diff --git a/gplearn/genetic.py b/gplearn/genetic.py
index 10f3e570..6a57672e 100644
--- a/gplearn/genetic.py
+++ b/gplearn/genetic.py
@@ -481,7 +481,11 @@ def fit(self, X, y, sample_weight=None):
         if isinstance(self, TransformerMixin):
             # Find the best individuals in the final generation
             fitness = np.array(fitness)
-            hall_of_fame = fitness.argsort()[:self.hall_of_fame]
+            if self._metric.greater_is_better:
+                hall_of_fame = fitness.argsort()[:self.hall_of_fame]
+            else:
+                hall_of_fame = fitness.argsort()[(self.population_size -
+                                                  self.hall_of_fame):]
             evaluation = np.array([gp.execute(X) for gp in
                                    [self._programs[-1][i] for
                                     i in hall_of_fame]])
diff --git a/gplearn/tests/test_genetic.py b/gplearn/tests/test_genetic.py
index f0639252..8a83a6f5 100644
--- a/gplearn/tests/test_genetic.py
+++ b/gplearn/tests/test_genetic.py
@@ -1022,14 +1022,16 @@ def test_warm_start():
     assert_equal(cold_program, warm_program)
 
 
-def test_customizied_regressor_metrics():
+def test_customized_regressor_metrics():
     """Check whether parameter greater_is_better works fine"""
 
     x_data = rng.uniform(-1, 1, 100).reshape(50, 2)
     y_true = x_data[:, 0] ** 2 + x_data[:, 1] ** 2
 
-    est_gp = SymbolicRegressor(metric='mean absolute error', stopping_criteria=0.000001, random_state=415,
-                               parsimony_coefficient=0.001, verbose=0, init_method='full', init_depth=(2, 4))
+    est_gp = SymbolicRegressor(metric='mean absolute error',
+                               stopping_criteria=0.000001, random_state=415,
+                               parsimony_coefficient=0.001, init_method='full',
+                               init_depth=(2, 4))
     est_gp.fit(x_data, y_true)
     formula = est_gp.__str__()
     assert_equal("add(mul(X1, X1), mul(X0, X0))", formula, True)
@@ -1037,13 +1039,15 @@ def test_customizied_regressor_metrics():
     def neg_mean_absolute_error(y, y_pred, sample_weight):
         return -1 * mean_absolute_error(y, y_pred, sample_weight)
 
-    customizied_fitness = make_fitness(neg_mean_absolute_error, greater_is_better=True)
+    customizied_fitness = make_fitness(neg_mean_absolute_error,
+                                       greater_is_better=True)
 
-    c_est_gp = SymbolicRegressor(metric=customizied_fitness, stopping_criteria=-0.000001, random_state=415,
-                                 parsimony_coefficient=0.001, verbose=0, init_method='full', init_depth=(2, 4))
+    c_est_gp = SymbolicRegressor(metric=customizied_fitness,
+                                 stopping_criteria=-0.000001, random_state=415,
+                                 parsimony_coefficient=0.001, verbose=0,
+                                 init_method='full', init_depth=(2, 4))
     c_est_gp.fit(x_data, y_true)
     c_formula = c_est_gp.__str__()
-
     assert_equal("add(mul(X1, X1), mul(X0, X0))", c_formula, True)