1 files changed, 66 insertions(+), 11 deletions(-)

M codes/eoa_lib/src/evolution.rs

@@ 101,6 101,64 @@ pub fn evolution_algorithm
         &mut TReplacement
     )
 ) -> Result<EvolutionResult<TChromosome, TResult>, Box<dyn Error>> {
+        evolution_algorithm_best_candidate(
+            initial_population,
+            parents_count,
+            fitness,
+            selection,
+            pairing,
+            crossover,
+            perturbation,
+            replacement,
+            better_than,
+            iterations,
+            rng,
+            evolutionary_strategy,
+            |_, evaluation, last_best_candidate| last_best_candidate.is_none() ||
+                better_than.better_than(
+                    evaluation,
+                    &last_best_candidate.as_ref().unwrap().evaluated_chromosome.evaluation
+                ))
+}
+
+pub fn evolution_algorithm_best_candidate
+    <TChromosome: Clone,
+     TResult: Clone,
+     const DParents: usize,
+     TSelection: Selection<TChromosome, TResult>,
+     TFitness: FitnessFunction<In = TChromosome, Out = TResult>,
+     TPairing: Pairing<DParents, Chromosome = TChromosome, Out = TResult>,
+     TCrossover: Crossover<DParents, Chromosome = TChromosome, Out = TResult>,
+     TReplacement: Replacement<TChromosome, TResult>,
+     TPerturbation: PerturbationOperator<Chromosome = TChromosome>>(
+    initial_population: Population<TChromosome>,
+    parents_count: usize,
+    fitness: &mut TFitness,
+    selection: &mut TSelection,
+    pairing: &mut TPairing,
+    crossover: &mut TCrossover,
+    perturbation: &mut TPerturbation,
+    replacement: &mut TReplacement,
+    better_than: &impl BetterThanOperator<TResult>,
+    // TODO: termination condition
+    iterations: usize,
+    rng: &mut dyn RngCore,
+    mut evolutionary_strategy: impl FnMut(
+        usize,
+        &EvolutionStats<TChromosome, TResult>,
+        &EvaluatedPopulation<TChromosome, TResult>,
+
+        &mut TFitness,
+        &mut TSelection,
+        &mut TPairing,
+        &mut TCrossover,
+        &mut TPerturbation,
+        &mut TReplacement
+    ),
+    // For the statistics, evaluate if a candidate is better. Potential for different functrion than better_than that's used
+    // for the replacement, selection etc.
+    better_than_stats: impl Fn(&TChromosome, &TResult, &Option<EvolutionCandidate<TChromosome, TResult>>) -> bool,
+) -> Result<EvolutionResult<TChromosome, TResult>, Box<dyn Error>> {
     let mut current_evaluation = 0;
 
     let mut last_best_candidate: Option<EvolutionCandidate<TChromosome, TResult>> = None;


@@ 110,21 168,17 @@ pub fn evolution_algorithm
 
     fn apply_new_eval<TChromosome: Clone, TResult: Clone>(
         current_evaluation: &mut usize,
-        better_than: &impl BetterThanOperator<TResult>,
         current_iteration: &usize,
         stats: &mut EvolutionStats<TChromosome, TResult>,
         population: &EvaluatedPopulation<TChromosome, TResult>,
-        last_best_candidate: &mut Option<EvolutionCandidate<TChromosome, TResult>>
+        last_best_candidate: &mut Option<EvolutionCandidate<TChromosome, TResult>>,
+        better_than_stats: &impl Fn(&TChromosome, &TResult, &Option<EvolutionCandidate<TChromosome, TResult>>) -> bool,
     ) {
         for individual in population.iter() {
             let evaluation = &individual.evaluation;
             let chromosome = &individual.chromosome;
 
-            if last_best_candidate.is_none() ||
-                better_than.better_than(
-                    evaluation,
-                    &last_best_candidate.as_ref().unwrap().evaluated_chromosome.evaluation
-                ) {
+            if better_than_stats(chromosome, evaluation, last_best_candidate) {
                     let previous_best = std::mem::replace(
                         last_best_candidate,
                         Some(EvolutionCandidate {


@@ 148,11 202,11 @@ pub fn evolution_algorithm
         initial_population.evaluate(fitness)?;
     apply_new_eval(
         &mut current_evaluation,
-        better_than,
         &0,
         &mut stats,
         &current_population,
-        &mut last_best_candidate);
+        &mut last_best_candidate,
+        &better_than_stats);
 
     for iteration in 1..=iterations {
         // Selection


@@ 171,11 225,12 @@ pub fn evolution_algorithm
             offsprings.evaluate(fitness)?;
         apply_new_eval(
             &mut current_evaluation,
-            better_than,
             &iteration,
             &mut stats,
             &current_population,
-          &mut last_best_candidate);
+            &mut last_best_candidate,
+            &better_than_stats
+        );
 
         // Replace
         current_population = replacement.replace(current_population, evaluated_offsprings, better_than, rng);