def setMultiProcessing(self, flag=True, full_copy=False, max_processes=None):
""" Sets the flag to enable/disable the use of python multiprocessing module.
Use this option when you have more than one core on your CPU and when your
evaluation function is very slow.
Pyevolve will automaticly check if your Python version has **multiprocessing**
support and if you have more than one single CPU core. If you don't have support
or have just only one core, Pyevolve will not use the **multiprocessing**
feature.
Pyevolve uses the **multiprocessing** to execute the evaluation function over
the individuals, so the use of this feature will make sense if you have a
truly slow evaluation function (which is commom in GAs).
The parameter "full_copy" defines where the individual data should be copied back
after the evaluation or not. This parameter is useful when you change the
individual in the evaluation function.
:param flag: True (default) or False
:param full_copy: True or False (default)
:param max_processes: None (default) or an integer value
.. warning:: Use this option only when your evaluation function is slow, so you'll
get a good tradeoff between the process communication speed and the
parallel evaluation. The use of the **multiprocessing** doesn't means
always a better performance.
.. note:: To enable the multiprocessing option, you **MUST** add the *__main__* check
on your application, otherwise, it will result in errors. See more on the
`Python Docs <http://docs.python.org/library/multiprocessing.html#multiprocessing-programming>`__
site.
.. versionadded:: 0.6
The `setMultiProcessing` method.
"""
if type(flag) != BooleanType:
utils.raiseException("Multiprocessing option must be True or False", TypeError)
if type(full_copy) != BooleanType:
utils.raiseException("Multiprocessing 'full_copy' option must be True or False", TypeError)
self.internalPop.setMultiProcessing(flag, full_copy, max_processes)
# -----------------------------------------------------------------
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