From f39d246106fe659544aca5cad3ce1fb343d9cfa4 Mon Sep 17 00:00:00 2001
From: Rutherther <rutherther@ditigal.xyz>
Date: Sat, 8 Nov 2025 17:26:10 +0100
Subject: [PATCH] feat(lib): allow modifying fitness function in
 evolution_algorithm

---
 codes/eoa_lib/src/evolution.rs |  9 ++++++---
 codes/tsp_hw01/src/main.rs     | 30 +++++++++++++++---------------
 2 files changed, 21 insertions(+), 18 deletions(-)

diff --git a/codes/eoa_lib/src/evolution.rs b/codes/eoa_lib/src/evolution.rs
index 837c3b54643e101be22922a460071a60d39e763e..cbb350ed5f7d9e7b72c891b65b34abc286ab308e 100644
--- a/codes/eoa_lib/src/evolution.rs
+++ b/codes/eoa_lib/src/evolution.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!(
diff --git a/codes/tsp_hw01/src/main.rs b/codes/tsp_hw01/src/main.rs
index d23a9b1f40b637ffcb85ab40453086186085907e..df99b70bb703bccbfa2e20c3023274709a1fe79d 100644
--- a/codes/tsp_hw01/src/main.rs
+++ b/codes/tsp_hw01/src/main.rs
@@ -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);