diff --git a/hyppopy/plugins/hyperopt_settings_plugin.py b/hyppopy/plugins/hyperopt_settings_plugin.py
index a188ef6..9aad0ac 100644
--- a/hyppopy/plugins/hyperopt_settings_plugin.py
+++ b/hyppopy/plugins/hyperopt_settings_plugin.py
@@ -1,103 +1,105 @@
 # DKFZ
 #
 #
 # Copyright (c) German Cancer Research Center,
 # Division of Medical and Biological Informatics.
 # 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.txt or http://www.mitk.org for details.
 #
 # Author: Sven Wanner (s.wanner@dkfz.de)
 
 import os
 import logging
 import numpy as np
 from hyppopy.globals import DEBUGLEVEL
 LOG = logging.getLogger(os.path.basename(__file__))
 LOG.setLevel(DEBUGLEVEL)
 
 from pprint import pformat
 
 try:
     from hyperopt import hp
     from yapsy.IPlugin import IPlugin
 except:
     LOG.warning("hyperopt package not installed, will ignore this plugin!")
     print("hyperopt package not installed, will ignore this plugin!")
 
 from hyppopy.settingspluginbase import SettingsPluginBase
 from hyppopy.settingsparticle import SettingsParticle
 
 
 class hyperopt_Settings(SettingsPluginBase, IPlugin):
 
     def __init__(self):
         SettingsPluginBase.__init__(self)
         LOG.debug("initialized")
 
     def convert_parameter(self, input_dict):
         LOG.debug("convert input parameter\n\n\t{}\n".format(pformat(input_dict)))
 
         solution_space = {}
         for name, content in input_dict.items():
             particle = hyperopt_SettingsParticle(name=name)
             for key, value in content.items():
                 if key == 'domain':
                     particle.domain = value
                 elif key == 'data':
                     particle.data = value
                 elif key == 'type':
                     particle.dtype = value
             solution_space[name] = particle.get()
         return solution_space
 
 
 class hyperopt_SettingsParticle(SettingsParticle):
 
     def __init__(self, name=None, domain=None, dtype=None, data=None):
         SettingsParticle.__init__(self, name, domain, dtype, data)
 
     def convert(self):
         if self.domain == "uniform":
             if self.dtype == "float" or self.dtype == "double":
                 return hp.uniform(self.name, self.data[0], self.data[1])
             elif self.dtype == "int":
                 data = list(np.arange(int(self.data[0]), int(self.data[1]+1)))
                 return hp.choice(self.name, data)
             else:
                 msg = "cannot convert the type {} in domain {}".format(self.dtype, self.domain)
                 LOG.error(msg)
                 raise LookupError(msg)
         elif self.domain == "loguniform":
             if self.dtype == "float" or self.dtype == "double":
                 return hp.loguniform(self.name, self.data[0], self.data[1])
             else:
                 msg = "cannot convert the type {} in domain {}".format(self.dtype, self.domain)
                 LOG.error(msg)
                 raise LookupError(msg)
         elif self.domain == "normal":
             if self.dtype == "float" or self.dtype == "double":
-                return hp.normal(self.name, self.data[0], self.data[1])
+                mu = (self.data[1] - self.data[0])/2.0
+                sigma = mu/3
+                return hp.normal(self.name, mu, sigma)
             else:
                 msg = "cannot convert the type {} in domain {}".format(self.dtype, self.domain)
                 LOG.error(msg)
                 raise LookupError(msg)
         elif self.domain == "categorical":
             if self.dtype == 'str':
                 return hp.choice(self.name, self.data)
             elif self.dtype == 'bool':
                 data = []
                 for elem in self.data:
                     if elem == "true" or elem == "True" or elem == 1 or elem == "1":
                         data .append(True)
                     elif elem == "false" or elem == "False" or elem == 0 or elem == "0":
                         data .append(False)
                     else:
                         msg = "cannot convert the type {} in domain {}, unknown bool type value".format(self.dtype, self.domain)
                         LOG.error(msg)
                         raise LookupError(msg)
                 return hp.choice(self.name, data)
diff --git a/setup.py b/setup.py
index feb0e7d..2486574 100644
--- a/setup.py
+++ b/setup.py
@@ -1,63 +1,63 @@
 # -*- coding: utf-8 -*-
 
 import os
 from setuptools import setup, find_packages
 
 with open('README.rst') as f:
     readme = f.read()
 
 with open('LICENSE') as f:
     license = f.read()
 
-VERSION = "0.3.0"
+VERSION = "0.3.1"
 
 ROOT = os.path.dirname(os.path.realpath(__file__))
 
 new_init = []
 with open(os.path.join(ROOT, *("hyppopy", "__init__.py")), "r") as infile:
 	for line in infile:
 		new_init.append(line)
 for n in range(len(new_init)):
 	if new_init[n].startswith("__version__"):
 		split = line.split("=")
 		new_init[n] = "__version__ = '" + VERSION + "'\n"
 with open(os.path.join(ROOT, *("hyppopy", "__init__.py")), "w") as outfile:
 	outfile.writelines(new_init)
 
 
 setup(
     name='hyppopy',
     version=VERSION,
     description='Hyper-Parameter Optimization Toolbox for Blackboxfunction Optimization',
     long_description=readme,
     # if you want, put your own name here
     # (this would likely result in people sending you emails)
     author='Sven Wanner',
     author_email='s.wanner@dkfz.de',
     url='',
     license=license,
     packages=find_packages(exclude=('*test*', 'doc')),
 	package_data={
 	   'hyppopy.plugins': ['*.yapsy-plugin']
     },
     # the requirements to install this project.
     # Since this one is so simple this is empty.
     install_requires=[
 	'dicttoxml>=1.7.4',
 	'xmltodict>=0.11.0',
 	'hyperopt>=0.1.1',
 	'Optunity>=1.1.1',
 	'numpy>=1.16.0',
 	'matplotlib>=3.0.2',
 	'scikit-learn>=0.20.2',
 	'scipy>=1.2.0',
 	'Sphinx>=1.8.3',
 	'xmlrunner>=1.7.7',
 	'Yapsy>=1.11.223',
 	'pandas>=0.24.1',
 	'seaborn>=0.9.0',
 	'deap>=1.2.2',
 	'bayesian-optimization>=1.0.1'
 	],
 )