feat: better responsability management in algorithm.py

Makefile

@@ -186,5 +186,5 @@ uninstall:
 	$(PIP) uninstall fandango-fuzzer -y
 
 # python -m evaluation.vs_isla.run_evaluation
-evaluate:
+evaluate evaluation:
 	$(PYTHON) -m evaluation.vs_isla.run_evaluation 1

src/fandango/evolution/adaptation.py

@@ -0,0 +1,82 @@
+from typing import List, Tuple
+
+from fandango.evolution.evaluation import Evaluator
+from fandango.language.grammar import DerivationTree
+from fandango.logger import LOGGER
+
+
+class AdaptiveTuner:
+    def __init__(self, initial_mutation_rate: float, initial_crossover_rate: float):
+        self.mutation_rate = initial_mutation_rate
+        self.crossover_rate = initial_crossover_rate
+
+    def update_parameters(
+        self,
+        generation: int,
+        prev_best_fitness: float,
+        current_best_fitness: float,
+        population: List[DerivationTree],
+        evaluator: Evaluator,
+    ) -> Tuple[float, float]:
+        diversity_map = evaluator.compute_diversity_bonus(population)
+        avg_diversity = (
+            sum(diversity_map.values()) / len(diversity_map) if diversity_map else 0
+        )
+
+        fitness_improvement_threshold = (
+            0.01  # minimal improvement to be considered significant
+        )
+        diversity_low_threshold = 0.1  # low diversity threshold
+
+        # Adaptive Mutation
+        if (
+            current_best_fitness - prev_best_fitness
+        ) < fitness_improvement_threshold or avg_diversity < diversity_low_threshold:
+            new_mutation_rate = min(1.0, self.mutation_rate * 1.1)
+            LOGGER.info(
+                f"Generation {generation}: Increasing mutation rate from {self.mutation_rate:.2f} to {new_mutation_rate:.2f}"
+            )
+            self.mutation_rate = new_mutation_rate
+        else:
+            new_mutation_rate = max(0.01, self.mutation_rate * 0.95)
+            LOGGER.info(
+                f"Generation {generation}: Decreasing mutation rate from {self.mutation_rate:.2f} to {new_mutation_rate:.2f}"
+            )
+            self.mutation_rate = new_mutation_rate
+
+        # Adaptive Crossover
+        if avg_diversity < diversity_low_threshold:
+            new_crossover_rate = min(1.0, self.crossover_rate * 1.05)
+            LOGGER.info(
+                f"Generation {generation}: Increasing crossover rate from {self.crossover_rate:.2f} to {new_crossover_rate:.2f}"
+            )
+            self.crossover_rate = new_crossover_rate
+        else:
+            new_crossover_rate = max(0.1, self.crossover_rate * 0.98)
+            LOGGER.info(
+                f"Generation {generation}: Decreasing crossover rate from {self.crossover_rate:.2f} to {new_crossover_rate:.2f}"
+            )
+            self.crossover_rate = new_crossover_rate
+
+        return self.mutation_rate, self.crossover_rate
+
+    def log_generation_statistics(
+        self,
+        generation: int,
+        evaluation: List[Tuple[DerivationTree, float, List]],
+        population: List[DerivationTree],
+        evaluator: Evaluator,
+    ):
+        best_fitness = max(fitness for _, fitness, _ in evaluation)
+        avg_fitness = sum(fitness for _, fitness, _ in evaluation) / len(evaluation)
+        diversity_bonus = evaluator.compute_diversity_bonus(population)
+        avg_diversity = (
+            sum(diversity_bonus.values()) / len(diversity_bonus)
+            if diversity_bonus
+            else 0
+        )
+        LOGGER.info(
+            f"Generation {generation} stats -- Best fitness: {best_fitness:.2f}, "
+            f"Avg fitness: {avg_fitness:.2f}, Avg diversity: {avg_diversity:.2f}, "
+            f"Population size: {len(population)}"
+        )