Recombination for Learning Strategy Parameters in the MO-CMA-ES
    Thomas Vo\ss, Nikolaus Hansen, and Christian Igel

  The multi-objective covariance matrix adaptation evolution strategy
  (MO-CMA-ES) is a variable-metric algorithm for real-valued vector
  optimization. It maintains a parent population of candidate
  solutions, which are varied by additive, zero-mean Gaussian
  mutations.  Each individual learns its own covariance matrix for the
  mutation distribution considering only its parent and offspring.
  However, the optimal mutation distribution of individuals that are
  close in decision space are likely to be similar if we presume some
  notion of continuity of the optimization problem. Therefore, we
  propose a lateral (inter-individual) transfer of information in the
  MO-CMA-ES considering also successful mutations of \emph{neighboring
  individuals} for the covariance matrix adaptation.  We evaluate this
  idea on common bi-criteria objective functions. The preliminary
  results show that the new adaptation rule significantly improves the
  performance of the MO-CMA-ES. 

In: Fifth International Conference on Evolutionary Multi-Criterion
  Optimization (EMO 2009)