Averaged Hausdorff Approximations of Pareto Fronts based on
  Multiobjective Estimation of Distribution Algorithms
  In the a posteriori approach of multiobjective optimization the Pareto front
is approximated by a finite set of solutions in the objective space. The
quality of the approximation can be measured by different indicators that take
into account the approximation's closeness to the Pareto front and its
distribution along the Pareto front. In particular, the averaged Hausdorff
indicator prefers an almost uniform distribution. An observed drawback of
multiobjective estimation of distribution algorithms (MEDAs) is that - as
common for randomized metaheuristics - the final population usually is not
uniformly distributed along the Pareto front. Therefore, we propose a
postprocessing strategy which consists of applying the averaged Hausdorff
indicator to the complete archive of generated solutions after optimization in
order to select a uniformly distributed subset of nondominated solutions from
the archive. In this paper, we put forward a strategy for extracting the above
described subset. The effectiveness of the proposal is contrasted in a series
of experiments that involve different MEDAs and filtering techniques.
