Add test

golem/core/optimisers/genetic/gp_params.py

@@ -59,7 +59,7 @@ class GPAlgorithmParameters(AlgorithmParameters):
 
     In the `steady_state` scheme at each iteration only one individual is updated.
 
-    The `parameter_free` scheme is an adaptive variation of the `generational` scheme.
+    The `parameter_free` scheme is an adaptive variation of the `steady_state` scheme.
     It specifies that the population size and the probability of mutation and crossover
     change depending on the success of convergence. If there are no improvements in fitness,
     then the size and the probabilities increase. When fitness improves, the size and the

test/integration/test_genetic_schemes.py

@@ -0,0 +1,42 @@
+from functools import partial
+
+import numpy as np
+import pytest
+
+from examples.synthetic_graph_evolution.experiment_setup import run_trial
+from examples.synthetic_graph_evolution.generators import generate_labeled_graph
+from examples.synthetic_graph_evolution.tree_search import tree_search_setup
+from golem.core.optimisers.genetic.gp_params import GPAlgorithmParameters
+from golem.core.optimisers.genetic.operators.base_mutations import MutationTypesEnum
+from golem.core.optimisers.genetic.operators.crossover import CrossoverTypesEnum
+from golem.core.optimisers.genetic.operators.inheritance import GeneticSchemeTypesEnum
+
+
+def set_up_params(genetic_scheme: GeneticSchemeTypesEnum):
+    gp_params = GPAlgorithmParameters(
+        multi_objective=False,
+        mutation_types=[
+            MutationTypesEnum.single_add,
+            MutationTypesEnum.single_drop,
+        ],
+        crossover_types=[CrossoverTypesEnum.none],
+        genetic_scheme_type=genetic_scheme
+    )
+    return gp_params
+
+
+@pytest.mark.parametrize('genetic_type', GeneticSchemeTypesEnum)
+def test_genetic_scheme_types(genetic_type):
+    target_graph = generate_labeled_graph('tree', 4, node_labels=['x'])
+    num_iterations = 30
+
+    gp_params = set_up_params(genetic_type)
+    found_graph, history = run_trial(target_graph=target_graph,
+                                     optimizer_setup=partial(tree_search_setup, algorithm_parameters=gp_params),
+                                     num_iterations=num_iterations)
+    assert found_graph is not None
+    # at least 20% more generation than early_stopping_iterations were evaluated
+    assert history.generations_count >= num_iterations // 3 * 1.2
+    # metric improved
+    assert np.mean([ind.fitness.value for ind in history.generations[0].data]) > \
+           np.mean([ind.fitness.value for ind in history.generations[-1].data])