class_docstr: Multiobjective CMA-ES strategy based on the paper [Voss2010]_. It
  is used similarly as the standard CMA-ES strategy with a generate-update
  scheme.
  
  :param population: An initial population of individual.
  :param sigma: The initial step size of the complete system.
  :param mu: The number of parents to use in the evolution. When not
             provided it defaults to the length of *population*. (optional)
  :param lambda_: The number of offspring to produce at each generation.
                  (optional, defaults to 1)
  :param indicator: The indicator function to use. (optional, default to
                    :func:`~deap.tools.hypervolume`)
  
  Other parameters can be provided as described in the next table
  
  +----------------+---------------------------+----------------------------+
  | Parameter      | Default                   | Details                    |
  +================+===========================+============================+
  | ``d``          | ``1.0 + N / 2.0``         | Damping for step-size.     |
  +----------------+---------------------------+----------------------------+
  | ``ptarg``      | ``1.0 / (5 + 1.0 / 2.0)`` | Target success rate.       |
  +----------------+---------------------------+----------------------------+
  | ``cp``         | ``ptarg / (2.0 + ptarg)`` | Step size learning rate.   |
  +----------------+---------------------------+----------------------------+
  | ``cc``         | ``2.0 / (N + 2.0)``       | Cumulation time horizon.   |
  +----------------+---------------------------+----------------------------+
  | ``ccov``       | ``2.0 / (N**2 + 6.0)``    | Covariance matrix learning |
  |                |                           | rate.                      |
  +----------------+---------------------------+----------------------------+
  | ``pthresh``    | ``0.44``                  | Threshold success rate.    |
  +----------------+---------------------------+----------------------------+
  
  .. [Voss2010] Voss, Hansen, Igel, "Improved Step Size Adaptation
     for the MO-CMA-ES", 2010.
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code: def _rankOneUpdate(self, invCholesky, A, alpha, beta, v):
      w = numpy.dot(invCholesky, v)
  
      # Under this threshold, the update is mostly noise
      if w.max() > 1e-20:
          w_inv = numpy.dot(w, invCholesky)
          norm_w2 = numpy.sum(w**2)
          a = sqrt(alpha)
          root = numpy.sqrt(1 + beta / alpha * norm_w2)
          b = a / norm_w2 * (root - 1)
  
          A = a * A + b * numpy.outer(v, w)
          invCholesky = 1.0 / a * invCholesky - b / (
              a**2 + a * b * norm_w2
          ) * numpy.outer(w, w_inv)
  
      return invCholesky, A
  
imports: import copy
  from math import sqrt, log, exp
  from itertools import cycle
  import warnings
  import numpy
  from .None import tools
label_desc: Use image node_id 4 for calling the StrategyMultiObjective obj's underlying member method code with example usage: obj._rankOneUpdate(invCholesky, A, alpha, beta, v) and returns: invCholesky,  A