  One drawback of evolutionary multiobjective optimization algorithms (EMOA)
has traditionally been high computational cost to create an approximation of
the Pareto front: number of required objective function evaluations usually
grows high. On the other hand, for the decision maker (DM) it may be difficult
to select one of the many produced solutions as the final one, especially in
the case of more than two objectives.
  To overcome the above mentioned drawbacks number of EMOA's incorporating the
decision makers preference information have been proposed. In this case, it is
possible to save objective function evaluations by generating only the part of
the front the DM is interested in, thus also narrowing down the pool of
possible selections for the final solution.
  Unfortunately, most of the current EMO approaches utilizing preferences are
not very intuitive to use, i.e. they may require tweaking of unintuitive
parameters, and it is not always clear what kind of results one can get with
given set of parameters. In this study we propose a new approach to visually
inspect produced solutions, and to extract preference information from the DM
to further guide the search. Our approach is based on intuitive use of dynamic
query sliders, which serve as a means to extract preference information and are
part of the graphical user interface implemented for the efficient UPS-EMO
algorithm.
