| On 2010-01-19 at 15:00:00 (Brussels Time) |
Abstract
Many practical optimization problems require the consideration of multiple, conflicting objectives. In such cases, usually no single optimal solution exists. Instead, there is a set of so-called efficient or Pareto-optimal solutions with different trade-offs of the objectives. In the absence of additional user preference information, they all have to be regarded as equally good. Evolutionary algorithms, i.e., heuristics inspired by natural evolution, are gaining increasing popularity for such problems. Because they work on a population of solutions, they can be used to search for a well-distributed set of Pareto-optimal solutions in one run, which are then given to the decision maker to choose from. This talk will give an introduction to evolutionary multiobjective optimisation, and then discuss why and how the decision maker's preferences should be incorporated already during the optimisation, rather than only after optimisation, as currently most evolutionary multiobjective optimisation approaches do.
Keywords
multiobjective optimization, evolutionary algorithms, preferences
