diff --git a/dmosopt/MOASMO.py b/dmosopt/MOASMO.py
index 7adf3ac..57da2e4 100644
--- a/dmosopt/MOASMO.py
+++ b/dmosopt/MOASMO.py
@@ -231,7 +231,7 @@ def epoch(
     N_resample = int(pop * pct)
 
     if Xinit is None:
-        Xinit, Yinit = yield
+        Xinit, Yinit, C = yield
 
     x = Xinit.copy().astype(np.float32)
     y = Yinit.copy().astype(np.float32)
@@ -337,7 +337,7 @@ def epoch(
             if sm is not None:
                 y_gen = sm.evaluate(x_gen)
             else:
-                _, y_gen = yield x_gen
+                _, y_gen, c_gen = yield x_gen
 
             try:
                 res = opt_gen.send(y_gen)
diff --git a/dmosopt/dmosopt.py b/dmosopt/dmosopt.py
index 82821a9..277dace 100644
--- a/dmosopt/dmosopt.py
+++ b/dmosopt/dmosopt.py
@@ -192,10 +192,12 @@ def _update_evals(self):
             y_completed = np.vstack([x.objectives for x in self.completed])
             n_objectives = y_completed.shape[0]
             y_predicted = np.vstack(
-                tuple(map(
-                    lambda x: [np.nan] * n_objectives if x is None else x,
-                    [x.prediction for x in self.completed],
-                ))
+                tuple(
+                    map(
+                        lambda x: [np.nan] * n_objectives if x is None else x,
+                        [x.prediction for x in self.completed],
+                    )
+                )
             )
 
             f_completed = None
@@ -341,12 +343,13 @@ def update_epoch(self, resample=False):
         else:
             x_gen = completed_evals[0]
             y_gen = completed_evals[1]
+            c_gen = completed_evals[4]
 
             try:
                 if isinstance(self.opt_gen, dict):
                     raise StopIteration(self.opt_gen)
                 else:
-                    item = self.opt_gen.send((x_gen, y_gen))
+                    item = self.opt_gen.send((x_gen, y_gen, c_gen))
             except StopIteration as ex:
                 if isinstance(self.opt_gen, GeneratorType):
                     self.opt_gen.close()
@@ -1146,6 +1149,15 @@ def run_epoch(self):
                         x_completed = completed_evals[0]
                         y_completed = completed_evals[1]
                         pred_completed = completed_evals[2]
+                        c_completed = completed_evals[4]
+                        if c_completed is not None:
+                            feasible = np.argwhere(np.all(c_completed > 0.0, axis=1))
+                            if len(feasible) > 0:
+                                feasible = feasible.ravel()
+                                x_completed = x_completed[feasible, :]
+                                y_completed = y_completed[feasible, :]
+                                pred_completed = pred_completed[feasible, :]
+
                         if x_completed.shape[0] > 0:
                             n_objectives = y_completed.shape[1]
                             mae = []
diff --git a/pyproject.toml b/pyproject.toml
index 4e72e9b..815d868 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "dmosopt"
-version = "0.45.2"
+version = "0.45.3"
 description = "Distributed multi-objective surrogate optimization algorithm."
 authors = ["Ivan Raikov <ivan.g.raikov@gmail.com>"]
 license = "GPL-3.0-or-later"