class_docstr: A CMA-ES strategy that uses the :math:`1 + \lambda` paradigm ([Igel2007]_).
  
  :param parent: An iterable object that indicates where to start the
                 evolution. The parent requires a fitness attribute.
  :param sigma: The initial standard deviation of the distribution.
  :param lambda_: Number of offspring to produce from the parent.
                  (optional, defaults to 1)
  :param parameter: One or more parameter to pass to the strategy as
                    described in the following table. (optional)
  
  Other parameters can be provided as described in the next table
  
  +----------------+---------------------------+----------------------------+
  | Parameter      | Default                   | Details                    |
  +================+===========================+============================+
  | ``d``          | ``1.0 + N / (2.0 *        | Damping for step-size.     |
  |                | lambda_)``                |                            |
  +----------------+---------------------------+----------------------------+
  | ``ptarg``      | ``1.0 / (5 + sqrt(lambda_)| Target success rate.       |
  |                | / 2.0)``                  |                            |
  +----------------+---------------------------+----------------------------+
  | ``cp``         | ``ptarg * lambda_ / (2.0 +| Step size learning rate.   |
  |                | ptarg * lambda_)``        |                            |
  +----------------+---------------------------+----------------------------+
  | ``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.    |
  +----------------+---------------------------+----------------------------+
  
  .. [Igel2007] Igel, Hansen, Roth, 2007. Covariance matrix adaptation for
     multi-objective optimization. *Evolutionary Computation* Spring;15(1):1-28
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docstr: Generate a population of :math:`\lambda` individuals of type
  *ind_init* from the current strategy.
  
  :param ind_init: A function object that is able to initialize an
                   individual from a list.
  :returns: A list of individuals.
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code: def generate(self, ind_init):
      r"""Generate a population of :math:`\lambda` individuals of type
      *ind_init* from the current strategy.
  
      :param ind_init: A function object that is able to initialize an
                       individual from a list.
      :returns: A list of individuals.
      """
      # self.y = numpy.dot(self.A, numpy.random.standard_normal(self.dim))
      arz = numpy.random.standard_normal((self.lambda_, self.dim))
      arz = self.parent + self.sigma * numpy.dot(arz, self.A.T)
      return [ind_init(a) for a in arz]
  
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 3 for calling the StrategyOnePlusLambda obj's underlying member method code with example usage: obj.generate(ind_init) and returns: unknown