diff --git a/mlxtend/feature_selection/exhaustive_feature_selector.py b/mlxtend/feature_selection/exhaustive_feature_selector.py
index 523c4ba19..7dc402a7b 100644
--- a/mlxtend/feature_selection/exhaustive_feature_selector.py
+++ b/mlxtend/feature_selection/exhaustive_feature_selector.py
@@ -169,8 +169,8 @@ class ExhaustiveFeatureSelector(BaseEstimator, MetaEstimatorMixin):
     def __init__(
         self,
         estimator,
-        min_features=1,
-        max_features=1,
+        min_features=None,
+        max_features=None,
         print_progress=True,
         scoring="accuracy",
         cv=5,
@@ -179,11 +179,13 @@ def __init__(
         clone_estimator=True,
         fixed_features=None,
         feature_groups=None,
+        feature_range = None
     ):
         self.estimator = estimator
         self.min_features = min_features
         self.max_features = max_features
         self.pre_dispatch = pre_dispatch
+        self.feature_range = feature_range
         # Want to raise meaningful error message if a
         # cross-validation generator is inputted
         if isinstance(cv, types.GeneratorType):
@@ -401,8 +403,26 @@ def fit(self, X, y, groups=None, **fit_params):
 
         # candidates in the following lines are the non-fixed-features candidates
         # (the fixed features will be added later to each combination)
-        min_num_candidates = self.min_features - len(self.fixed_features_group_set)
-        max_num_candidates = self.max_features - len(self.fixed_features_group_set)
+        
+        if self.min_features == None and self.max_features == None:
+            if self.feature_range == None:
+                min_num_candidates = 1
+                max_num_candidates = 1
+            elif self.feature_range == "all":
+                min_num_candidates = 1
+                max_num_candidates = X.shape[1]
+            else:
+                try:
+                    min_num_candidates = self.feature_range[0]
+                    max_num_candidates = self.feature_range[1]
+                except ValueError:
+                    raise ValueError(
+                        """feature_range should be of tuple type. First argument should be
+                        minimum number of feature and second argument shoulf be maximum number of feature"""
+                    )
+        else:
+            min_num_candidates = self.min_features - len(self.fixed_features_group_set)
+            max_num_candidates = self.max_features - len(self.fixed_features_group_set)
         candidates = chain.from_iterable(
             combinations(non_fixed_groups, r=i)
             for i in range(min_num_candidates, max_num_candidates + 1)