'return the set of indices to be reevaluted for noise measurement,
taking the ``lam_reeval`` best from the first ``2 * lam_reeval + 2``
values.
Given the first values are the earliest, this is a useful policy also
with a time changing objective.'

def indices(self, fit):

    lam = (self.lam_reeval if self.lam_reeval else (2 + (len(fit) / 20)))
    reev = (int(lam) + ((lam % 1) > np.random.rand()))
    return np.argsort(array(fit, copy=False)[:(2 * (reev + 1))])[:reev]
