2 files changed, 21 insertions(+), 18 deletions(-)

M codes/eoa_lib/src/evolution.rs
M codes/tsp_hw01/src/main.rs

@@ 35,13 35,14 @@ pub fn evolution_algorithm
      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: &impl FitnessFunction<In = TChromosome, Out = TResult>,
+    fitness: &mut TFitness,
     selection: &mut TSelection,
     pairing: &mut TPairing,
     crossover: &mut TCrossover,


@@ 56,6 57,7 @@ pub fn evolution_algorithm
         &EvolutionStats<TChromosome, TResult>,
         &EvaluatedPopulation<TChromosome, TResult>,
 
+        &mut TFitness,
         &mut TSelection,
         &mut TPairing,
         &mut TCrossover,


@@ 149,6 151,7 @@ pub fn evolution_algorithm
             iteration,
             &stats,
             &current_population,
+            fitness,
             selection,
             pairing,
             crossover,


@@ 195,7 198,7 @@ pub mod tests {
         let result = evolution_algorithm(
             population,
             50,
-            &one_max,
+            &mut one_max,
             &mut TournamentSelection::new(5, 0.8),
             &mut AdjacentPairing::new(),
             &mut BinaryOnePointCrossover::new(),


@@ 213,7 216,7 @@ pub mod tests {
             &MinimizingOperator,
             1000,
             &mut rng,
-            |_, _, _, _, _, _, _, _| ()
+            |_, _, _, _, _, _, _, _, _| ()
         ).unwrap();
 
         assert_eq!(

@@ 269,7 269,7 @@ fn run_evolution_algorithm(instance: &TSPInstance<Dyn>) -> Result<PlotData, Box<
     let result = evolution_algorithm(
         initial_population.clone(),
         parents_count,
-        instance,
+        &mut instance.clone(),
         &mut selection,
         &mut pairing,
         &mut crossover,


@@ 278,7 278,7 @@ fn run_evolution_algorithm(instance: &TSPInstance<Dyn>) -> Result<PlotData, Box<
         &better_than_operator,
         EA_MAX_ITERATIONS, // max iterations
         &mut rng,
-        |iteration, stats, _, _, _, _, perturbation, _| {
+        |iteration, stats, _, _, _, _, _, perturbation, _| {
             let iters_till_end = EA_MAX_ITERATIONS - iteration + 1;
             let iters_since_better =
                 iteration - stats.best_candidates.last().map(|c| c.iteration).unwrap_or(0);


@@ 335,7 335,7 @@ fn run_evolution_algorithm_mst(instance: &TSPInstance<Dyn>) -> Result<PlotData, 
     let result = evolution_algorithm(
         initial_population.clone(),
         parents_count,
-        instance,
+        &mut instance.clone(),
         &mut selection,
         &mut pairing,
         &mut crossover,


@@ 344,7 344,7 @@ fn run_evolution_algorithm_mst(instance: &TSPInstance<Dyn>) -> Result<PlotData, 
         &better_than_operator,
         EA_MAX_ITERATIONS, // max iterations
         &mut rng,
-        |iteration, stats, _, _, _, _, perturbation, _| {
+        |iteration, stats, _, _, _, _, _, perturbation, _| {
             let iters_till_end = EA_MAX_ITERATIONS - iteration + 1;
             let iters_since_better =
                 iteration - stats.best_candidates.last().map(|c| c.iteration).unwrap_or(0);


@@ 401,7 401,7 @@ fn run_evolution_algorithm_nn(instance: &TSPInstance<Dyn>) -> Result<PlotData, B
     let result = evolution_algorithm(
         initial_population.clone(),
         parents_count,
-        instance,
+        &mut instance.clone(),
         &mut selection,
         &mut pairing,
         &mut crossover,


@@ 410,7 410,7 @@ fn run_evolution_algorithm_nn(instance: &TSPInstance<Dyn>) -> Result<PlotData, B
         &better_than_operator,
         EA_MAX_ITERATIONS, // max iterations
         &mut rng,
-        |iteration, stats, _, _, _, _, perturbation, _| {
+        |iteration, stats, _, _, _, _, _, perturbation, _| {
             let iters_till_end = EA_MAX_ITERATIONS - iteration + 1;
             let iters_since_better =
                 iteration - stats.best_candidates.last().map(|c| c.iteration).unwrap_or(0);


@@ 467,7 467,7 @@ fn run_evolution_algorithm_cx(instance: &TSPInstance<Dyn>) -> Result<PlotData, B
     let result = evolution_algorithm(
         initial_population.clone(),
         parents_count,
-        instance,
+        &mut instance.clone(),
         &mut selection,
         &mut pairing,
         &mut crossover,


@@ 476,7 476,7 @@ fn run_evolution_algorithm_cx(instance: &TSPInstance<Dyn>) -> Result<PlotData, B
         &better_than_operator,
         EA_MAX_ITERATIONS, // max iterations
         &mut rng,
-        |_iteration, _stats, _, _, _, _, _perturbation, _| {
+        |_iteration, _stats, _, _, _, _, _, _perturbation, _| {
             // No adaptive perturbation changes - keep probabilities constant
         }
     )?;


@@ 525,7 525,7 @@ fn run_evolution_algorithm_pmx(instance: &TSPInstance<Dyn>) -> Result<PlotData, 
     let result = evolution_algorithm(
         initial_population.clone(),
         parents_count,
-        instance,
+        &mut instance.clone(),
         &mut selection,
         &mut pairing,
         &mut crossover,


@@ 534,7 534,7 @@ fn run_evolution_algorithm_pmx(instance: &TSPInstance<Dyn>) -> Result<PlotData, 
         &better_than_operator,
         EA_MAX_ITERATIONS, // max iterations
         &mut rng,
-        |_iteration, _stats, _, _, _, _, _perturbation, _| {
+        |_iteration, _stats, _, _, _, _, _, _perturbation, _| {
             // No adaptive perturbation changes - keep probabilities constant
         }
     )?;


@@ 583,7 583,7 @@ fn run_evolution_algorithm_erx(instance: &TSPInstance<Dyn>) -> Result<PlotData, 
     let result = evolution_algorithm(
         initial_population.clone(),
         parents_count,
-        instance,
+        &mut  instance.clone(),
         &mut selection,
         &mut pairing,
         &mut crossover,


@@ 592,7 592,7 @@ fn run_evolution_algorithm_erx(instance: &TSPInstance<Dyn>) -> Result<PlotData, 
         &better_than_operator,
         EA_MAX_ITERATIONS, // max iterations
         &mut rng,
-        |_iteration, _stats, _, _, _, _, _perturbation, _| {
+        |_iteration, _stats, _, _, _, _, _, _perturbation, _| {
             // No adaptive perturbation changes - keep probabilities constant
         }
     )?;


@@ 638,14 638,14 @@ fn run_evolution_algorithm_binary(instance: &TSPInstance<Dyn>) -> Result<PlotDat
     let initial_population = initializer.initialize(input_dimension, population_size, &mut rng);
     let initial_population = eoa_lib::population::Population::from_vec(initial_population);
 
-    let fitness = TSPBinaryStringWrapper::new(instance, input_dimension, output_dimension).unwrap();
+    let mut fitness = TSPBinaryStringWrapper::new(instance, input_dimension, output_dimension).unwrap();
 
     // Run evolution algorithm
     let parents_count = EA_PARENTS_COUNT;
     let result = evolution_algorithm(
         initial_population.clone(),
         parents_count,
-        &fitness,
+        &mut fitness,
         &mut selection,
         &mut pairing,
         &mut crossover,


@@ 654,7 654,7 @@ fn run_evolution_algorithm_binary(instance: &TSPInstance<Dyn>) -> Result<PlotDat
         &better_than_operator,
         EA_MAX_ITERATIONS, // max iterations
         &mut rng,
-        |iteration, stats, _, _, _, _, perturbation, _| {
+        |iteration, stats, _, _, _, _, _, perturbation, _| {
             let iters_till_end = EA_MAX_ITERATIONS - iteration + 1;
             let iters_since_better =
                 iteration - stats.best_candidates.last().map(|c| c.iteration).unwrap_or(0);