diff --git a/examples/tutorial_custom_visualization.py b/examples/tutorial_custom_visualization.py
index e9067c7..c4137b3 100644
--- a/examples/tutorial_custom_visualization.py
+++ b/examples/tutorial_custom_visualization.py
@@ -1,104 +1,101 @@
-import numpy as np
 import matplotlib.pylab as plt
 
 from hyppopy.HyppopyProject import HyppopyProject
 from hyppopy.VirtualFunction import VirtualFunction
 from hyppopy.BlackboxFunction import BlackboxFunction
 from hyppopy.solver.HyperoptSolver import HyperoptSolver
 from hyppopy.solver.OptunitySolver import OptunitySolver
 from hyppopy.solver.RandomsearchSolver import RandomsearchSolver
 
 project = HyppopyProject()
 project.add_hyperparameter(name="axis_00", domain="uniform", data=[0, 1], dtype="float")
 project.add_hyperparameter(name="axis_01", domain="uniform", data=[0, 800], dtype="float")
 project.add_hyperparameter(name="axis_02", domain="uniform", data=[0, 5], dtype="float")
 project.add_hyperparameter(name="axis_03", domain="uniform", data=[1, 10000], dtype="float")
 project.add_hyperparameter(name="axis_04", domain="uniform", data=[0, 10], dtype="float")
 project.add_settings(section="solver", name="max_iterations", value=500)
-project.add_settings(section="custom", name="use_solver", value="hyperopt")
+project.add_settings(section="custom", name="use_solver", value="randomsearch")
 
 plt.ion()
-fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(12, 8))
+fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(12, 8), sharey=True)
 plot_data = {"iterations": [],
              "loss": [],
              "axis_00": [],
              "axis_01": [],
              "axis_02": [],
              "axis_03": [],
              "axis_04": []}
 
 
 def my_visualization_function(**kwargs):
     print("\r{}".format(kwargs), end="")
     plot_data["iterations"].append(kwargs['iterations'])
     plot_data["loss"].append(kwargs['loss'])
     plot_data["axis_00"].append(kwargs['axis_00'])
     plot_data["axis_01"].append(kwargs['axis_01'])
     plot_data["axis_02"].append(kwargs['axis_02'])
     plot_data["axis_03"].append(kwargs['axis_03'])
     plot_data["axis_04"].append(kwargs['axis_04'])
 
     axes[0, 0].clear()
-    #axes[0, 0].plot(plot_data["loss"], plot_data["axis_00"], ".r")
     axes[0, 0].scatter(plot_data["axis_00"], plot_data["loss"], c=plot_data["loss"], cmap="jet", marker='.')
+    axes[0, 0].set_ylabel("loss")
     axes[0, 0].set_xlabel("axis_00")
 
     axes[0, 1].clear()
-    #axes[0, 1].plot(plot_data["loss"], plot_data["axis_01"], ".r")
     axes[0, 1].scatter(plot_data["axis_01"], plot_data["loss"], c=plot_data["loss"], cmap="jet", marker='.')
     axes[0, 1].set_xlabel("axis_01")
 
     axes[0, 2].clear()
-    #axes[0, 2].plot(plot_data["loss"], plot_data["axis_02"], ".r")
     axes[0, 2].scatter(plot_data["axis_02"], plot_data["loss"], c=plot_data["loss"], cmap="jet", marker='.')
     axes[0, 2].set_xlabel("axis_02")
 
     axes[1, 0].clear()
-    #axes[1, 0].plot(plot_data["loss"], plot_data["axis_03"], ".r")
     axes[1, 0].scatter(plot_data["axis_03"], plot_data["loss"], c=plot_data["loss"], cmap="jet", marker='.')
-    #axes[1, 0].set_xlabel("loss")
+    axes[1, 0].set_ylabel("loss")
     axes[1, 0].set_xlabel("axis_03")
 
     axes[1, 1].clear()
-    #axes[1, 1].plot(plot_data["loss"], plot_data["axis_04"], ".r")
     axes[1, 1].scatter(plot_data["axis_04"], plot_data["loss"], c=plot_data["loss"], cmap="jet", marker='.')
-    #axes[1, 1].set_xlabel("loss")
     axes[1, 1].set_xlabel("axis_04")
 
     axes[1, 2].clear()
+    axes[1, 2].plot(plot_data["iterations"], plot_data["loss"], "--", c=(0.8, 0.8, 0.8, 0.5))
     axes[1, 2].scatter(plot_data["iterations"], plot_data["loss"], marker='.', c=(0.2, 0.2, 0.2))
-    axes[1, 2].plot(plot_data["iterations"], plot_data["loss"], "-", c=(0.8, 0.8, 0.8, 0.5))
-    axes[1, 2].set_ylabel("loss")
     axes[1, 2].set_xlabel("iterations")
 
     plt.draw()
     plt.tight_layout()
     plt.pause(0.001)
 
 
 def my_loss_function(data, params):
     vfunc = VirtualFunction()
     vfunc.load_default(5)
     return vfunc(**params)
 
 
 blackbox = BlackboxFunction(data=[],
                             blackbox_func=my_loss_function,
                             callback_func=my_visualization_function)
 
 if project.custom_use_solver == "hyperopt":
     solver = HyperoptSolver(project)
 elif project.custom_use_solver == "optunity":
     solver = OptunitySolver(project)
 elif project.custom_use_solver == "randomsearch":
     solver = RandomsearchSolver(project)
 
 if solver is not None:
     solver.blackbox = blackbox
 solver.run()
 df, best = solver.get_results()
 
 print("\n")
 print("*" * 100)
 print("Best Parameter Set:\n{}".format(best))
-print("*" * 100)
\ No newline at end of file
+print("*" * 100)
+print("")
+save_plot = input("Save Plot? [y/n] ")
+if save_plot == "y":
+    plt.savefig('plot_{}.png'.format(project.custom_use_solver))