Information retrieval systems depend on Boolean queries. Proposed evolution of Boolean queries should increase the performance of the information retrieval system. Information retrieval systems quality are measured in terms of two different criteria, precision and recall. Evolutionary techniques are widely applied for optimization tasks in different areas including the area of information retrieval systems. In information retrieval applications both criteria have been combined in a single scalar fitness function by means of a weighting scheme 'harmonic mean'. Usage of genetic algorithms in the Information retrieval, especially in optimizing a Boolean query, is presented in this paper. Influence of both criteria, precision and recall, on quality improvement are discussed as well.
As the volume and variety of information sources, especially on the World Wide Web (WWW), continue to grow, the requirements imposed on search applications are steadily increasing. The amount of available data is growing and so do the user demands. Search application should provide the users with accurate, sensible responses to their requests. It is difficult to provide information that accurately matches user information needs. Search effectiveness can be seen as the accuracy of matching user information needs against the retrieved information. There are problems emerging: users often do not present search queries in the form that optimally represents their information need, the measure of a document's relevance is often highly subjective between different users, and information sources might contain heterogeneous documents, in multiple formats and the representation of documents is not unified. This contribution presents a proposal to improve web search effectiveness via evolutionary optimization of the Boolean and vector search queries based on individual user models.