Fusing Data and Optimizing Queries for Intelligent Search

A progressive application of evolutionary computing to optimize Boolean search queries in crisp and fuzzy information retrieval systems was investigated, evaluated in laboratory environment and presented. Additionally, WebFusion - novel meta- search engine contributing to the effectiveness of web search has been presented. The system learns the expertness of every particular underlying standalone search engine in a certain category based on the users ´ preferences estimated according to an analysis of the click-through behavior. An intelligent re-ranking based on ordered weighted averaging is used for fusing the results´ scores obtained from the underlying search engines. In this paper, the two promising web search improvement techniques are merged on the way towards intelligent search application.