _ in 0..self.config.generations {
+            // --- Phase 1: parent selection + variation (serial RNG) ---
+            let mut offspring_decisions: Vec<P::Decision> = Vec::with_capacity(n);
+            while offspring_decisions.len() < n {
+                let parents_decisions = tournament_select_single_objective(
+                    &population,
+                    &objectives,
+                    self.config.tournament_size,
+                    2,
+                    &mut rng,
+                );
+                let children = self.variation.vary(&parents_decisions, &mut rng);
+                assert!(!children.is_empty(), "GeneticAlgorithm variation returned no children");
+                for child in children {
+                    if offspring_decisions.len() >= n {
+                        break;
+                    }
+                    offspring_decisions.push(child);
+                }
+            }
+
+            // --- Phase 2: parallel-friendly batch evaluation ---
+            let offspring = evaluate_batch(problem, offspring_decisions);
+            evaluations += offspring.len();
+
+            // --- Phase 3: survival = elites + best offspring ---
+            population = survival_selection(
+                &population,
+                offspring,
+                direction,
+                n,
+                self.config.elitism,
+            );
+        }
+
+        let best = best_candidate(&population, &objectives);
+        let front: Vec<Candidate<P::Decision>> = best.iter().cloned().collect();
+        OptimizationResult::new(
+            Population::new(population),
+            front,
+            best,
+            evaluations,
+            self.config.generations,
+        )
+    }
+}
+
+fn survival_selection<D: Clone>(
+    parents: &[Candidate<D>],
+    offspring: Vec<Candidate<D>>,
+    direction: Direction,
+    n: usize,
+    elitism: usize,
+) -> Vec<Candidate<D>> {
+    // Sort the parents by fitness descending (best first).
+    let mut sorted_parents: Vec<Candidate<D>> = parents.to_vec();
+    sorted_parents.sort_by(|a, b| compare_for_fitness(a, b, direction));
+
+    // Sort the offspring the same way.
+    let mut sorted_offspring = offspring;
+    sorted_offspring.sort_by(|a, b| compare_for_fitness(a, b, direction));
+
+    let mut next: Vec<Candidate<D>> = Vec::with_capacity(n);
+    next.extend(sorted_parents.into_iter().take(elitism));
+    next.extend(sorted_offspring.into_iter().take(n - elitism));
+    next
+}
+
+/// Order such that "best" comes first. Feasible beats infeasible; among
+/// infeasibles, lower violation wins; among feasibles, direction-aware
+/// objective comparison.
+fn compare_for_fitness<D>(
+    a: &Candidate<D>,
+    b: &Candidate<D>,
+    direction: Direction,
+) -> std::cmp::Ordering {
+    match (a.evaluation.is_feasible(), b.evaluation.is_feasible()) {
+        (true, false) => std::cmp::Ordering::Less,
+        (false, true) => std::cmp::Ordering::Greater,
+        (false, false) => a
+            .evaluation
+            .constraint_violation
+            .partial_cmp(&b.evaluation.constraint_violation)
+            .unwrap_or(std::cmp::Ordering::Equal),
+        (true, true) => match direction {
+            Direction::Minimize => a.evaluation.objectives[0]
+                .partial_cmp(&b.evaluation.objectives[0])
+                .unwrap_or(std::cmp::Ordering::Equal),
+            Direction::Maximize => b.evaluation.objectives[0]
+                .partial_cmp(&a.evaluation.objectives[0])
+                .unwrap_or(std::cmp::Ordering::Equal),
+        },
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::operators::{
+        CompositeVariation, PolynomialMutation, RealBounds, SimulatedBinaryCrossover,
+    };
+    use crate::tests_support::{SchafferN1, Sphere1D};
+
+    fn make_optimizer(
+        seed: u64,
+    ) -> GeneticAlgorithm<RealBounds, CompositeVariation<SimulatedBinaryCrossover, PolynomialMutation>>
+    {
+        let bounds = vec![(-5.0, 5.0)];
+        let initializer = RealBounds::new(bounds.clone());
+        let variation = CompositeVariation {
+            crossover: SimulatedBinaryCrossover::new(bounds.clone(), 15.0, 0.5),
+            mutation: PolynomialMutation::new(bounds, 20.0, 1.0),
+        };
+        GeneticAlgorithm::new(
+            GeneticAlgorithmConfig {
+                population_size: 30,
+                generations: 50,
+                tournament_size: 2,
+                elitism: 2,
+                seed,
+            },
+            initializer,
+            variation,
+        )
+    }
+
+    #[test]
+    fn finds_minimum_of_sphere() {
+        let mut opt = make_optimizer(1);
+        let r = opt.run(&Sphere1D);
+        let best = r.best.unwrap();
+        assert!(
+            best.evaluation.objectives[0] < 1e-2,
+            "got f = {}",
+            best.evaluation.objectives[0],
+        );
+    }
+
+    #[test]
+    fn deterministic_with_same_seed() {
+        let mut a = make_optimizer(99);
+        let mut b = make_optimizer(99);
+        let ra = a.run(&Sphere1D);
+        let rb = b.run(&Sphere1D);
+        assert_eq!(
+            ra.best.unwrap().evaluation.objectives,
+            rb.best.unwrap().evaluation.objectives,
+        );
+    }
+
+    #[test]
+    #[should_panic(expected = "exactly one objective")]
+    fn multi_objective_panics() {
+        let mut opt = make_optimizer(0);
+        let _ = opt.run(&SchafferN1);
+    }
+
+    #[test]
+    #[should_panic(expected = "elitism must be < population_size")]
+    fn elitism_too_large_panics() {
+        let bounds = vec![(-1.0, 1.0)];
+        let initializer = RealBounds::new(bounds.clone());
+        let variation = CompositeVariation {
+            crossover: SimulatedBinaryCrossover::new(bounds.clone(), 15.0, 0.5),
+            mutation: PolynomialMutation::new(bounds, 20.0, 1.0),
+        };
+        let mut opt = GeneticAlgorithm::new(
+            GeneticAlgorithmConfig {
+                population_size: 4,
+                generations: 1,
+                tournament_size: 2,
+                elitism: 4,
+                seed: 0,
+            },
+            initializer,
+            variation,
+        );
+        let _ = opt.run(&Sphere1D);
+    }
+}

src/algorithms/mod.rs

@@ -1,6 +1,7 @@
 //! Built-in reference optimizers.
 
 pub mod differential_evolution;
+pub mod genetic_algorithm;
 pub mod hill_climber;
 pub mod moead;
 pub mod nsga2;
@@ -12,6 +13,7 @@ pub mod simulated_annealing;
 pub mod spea2;
 
 pub use differential_evolution::*;
+pub use genetic_algorithm::*;
 pub use hill_climber::*;
 pub use moead::*;
 pub use nsga2::*;

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,
 };