diff --git a/main_package/classes/estimators/structure_score_based_estimator.py b/main_package/classes/estimators/structure_score_based_estimator.py
index d918235..df42668 100644
--- a/main_package/classes/estimators/structure_score_based_estimator.py
+++ b/main_package/classes/estimators/structure_score_based_estimator.py
@@ -10,6 +10,8 @@ from networkx.readwrite import json_graph
 
 from random import choice
 
+import concurrent.futures
+
 import copy
 import utility.cache as ch
 import structure_graph.conditional_intensity_matrix as condim
@@ -92,17 +94,27 @@ class StructureScoreBasedEstimator(se.StructureEstimator):
         if disable_multiprocessing:
             cpu_count = 1
 
-        'Estimate the best parents for each node'
-        with get_context("spawn").Pool(processes=cpu_count) as pool:
+        
+
+
+
+        #with get_context("spawn").Pool(processes=cpu_count) as pool:
         #with multiprocessing.Pool(processes=cpu_count) as pool:
-            list_edges_partial = pool.starmap(estimate_parents, zip(
-                                                                self.nodes,
-                                                                l_max_parents,
-                                                                l_iterations_number,
-                                                                l_patience,
-                                                                l_tabu_length,
-                                                                l_tabu_rules_duration,
-                                                                l_optimizer))
+
+        'Estimate the best parents for each node'
+        with concurrent.futures.ProcessPoolExecutor(max_workers=cpu_count) as executor:
+            list_edges_partial = executor.map(estimate_parents, 
+                                                            self.nodes,
+                                                            l_max_parents,
+                                                            l_iterations_number,
+                                                            l_patience,
+                                                            l_tabu_length,
+                                                            l_tabu_rules_duration,
+                                                            l_optimizer)
+
+
+
+
             # list_edges_partial = [estimate_parents(n,max_parents,iterations_number,patience,tabu_length,tabu_rules_duration,optimizer) for n in self.nodes]
             #list_edges_partial = p.map(estimate_parents, self.nodes)
             #list_edges_partial= estimate_parents('Q',max_parents,iterations_number,patience,tabu_length,tabu_rules_duration,optimizer) 
diff --git a/main_package/tests/estimators/test_structure_score_based_estimator.py b/main_package/tests/estimators/test_structure_score_based_estimator.py
index 877bbae..7fbf623 100644
--- a/main_package/tests/estimators/test_structure_score_based_estimator.py
+++ b/main_package/tests/estimators/test_structure_score_based_estimator.py
@@ -16,9 +16,6 @@ import structure_graph.sample_path as sp
 import estimators.structure_score_based_estimator as se
 import utility.json_importer as ji
 
-from multiprocessing import set_start_method
-
-
 
 
 class TestStructureScoreBasedEstimator(unittest.TestCase):
@@ -26,7 +23,7 @@ class TestStructureScoreBasedEstimator(unittest.TestCase):
     @classmethod
     def setUpClass(cls):
         #cls.read_files = glob.glob(os.path.join('../../data', "*.json"))
-        cls.importer = ji.JsonImporter("../../data/networks_and_trajectories_ternary_data_15.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        cls.importer = ji.JsonImporter("../../data/networks_and_trajectories_binary_data_10.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name')
         cls.s1 = sp.SamplePath(cls.importer)
         cls.s1.build_trajectories()
         cls.s1.build_structure()
@@ -37,7 +34,7 @@ class TestStructureScoreBasedEstimator(unittest.TestCase):
         true_edges = copy.deepcopy(self.s1.structure.edges)
         true_edges = set(map(tuple, true_edges))
 
-        set_start_method("spawn")
+        
         se1 = se.StructureScoreBasedEstimator(self.s1)
         edges = se1.estimate_structure(
                                     max_parents = None,
@@ -46,7 +43,7 @@ class TestStructureScoreBasedEstimator(unittest.TestCase):
                                     tabu_length = 15,
                                     tabu_rules_duration = 15,
                                     optimizer = 'tabu',
-                                    disable_multiprocessing=False
+                                    disable_multiprocessing=True
                                     )