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def varOr(toolbox, population, lambda_, cxpb, mutpb):
    assert (cxpb + mutpb) <= 1.0, ("The sum of the crossover and mutation "
        "probabilities must be smaller or equal to 1.0.")
    
    ncx, nmut, nrep = numpy.random.multinomial(lambda_, (cxpb, mutpb, 1-cxpb-mutpb))
    offcx = map(toolbox.clone, tools.selRandom(population, 2*ncx))
    offmut = map(toolbox.clone, tools.selRandom(population, nmut))
    offrep = map(toolbox.clone, tools.selRandom(population, nrep))

    for ind1, ind2 in offcx:

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def eaGenerateUpdate(toolbox, ngen, halloffame=None, stats=None, verbose=True):
    """The CMA-ES algorithm as described in Hansen, N. (2006). *The CMA
    Evolution Strategy: A Comparing Rewiew.*
    
    :param toolbox: A :class:`~deap.base.Toolbox` that contains the evolution
                    operators.
    :param ngen: The number of generation.
    :param stats: A :class:`~deap.tools.Statistics` object that is updated
                  inplace, optional.
    :param halloffame: A :class:`~deap.tools.HallOfFame` object that will

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class Strategy(base.Toolbox):
    """
    A strategy that will keep track of the basic parameters of the CMA-ES
    algorithm.
    
    :param centroid: An iterable object that indicates where to start the
                     evolution.
    :param sigma: The list of initial standard deviations of the distribution,
                  it shall be the same length than the centroid.
    :param parameter: One or more parameter to pass to the strategy as

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import numpy

from deap import algorithms
from deap import base
from deap import benchmarks
from deap import cma
from deap import creator
from deap import tools

# Problem size

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#    This file is part of DEAP.
#
#    DEAP is free software: you can redistribute it and/or modify
#    it under the terms of the GNU Lesser General Public License as
#    published by the Free Software Foundation, either version 3 of
#    the License, or (at your option) any later version.
#
#    DEAP is distributed in the hope that it will be useful,
#    but WITHOUT ANY WARRANTY; without even the implied warranty of
#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the

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#    This file is part of DEAP.
#
#    DEAP is free software: you can redistribute it and/or modify
#    it under the terms of the GNU Lesser General Public License as
#    published by the Free Software Foundation, either version 3 of
#    the License, or (at your option) any later version.
#
#    DEAP is distributed in the hope that it will be useful,
#    but WITHOUT ANY WARRANTY; without even the implied warranty of
#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the

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import gp
import random

__type__ = None

class PrimitiveTree(list):
    def __init__(self, content):
        self[:] = content

    @property

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#    This file is part of EAP.
#
#    EAP is free software: you can redistribute it and/or modify
#    it under the terms of the GNU Lesser General Public License as
#    published by the Free Software Foundation, either version 3 of
#    the License, or (at your option) any later version.
#
#    EAP is distributed in the hope that it will be useful,
#    but WITHOUT ANY WARRANTY; without even the implied warranty of
#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the

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#    This file is part of DEAP.
#
#    DEAP is free software: you can redistribute it and/or modify
#    it under the terms of the GNU Lesser General Public License as
#    published by the Free Software Foundation, either version 3 of
#    the License, or (at your option) any later version.
#
#    DEAP is distributed in the hope that it will be useful,
#    but WITHOUT ANY WARRANTY; without even the implied warranty of
#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the

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import array
import pycec2013

from scoop import futures

if __name__ == "__main__":
  	bench = pycec2013.Benchmark(1)
	pop = [array.array('d', [1.0]), array.array('d', [2.0]), array.array('d', [3.0])]
	print(list(futures.map(bench.evaluate, pop)))
	# print(list(map(bench.evaluate, pop)))