Apply PR-FB

Apply PR-FB

test/test_ea_mu_plus_lambda.py

@@ -228,39 +228,37 @@ def objective(individual):
         individual.fitness = float(individual.idx)
         return individual
 
-    # create a population and an instance of the evolutionary algorithm and
-    # check that number of objective calls is initially zero
+    n_objective_calls_expected = 0
     pop = cgp.Population(**population_params, genome_params=genome_params)
     ea = cgp.ea.MuPlusLambda(**ea_params)
-    assert ea.n_objective_calls == 0
+    assert ea.n_objective_calls == n_objective_calls_expected
 
-    # after initialization of parents number of objective calls must be equal
-    # to the number of parents
     ea.initialize_fitness_parents(pop, objective)
-    assert ea.n_objective_calls == population_params["n_parents"]
+    n_objective_calls_expected = population_params["n_parents"]
+    assert ea.n_objective_calls == n_objective_calls_expected
 
-    # count the number of objective calls by adding the number of offspring with fitness = None
-    # at each generation
-    n_objective_calls = ea.n_objective_calls
+    n_objective_calls_expected = ea.n_objective_calls
     n_generations = 100
     for _ in range(n_generations):
         offsprings = ea._create_new_offspring_generation(pop)
         combined = offsprings + pop.parents
-        n_objective_calls += sum([1 for ind in combined if ind.fitness is None])
+        n_objective_calls_expected += sum([1 for ind in combined if ind.fitness is None])
         combined = ea._compute_fitness(combined, objective)
-        assert n_objective_calls == ea.n_objective_calls
+        assert n_objective_calls_expected == ea.n_objective_calls
+
+
+def test_update_n_objective_calls_mutation_rate_one(population_params, genome_params, ea_params):
+    def objective(individual):
+        individual.fitness = float(individual.idx)
+        return individual
 
-    # create a population with mutation rate one and check that the number
-    # of objective calls is always equal to the number of parents
-    # plus the current generation times the number of offspring
     population_params["mutation_rate"] = 1.0
     pop = cgp.Population(**population_params, genome_params=genome_params)
     ea = cgp.ea.MuPlusLambda(**ea_params)
     ea.initialize_fitness_parents(pop, objective)
+    n_objective_calls_expected = population_params["n_parents"]
     n_step_calls = 100
     for idx_current_step in range(n_step_calls):
         ea.step(pop, objective)
-        assert (
-            ea.n_objective_calls
-            == population_params["n_parents"] + (idx_current_step + 1) * ea_params["n_offsprings"]
-        )
+        n_objective_calls_expected += ea_params["n_offsprings"]
+        assert ea.n_objective_calls == n_objective_calls_expected

test/test_hl_api.py

@@ -135,7 +135,9 @@ def f1(x):
     assert abs(pop.champion.fitness) == pytest.approx(0.0)
 
 
-def test_finite_evolve(population_params, genome_params, ea_params):
+def test_finite_max_generations_or_max_objective_calls(
+    population_params, genome_params, ea_params
+):
     def objective(individual):
         individual.fitness = float(individual.idx)
         return individual
@@ -144,7 +146,7 @@ def objective(individual):
     ea = cgp.ea.MuPlusLambda(**ea_params)
     evolve_params = {
         "max_generations": np.inf,
-        "min_fitness": -1e-12,
+        "min_fitness": 0,
         "max_objective_calls": np.inf,
     }
     with pytest.raises(ValueError):