diff --git a/autotm/algorithms_for_tuning/genetic_algorithm/genetic_algorithm.py b/autotm/algorithms_for_tuning/genetic_algorithm/genetic_algorithm.py
index 04990ff..9aac782 100755
--- a/autotm/algorithms_for_tuning/genetic_algorithm/genetic_algorithm.py
+++ b/autotm/algorithms_for_tuning/genetic_algorithm/genetic_algorithm.py
@@ -9,7 +9,8 @@
 from autotm.algorithms_for_tuning.genetic_algorithm.ga import GA
 from autotm.algorithms_for_tuning.genetic_algorithm.surrogate import Surrogate
 from autotm.algorithms_for_tuning.individuals import IndividualBuilder
-from autotm.fitness.estimator import ComputableFitnessEstimator, SurrogateEnabledComputableFitnessEstimator
+from autotm.fitness.estimator import ComputableFitnessEstimator, SurrogateEnabledComputableFitnessEstimator, \
+    DistributedSurrogateEnabledComputableFitnessEstimator
 from autotm.fitness.tm import fit_tm, TopicModel
 from autotm.utils import make_log_config_dict
 
@@ -47,6 +48,7 @@ def get_best_individual(
         quiet_log: bool = False,
         statistics_collector: Optional[StatisticsCollector] = None,
         individual_type: str = "regular",
+        fitness_estimator_type: str = "local", # distributed
         **kwargs
 ):
     """
@@ -103,15 +105,31 @@ def get_best_individual(
         cross_alpha = float(cross_alpha)
 
     ibuilder = IndividualBuilder(individual_type)
-    fitness_estimator = \
-        SurrogateEnabledComputableFitnessEstimator(
+
+    if fitness_estimator_type == "local" and surrogate_name:
+        fitness_estimator = SurrogateEnabledComputableFitnessEstimator(
+            ibuilder,
+            Surrogate(surrogate_name),
+            "type1",
+            SPEEDUP,
+            num_fitness_evaluations,
+            statistics_collector
+        )
+    elif fitness_estimator_type == "local":
+        fitness_estimator = ComputableFitnessEstimator(ibuilder, num_fitness_evaluations, statistics_collector)
+    elif fitness_estimator_type == "distributed" and surrogate_name:
+        fitness_estimator = DistributedSurrogateEnabledComputableFitnessEstimator(
             ibuilder,
             Surrogate(surrogate_name),
             "type1",
             SPEEDUP,
             num_fitness_evaluations,
             statistics_collector
-        ) if surrogate_name else ComputableFitnessEstimator(ibuilder, num_fitness_evaluations, statistics_collector)
+        )
+    elif fitness_estimator_type == "distributed":
+        fitness_estimator = ComputableFitnessEstimator(ibuilder, num_fitness_evaluations, statistics_collector)
+    else:
+        raise ValueError("Incorrect settings")
 
     g = GA(
         dataset=dataset,
@@ -174,7 +192,8 @@ def run_algorithm(
         use_nelder_mead_in_selector: bool = False,
         train_option: str = "offline",
         quiet_log: bool = False,
-        individual_type: str = "regular"
+        individual_type: str = "regular",
+        fitness_estimator_type: str = "local"
 ) -> TopicModel:
     best_individual = get_best_individual(dataset, data_path, exp_id, topic_count, num_individuals, num_iterations,
                                           num_fitness_evaluations, mutation_type, crossover_type, selection_type,
@@ -182,7 +201,8 @@ def run_algorithm(
                                           gpr_kernel, gpr_alpha, gpr_normalize_y, use_pipeline,
                                           use_nelder_mead_in_mutation, use_nelder_mead_in_crossover,
                                           use_nelder_mead_in_selector, train_option, quiet_log,
-                                          individual_type=individual_type)
+                                          individual_type=individual_type,
+                                          fitness_estimator_type=fitness_estimator_type)
 
     best_topic_model = fit_tm(
         preproc_data_path=data_path,
diff --git a/autotm/fitness/estimator.py b/autotm/fitness/estimator.py
index f4a4ac9..870e316 100644
--- a/autotm/fitness/estimator.py
+++ b/autotm/fitness/estimator.py
@@ -278,3 +278,27 @@ def __init__(self,
         if calc_scheme not in self.SUPPORTED_CALC_SCHEMES:
             raise ValueError(f"Unexpected surrogate scheme! {self.calc_scheme}")
         super().__init__(ibuilder, num_fitness_evaluations, statistics_collector)
+
+
+class DistributedSurrogateEnabledComputableFitnessEstimator(
+    DistributedComputableFitnessEstimator,
+    SurrogateEnabledFitnessEstimatorMixin
+):
+    def __init__(self,
+                 ibuilder: IndividualBuilder,
+                 surrogate: Surrogate,
+                 calc_scheme: str,
+                 speedup: bool = True,
+                 num_fitness_evaluations: Optional[int] = None,
+                 statistics_collector: Optional[StatisticsCollector] = None):
+        self.ibuilder = ibuilder
+        self.surrogate = surrogate
+        self.calc_scheme = calc_scheme
+        self.speedup = speedup
+
+        self.all_params: List[AbstractParams] = []
+        self.all_fitness: List[float] = []
+
+        if calc_scheme not in self.SUPPORTED_CALC_SCHEMES:
+            raise ValueError(f"Unexpected surrogate scheme! {self.calc_scheme}")
+        super().__init__(ibuilder, num_fitness_evaluations, statistics_collector)
diff --git a/tests/integration/test_fit_predict.py b/tests/integration/test_fit_predict.py
index e7a9c05..e98d54b 100644
--- a/tests/integration/test_fit_predict.py
+++ b/tests/integration/test_fit_predict.py
@@ -42,6 +42,7 @@ def test_fit_predict(pytestconfig):
             alg_params={
                 "num_iterations": 2,
                 "num_individuals": 4,
+                "use_pipeline": False,
                 "use_nelder_mead_in_mutation": False,
                 "use_nelder_mead_in_crossover": False,
                 "use_nelder_mead_in_selector": False,