Update the crossover method to use dictionaries instead of tuples

src/main.py

@@ -561,33 +561,34 @@ def mutate(parents, num_mutations, d_model):
     return mutated_population
 
 
-def mutate(parents, num_mutations, d_model):
+def crossover(parents, num_crossovers, d_model):
     """
-    Perform mutation on the parent population.
+    Perform crossover on the parent population.
 
     Args:
         parents (list): Parent population.
-        num_mutations (int): Number of mutations to perform.
+        num_crossovers (int): Number of crossovers to perform.
         d_model (int): Dimension of the model.
 
     Returns:
-        list: Mutated population.
+        list: Crossover population.
     """
-    mutated_population = []
-    for _ in range(num_mutations):
-        parent = random.choice(parents)
-        child = {"lambda_i": parent["lambda_i"].clone(), "n_hat": parent["n_hat"]}
+    crossover_population = []
+    for _ in range(num_crossovers):
+        parent1 = random.choice(parents)
+        parent2 = random.choice(parents)
+        child = {"lambda_i": parent1["lambda_i"].clone(), "n_hat": parent1["n_hat"]}
 
         for i in range(d_model):
-            if random.random() < 0.1:
-                child["lambda_i"][i] *= random.uniform(0.8, 1.2)
+            if random.random() < 0.5:
+                child["lambda_i"][i] = parent2["lambda_i"][i]
 
-        if random.random() < 0.1:
-            child["n_hat"] = random.randint(0, d_model)
+        if random.random() < 0.5:
+            child["n_hat"] = parent2["n_hat"]
 
-        mutated_population.append(child)
+        crossover_population.append(child)
 
-    return mutated_population
+    return crossover_population
 
 
 def fine_tune(model, train_data, val_data, target_length, lambda_factors, n_hat, steps):