mutation

lab4/main.py

@@ -15,10 +15,11 @@ from gp.fitness import (
 )
 from gp.ga import GARunConfig, genetic_algorithm
 from gp.mutations import (
-    grow_mutation,
-    hoist_mutation,
-    node_replacement_mutation,
-    shrink_mutation,
+    CombinedMutation,
+    GrowMutation,
+    HoistMutation,
+    NodeReplacementMutation,
+    ShrinkMutation,
 )
 from gp.ops import ADD, COS, DIV, EXP, MUL, NEG, POW, SIN, SQUARE, SUB
 from gp.population import ramped_initialization
@@ -36,7 +37,6 @@ X = np.random.uniform(-5.536, 5.536, size=(TEST_POINTS, NUM_VARS))
 # axes = [np.linspace(-5.536, 5.536, TEST_POINTS) for _ in range(NUM_VARS)]
 # X = np.array(np.meshgrid(*axes)).T.reshape(-1, NUM_VARS)
 operations = [SQUARE, SIN, COS, EXP, ADD, SUB, MUL, DIV, POW]
-# operations = [SQUARE, ADD, SUB, MUL]
 terminals = [Var(f"x{i}") for i in range(1, NUM_VARS + 1)]
 
 
@@ -53,36 +53,16 @@ def target_function(x: NDArray[np.float64]) -> NDArray[np.float64]:
     return np.sum(prefix_sums, axis=1)
 
 
-# fitness_function = MSEFitness(target_function, lambda: X)
-# fitness_function = HuberFitness(target_function, lambda: X, delta=0.5)
-# fitness_function = PenalizedFitness(
-#     target_function, lambda: X, base_fitness=fitness, lambda_=0.1
-# )
-# fitness_function = NRMSEFitness(target_function, lambda: X)
 fitness_function = RMSEFitness(target_function, lambda: X)
-
-# fitness_function = PenalizedFitness(
-#     target_function, lambda: X, base_fitness=fitness_function, lambda_=0.0001
-# )
-
-
-def adaptive_mutation(
-    chromosome: Chromosome,
-    generation: int,
-    max_generations: int,
-    max_depth: int,
-) -> Chromosome:
-    r = random.random()
-
-    if r < 0.4:
-        return grow_mutation(chromosome, max_depth=max_depth)
-    elif r < 0.7:
-        return node_replacement_mutation(chromosome)
-    elif r < 0.85:
-        return hoist_mutation(chromosome)
-
-    return shrink_mutation(chromosome)
-
+combined_mutation = CombinedMutation(
+    mutations=[
+        GrowMutation(max_depth=MAX_DEPTH),
+        NodeReplacementMutation(),
+        HoistMutation(),
+        ShrinkMutation(),
+    ],
+    probs=[0.4, 0.3, 0.15, 0.15],
+)
 
 init_population = ramped_initialization(
     20, [i for i in range(MAX_DEPTH - 9, MAX_DEPTH + 1)], terminals, operations
@@ -93,9 +73,7 @@ print("Population size:", len(init_population))
 config = GARunConfig(
     fitness_func=fitness_function,
     crossover_fn=lambda p1, p2: crossover_subtree(p1, p2, max_depth=MAX_DEPTH),
-    mutation_fn=lambda chrom, gen_num: adaptive_mutation(
-        chrom, gen_num, MAX_GENERATIONS, MAX_DEPTH
-    ),
+    mutation_fn=combined_mutation,
     # selection_fn=roulette_selection,
     selection_fn=lambda p, f: tournament_selection(p, f, k=3),
     init_population=init_population,