def METHOD_NAME(rlz,**kwargs):
  """
    Logistic fitness method
    .. math::
    fitness = \frac{1}{1+e^{-a(x-b)}}
    @ In, rlz, xr.Dataset, containing the evaluation of a certain
              set of individuals (can be the initial population for the very first iteration,
              or a population of offsprings)
    @ In, kwargs, dict, dictionary of parameters for this fitness method:
          objVar, string, the name of the objective variable
          a, float, linear coefficient for the objective function (default = 1.0)
          penalty, float, measuring the severity of the constraint violation. (default = 1.0)
          b, float, linear coefficient for the penalty measure. (default = 1.0)
    @ Out, fitness, xr.DataArray, the fitness function of the given objective corresponding to a specific chromosome.
  """
  if kwargs['a'] == None:
    a = 1.0
  else:
    a = kwargs['a']
  if kwargs['b'] == None:
    b = 0.0
  else:
    b = kwargs['b']
  objVar = kwargs['objVar']
  val = rlz[objVar]
  data = np.atleast_1d(rlz[objVar].data)
  denom = 1.0 + np.exp(-a * (val - b))
  fitness = 1.0 / denom
  fitness = xr.DataArray(np.array(fitness),
                          dims=['chromosome'],
                          coords={'chromosome': np.arange(len(data))})
  return fitness