human
Complete this python program:
def harm(population, toolbox, cxpb, mutpb, ngen, alpha, beta, gamma, rho, nbrindsmodel=(-1), mincutoff=20, stats=None, halloffame=None, verbose=__debug__):
assistant
#Here is the Python code you requested.

    """Implement bloat control on a GP evolution using HARM-GP, as defined in
    [Gardner2015]. It is implemented in the form of an evolution algorithm
    (similar to :func:`~deap.algorithms.eaSimple`).
    :param population: A list of individuals.
    :param toolbox: A :class:`~deap.base.Toolbox` that contains the evolution
    operators.
    :param cxpb: The probability of mating two individuals.
    :param mutpb: The probability of mutating an individual.
    :param ngen: The number of generation.
    :param alpha: The HARM *alpha* parameter.
    :param beta: The HARM *beta* parameter.
    :param gamma: The HARM *gamma* parameter.
    :param rho: The HARM *rho* parameter.
    :param nbrindsmodel: The number of individuals to generate in order to
    model the natural distribution. -1 is a special
    value which uses the equation proposed in
    [Gardner2015] to set the value of this parameter :
    max(2000, len(population))
    :param mincutoff: The absolute minimum value for the cutoff point. It is
    used to ensure that HARM does not shrink the population
    too much at the beginning of the evolution. The default
    value is usually fine.
    :param stats: A :class:`~deap.tools.Statistics` object that is updated
    inplace, optional.
    :param halloffame: A :class:`~deap.tools.HallOfFame` object that will
    contain the best individuals, optional.
    :param verbose: Whether or not to log the statistics.
    :returns: The final population
    :returns: A class:`~deap.tools.Logbook` with the statistics of the
    evolution
    This function expects the :meth:`toolbox.mate`, :meth:`toolbox.mutate`,
    :meth:`toolbox.select` and :meth:`toolbox.evaluate` aliases to be
    registered in the toolbox.
    .. note::
    The recommended values for the HARM-GP parameters are *alpha=0.05*,
    *beta=10*, *gamma=0.25*, *rho=0.9*. However, these parameters can be
    adjusted to perform better on a specific problem (see the relevant
    paper for tuning information). The number of individuals used to
    model..."""
    def _genpop(n, pickfrom=[], acceptfunc=(lambda s: True), producesizes=False):
        producedpop = []
        producedpopsizes = []
        while (len(producedpop) < n):
            if (len(pickfrom) > 0):
                aspirant = pickfrom.pop()
                if acceptfunc(len(aspirant)):
                    producedpop.append(aspirant)
                    if producesizes:
                        producedpopsizes.append(len(aspirant))
            else:
                opRandom = random.random()
                if (opRandom < cxpb):
                    (aspirant1, aspirant2) = toolbox.mate(*map(toolbox.clone, toolbox.select(population, 2)))
                    del aspirant1.fitness.values, aspirant2.fitness.values
                    if acceptfunc(len(aspirant1)):
                        producedpop.append(aspirant1)
                        if producesizes:
                            producedpopsizes.append(len(aspirant1))
                    if ((len(producedpop) < n) and acceptfunc(len(aspirant2))):
                        producedpop.append(aspirant2)
                        if producesizes:
                            producedpopsizes.append(len(aspirant2))
                else:
                    aspirant = toolbox.clone(toolbox.select(population, 1)[0])
                    if ((opRandom - cxpb) < mutpb):
                        aspirant = toolbox.mutate(aspirant)[0]
                        del aspirant.fitness.values
                    if acceptfunc(len(aspirant)):
                        producedpop.append(aspirant)
                        if producesizes:
                            producedpopsizes.append(len(aspirant))
        if producesizes:
            return (producedpop, producedpopsizes)
        else:
            return producedpop
    def halflifefunc(x):
        return ((x * float(alpha)) + beta)
    if (nbrindsmodel == (-1)):
        nbrindsmodel = max(2000, len(population))
    logbook = tools.Logbook()
    logbook.header = (['gen', 'nevals'] + (stats.fields if stats else []))
 ...