diff --git a/mpiplayground.py b/mpiplayground.py
index 77fc6d0..7079146 100644
--- a/mpiplayground.py
+++ b/mpiplayground.py
@@ -1,81 +1,84 @@
 # DKFZ
 #
 #
 # Copyright (c) German Cancer Research Center,
 # Division of Medical Image Computing.
 # All rights reserved.
 #
 # This software is distributed WITHOUT ANY WARRANTY; without
 # even the implied warranty of MERCHANTABILITY or FITNESS FOR
 # A PARTICULAR PURPOSE.
 #
 # See LICENSE
 
 # A hyppopy minimal example optimizing a simple demo function f(x,y) = x**2+y**2
 
 # import the HyppopyProject class keeping track of inputs
+from hyppopy.BlackboxFunction import BlackboxFunction
 from hyppopy.HyppopyProject import HyppopyProject
+from hyppopy.solvers.RandomsearchSolver import RandomsearchSolver
 from hyppopy.solvers.GridsearchSolver import GridsearchSolver
 from hyppopy.solvers.MPISolverWrapper import MPISolverWrapper
 from hyppopy.MPIBlackboxFunction import MPIBlackboxFunction
 
 # To configure the Hyppopy solver we use a simple nested dictionary with two obligatory main sections,
 # hyperparameter and settings. The hyperparameter section defines your searchspace. Each hyperparameter
 # is again a dictionary with:
 #
 # - a domain ['categorical', 'uniform', 'normal', 'loguniform']
 # - the domain data [left bound, right bound] and
 # - a type of your domain ['str', 'int', 'float']
 #
 # The settings section has two subcategories, solver and custom. The first contains settings for the solver,
 # here 'max_iterations' - is the maximum number of iteration.
 #
 # The custom section allows defining custom parameter. An entry here is transformed to a member variable of the
 # HyppopyProject class. These can be useful when implementing new solver classes or for control your hyppopy script.
 # Here we use it as a solver switch to control the usage of our solver via the config. This means with the script
 # below your can try out every solver by changing use_solver to 'optunity', 'randomsearch', 'gridsearch',...
 # It can be used like so: project.custom_use_plugin (see below) If using the gridsearch solver, max_iterations is
 # ignored, instead each hyperparameter must specifiy a number of samples additionally to the range like so:
 # 'data': [0, 1, 100] which means sampling the space from 0 to 1 in 100 intervals.
 
 config = {
     "hyperparameter": {
         "x": {
             "domain": "normal",
             "data": [-10.0, 10.0],
             "type": float,
             "frequency": 10
         },
         "y": {
             "domain": "uniform",
             "data": [-10.0, 10.0],
             "type": float,
             "frequency": 10
         }
     },
     "max_iterations": 500
 }
 
 project = HyppopyProject(config=config)
 
 
 # The user defined loss function
-def my_loss_function(data, params):
+def my_loss_function(params):
     x = params['x']
     y = params['y']
     return x**2+y**3
 
 
+bb = BlackboxFunction(blackbox_func=my_loss_function)
 solver = MPISolverWrapper(GridsearchSolver(project))
-blackbox = MPIBlackboxFunction(blackbox_func=my_loss_function)
+blackbox = MPIBlackboxFunction(blackbox_func=bb)
 solver.blackbox = blackbox
 
 solver.run()
 
-df, best = solver.get_results()  # gather the results from the different processes.
+df, best = solver.get_results()
 
 if solver.is_master() is True:  # TODO: Find better solution. At the moment it is printed for every worker in the End.
     print("\n")
     print("*" * 100)
     print("Best Parameter Set:\n{}".format(best))
     print("*" * 100)