diff --git a/README.md b/README.md
index 16b11cb..2f14c63 100644
--- a/README.md
+++ b/README.md
@@ -352,8 +352,9 @@ The configuration parameters are:
       ...
       return True/False
   ```
-Early stopping is one of Mango's important features that allow to early terminate the current parallel search based on the custom user-designed criteria, such as the total optimization time spent, current validation accuracy achieved, or improvements in the past few iterations. For usage see early stopping examples [notebook](https://github.com/ARM-software/mango/blob/master/examples/EarlyStopping.ipynb).
+  Early stopping is one of Mango's important features that allow to early terminate the current parallel search based on the custom user-designed criteria, such as the total optimization time spent, current validation accuracy achieved, or improvements in the past few iterations. For usage see early stopping examples [notebook](https://github.com/ARM-software/mango/blob/master/examples/EarlyStopping.ipynb).
 
+- initial_custom: A list of initial evaluation points to warm up the optimizer instead of random sampling. For example, for a search space with two parameters `x1` and `x2` the input could be:   `[{'x1': 10, 'x2': -5}, {'x1': 0, 'x2': 10}]`. This allows the user to customize the initial evaluation points and therefore guide the optimization process. If this option is given then `initial_random` is ignored.  
 
 The default configuration parameters can be modified, as shown below. Only the parameters whose values need to adjusted can be passed as the dictionary.
 
diff --git a/mango/tuner.py b/mango/tuner.py
index 360a859..97f02a1 100644
--- a/mango/tuner.py
+++ b/mango/tuner.py
@@ -29,6 +29,7 @@ class Tuner:
     class Config:
         domain_size: int = None
         initial_random: int = 2
+        initial_custom: dict = None
         num_iteration: int = 20
         batch_size: int = 1
         optimizer: str = 'Bayesian'
@@ -151,25 +152,35 @@ class Tuner:
         self.maximize_objective = False
         return self.run()
 
+
+    def run_initial(self):
+        if self.config.initial_custom is not None:
+            X_tried = copy.deepcopy(self.config.initial_custom)
+            X_list, Y_list = self.runUserObjective(X_tried)
+        else:
+            # getting first few random values
+            X_tried = self.ds.get_random_sample(self.config.initial_random)
+            X_list, Y_list = self.runUserObjective(X_tried)
+
+            # in case initial random results are invalid try different samples
+            n_tries = 1
+            while len(Y_list) < self.config.initial_random and n_tries < 3:
+                X_tried2 = self.ds.get_random_sample(self.config.initial_random - len(Y_list))
+                X_list2, Y_list2 = self.runUserObjective(X_tried2)
+                X_tried2.extend(X_tried2)
+                X_list = np.append(X_list, X_list2)
+                Y_list = np.append(Y_list, Y_list2)
+                n_tries += 1
+
+            if len(Y_list) == 0:
+                raise ValueError("No valid configuration found to initiate the Bayesian Optimizer")
+        return X_list, Y_list, X_tried
+
     def runBayesianOptimizer(self):
         results = dict()
 
-        # getting first few random values
-        random_hyper_parameters = self.ds.get_random_sample(self.config.initial_random)
-        X_list, Y_list = self.runUserObjective(random_hyper_parameters)
-
-        # in case initial random results are invalid try different samples
-        n_tries = 1
-        while len(Y_list) < self.config.initial_random and n_tries < 3:
-            random_hps = self.ds.get_random_sample(self.config.initial_random - len(Y_list))
-            X_list2, Y_list2 = self.runUserObjective(random_hps)
-            random_hyper_parameters.extend(random_hps)
-            X_list = np.append(X_list, X_list2)
-            Y_list = np.append(Y_list, Y_list2)
-            n_tries += 1
+        X_list, Y_list, X_tried = self.run_initial()
 
-        if len(Y_list) == 0:
-            raise ValueError("No valid configuration found to initiate the Bayesian Optimizer")
 
         # evaluated hyper parameters are used
         X_init = self.ds.convert_GP_space(X_list)
@@ -186,7 +197,7 @@ class Tuner:
         X_sample = X_init
         Y_sample = Y_init
 
-        hyper_parameters_tried = random_hyper_parameters
+        hyper_parameters_tried = X_tried
         objective_function_values = Y_list
         surrogate_values = Y_list
 

diff --git a/tests/test_tuner.py b/tests/test_tuner.py
index 24e7c99..98e5fbd 100644
--- a/tests/test_tuner.py
+++ b/tests/test_tuner.py
@@ -14,7 +14,6 @@ import numpy as np
 from mango.domain.domain_space import domain_space
 from mango import Tuner, scheduler
 from scipy.stats import uniform
-from mango.domain.distribution import loguniform
 
 # Simple param_dict
 param_dict = {"a": uniform(0, 1),  # uniform distribution
@@ -125,7 +124,7 @@ def test_rosenbrock():
             results.append(result)
         return results
 
-    tuner = Tuner(param_dict, objfunc, conf_dict=dict(domain_size=100000))
+    tuner = Tuner(param_dict, objfunc, conf_dict=dict(domain_size=100000, num_iteration=40))
     results = tuner.run()
 
     print('best hyper parameters:', results['best_params'])
@@ -190,6 +189,40 @@ def test_convex():
     assert abs(results['best_params']['y'] - y_opt) <= 3
 
 
+def test_initial_custom():
+    param_dict = {
+        'x': range(-100, 10),
+        'y': range(-10, 20),
+    }
+
+    x_opt = 0
+    y_opt = 0
+
+    def objfunc(args_list):
+        results = []
+        for hyper_par in args_list:
+            x = hyper_par['x']
+            y = hyper_par['y']
+            result = (x ** 2 + y ** 2) / 1e4
+            results.append(result)
+        return results
+
+    config = dict(initial_custom=[dict(x=-100, y=20),
+                                 dict(x=10, y=20)]
+                 )
+
+    tuner = Tuner(param_dict, objfunc, conf_dict=config)
+    results = tuner.minimize()
+
+    print('best hyper parameters:', results['best_params'])
+    print('best Accuracy:', results['best_objective'])
+
+    assert abs(results['best_params']['x'] - x_opt) <= 3
+    assert abs(results['best_params']['y'] - y_opt) <= 3
+    assert results['random_params'][0] == config['initial_custom'][0]
+    assert results['random_params'][1] == config['initial_custom'][1]
+
+
 def test_local_scheduler():
     param_space = dict(x=range(-10, 10),
                        y=range(-10, 10))

Is it possible to add an initial data point?
The way Im currently using mango, I will always have a first run with good defaults. Is it possible to use this information somehow? I have quite wide ranges for my hyper parameters, and I think this would help a lot.
tests/test_tuner.py::test_initial_custom
tests/test_tuner.py::test_domain tests/test_tuner.py::test_tuner tests/test_tuner.py::test_rosenbrock tests/test_tuner.py::test_config tests/test_tuner.py::test_convex tests/test_tuner.py::test_six_hump tests/test_tuner.py::test_celery_scheduler tests/test_tuner.py::test_custom_scheduler tests/test_tuner.py::test_early_stopping_simple tests/test_tuner.py::test_early_stopping_complex