2 files changed, 90 insertions(+), 0 deletions(-)

A env/src/evolution.rs
M env/src/main.rs

@@ 0,0 1,89 @@
+use std::error::Error;
+
+use crate::{comparison::BetterThanOperator, crossover::Crossover, fitness::FitnessFunction, pairing::Pairing, perturbation::PerturbationOperator, replacement::{EvaluatedChromosome, EvaluatedPopulation, Population, Replacement}, selection::Selection};
+
+pub struct EvolutionCandidate<TInput, TResult> {
+    pub evaluated_chromosome: EvaluatedChromosome<TInput, TResult>,
+    pub iteration: usize
+}
+
+pub struct EvolutionCandidatePopulation<TInput, TResult> {
+    pub current_population: EvaluatedPopulation<TInput, TResult>,
+    pub iteration: usize
+}
+
+pub struct EvolutionStats<TInput, TResult> {
+    pub best_candidates: Vec<EvolutionCandidate<TInput, TResult>>,
+}
+
+pub struct EvolutionResult<TInput, TResult> {
+    pub population: EvaluatedPopulation<TInput, TResult>,
+    pub stats: EvolutionStats<TInput, TResult>,
+    pub iterations: usize
+}
+
+pub fn evolution_algorithm<TChromosome: Clone, TResult: Clone>(
+    initial_population: Population<TChromosome>,
+    parents_count: usize,
+    fitness: &impl FitnessFunction<In = TChromosome, Out = TResult>,
+    selection: &mut impl Selection<TChromosome, TResult>,
+    pairing: &mut impl Pairing<Chromosome = TChromosome, Out = TResult>,
+    crossover: &mut impl Crossover<Chromosome = TChromosome, Out = TResult>,
+    perturbation: &mut impl PerturbationOperator<Chromosome = TChromosome>,
+    replacement: &mut impl Replacement<TChromosome, TResult>,
+    better_than: &impl BetterThanOperator<TResult>,
+    // TODO: termination condition
+    iterations: usize
+) -> Result<EvolutionResult<TChromosome, TResult>, Box<dyn Error>> {
+    let mut current_population = initial_population.evaluate(fitness)?;
+
+    let mut last_best_candidate = EvolutionCandidate {
+        evaluated_chromosome: current_population.best_candidate(better_than).clone(),
+        iteration: 0
+    };
+    let mut stats: EvolutionStats<TChromosome, TResult> = EvolutionStats {
+        best_candidates: vec![]
+    };
+
+    for iteration in 0..iterations {
+        // Figure out best candidate and save it if better than last time
+        let best_candidate = current_population.best_candidate(better_than);
+
+        if better_than.better_than(
+            &best_candidate.evaluation,
+            &last_best_candidate.evaluated_chromosome.evaluation
+        ) {
+            stats.best_candidates.push(last_best_candidate);
+
+            last_best_candidate = EvolutionCandidate {
+                evaluated_chromosome: best_candidate.clone(),
+                iteration
+            }
+        }
+
+        // Selection
+        let parents = selection.select(parents_count, &current_population, better_than);
+        let parent_pairings = pairing.pair(&current_population, parents);
+
+        // Crossover
+        let mut offsprings = crossover.crossover(&current_population, parent_pairings);
+
+        // Mutation
+        for offspring in offsprings.iter_mut() {
+            *offspring = perturbation.perturb(offspring);
+        }
+
+        let evaluated_offsprings = offsprings.evaluate(fitness)?;
+
+        // Replace
+        current_population = replacement.replace(current_population, evaluated_offsprings, better_than);
+    }
+
+    stats.best_candidates.push(last_best_candidate);
+
+    Ok(EvolutionResult {
+        population: current_population,
+        stats,
+        iterations
+    })
+}

@@ 1,5 1,6 @@
 pub mod fitness;
 pub mod pairing;
+pub mod evolution;
 pub mod crossover;
 pub mod bounded;
 pub mod selection;