  In computer interfaces in general, especially in information retrieval tasks,
it is important to be able to quickly find and retrieve information. State of
the art approach, used, for example, in search engines, is not effective as it
introduces losses of meanings due to context to keywords back and forth
translation. Authors argue it increases the time and reduces the accuracy of
information retrieval compared to what it could be in the system that employs
modern information retrieval and text mining methods while presenting results
in an adaptive human- computer interface where system effectively learns what
operator needs through iterative interaction. In current work, a combination of
adaptive navigational interface and real time collaborative feedback analysis
for documents relevance weighting is proposed as an viable alternative to
prevailing "telegraphic" approach in information retrieval systems. Adaptive
navigation is provided through a dynamic links panel controlled by an
evolutionary algorithm. Documents relevance is initially established with
standard information retrieval techniques and is further refined in real time
through interaction of users with the system. Introduced concepts of
multidimensional Knowledge Map and Weighted Point of Interest allow finding
relevant documents and users with common interests through a trivial
calculation. Browsing search approach, the ability of the algorithm to adapt
navigation to users interests, collaborative refinement and the self-organising
features of the system are the main factors making such architecture effective
in various fields where non-structured knowledge shall be represented to the
users.
