diff --git a/few/few.py b/few/few.py
index a41bd23..7601c63 100644
--- a/few/few.py
+++ b/few/few.py
@@ -53,7 +53,7 @@ def __init__(self, population_size=50, generations=100,
                  mutation_rate=0.5, crossover_rate=0.5,
                  ml = None, min_depth = 1, max_depth = 2, max_depth_init = 2,
                  sel = 'epsilon_lexicase', tourn_size = 2, fit_choice = None,
-                 op_weight = False, seed_with_ml = True, erc = False,
+                 op_weight = False, max_stall=10, seed_with_ml = True, erc = False,
                  random_state=np.random.randint(9999999), verbosity=0,
                  scoring_function=None, disable_update_check=False,
                  elitism=True, boolean = False,classification=False,clean=False,
@@ -90,6 +90,7 @@ def __init__(self, population_size=50, generations=100,
         self.tourn_size = tourn_size
         self.fit_choice = fit_choice
         self.op_weight = op_weight
+        self.max_stall = max_stall
         self.seed_with_ml = seed_with_ml
         self.erc = erc
         self.random_state = random_state
@@ -272,8 +273,11 @@ def fit(self, features, labels):
         # progress bar
         pbar = tqdm(total=self.generations,disable = self.verbosity==0,
                     desc='Internal CV: {:1.3f}'.format(self._best_score))
+        stall_count = 0
         # for each generation g
         for g in np.arange(self.generations):
+            if stall_count == self.max_stall:
+                break;
 
             if self.track_diversity:
                 self.get_diversity(self.X)
@@ -344,9 +348,12 @@ def fit(self, features, labels):
             if self.valid_loc() and tmp_score > self._best_score:
                 self._best_estimator = copy.deepcopy(self.ml)
                 self._best_score = tmp_score
+                stall_count = 0;
                 self._best_inds = copy.deepcopy(self.valid())
                 if self.verbosity > 1:
                     print("updated best internal CV:",self._best_score)
+            else:
+                stall_count = stall_count + 1
 
             # Variation
             if self.verbosity > 2:
@@ -808,6 +815,10 @@ def main():
                         type=bool, help='Weight attributes for incuded in'
                         ' features based on ML scores. Default: off')
 
+    parser.add_argument('-ms', action='store', dest='MAX_STALL',default=10,
+                         help='The number of iterations to do when the best'
+                         ' score is not improving before taking the best score value')
+
     parser.add_argument('-sel', action='store', dest='SEL',
                         default='epsilon_lexicase',
                         choices = ['tournament','lexicase','epsilon_lexicase',
@@ -928,6 +939,7 @@ def main():
                   min_depth = args.MIN_DEPTH,max_depth = args.MAX_DEPTH,
                   sel = args.SEL, tourn_size = args.TOURN_SIZE,
                   seed_with_ml = args.SEED_WITH_ML, op_weight = args.OP_WEIGHT,
+                  max_stall = args.MAX_STALL,
                   erc = args.ERC, random_state=args.RANDOM_STATE,
                   verbosity=args.VERBOSITY,
                   disable_update_check=args.DISABLE_UPDATE_CHECK,