  The article presents an approach to interactively solve multi-objective
optimization problems. While the identification of efficient solutions is
supported by computational intelligence techniques on the basis of local
search, the search is directed by partial preference information obtained from
the decision maker.
  An application of the approach to biobjective portfolio optimization, modeled
as the well-known knapsack problem, is reported, and experimental results are
reported for benchmark instances taken from the literature. In brief, we obtain
encouraging results that show the applicability of the approach to the
described problem.
