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.
Since their appearance in 1993, first approaching the Shannon limit, turbo codes have given a new direction in the channel encoding field, especially since they have been adopted for multiple norms of telecommunications such as deeper communication. A robust interleaver can significantly contribute to the overall performance a turbo code system. Search for a good interleaver is a complex combinatorial optimization problem. In this paper, we present genetic algorithms and differential evolution, two bio-inspired approaches that have proven the ability to solve non-trivial combinatorial optimization tasks, as promising optimization methods to find a well-performing interleaver for large frame sizes.
Matrix factorization or factor analysis is an important task helpful in the analysis of high dimensional real world data. There are several well known methods and algorithms for factorization of real data but many application areas including information retrieval, pattern recognition and data mining require processing of binary rather than real data. Unfortunately, the methods used for real matrix factorization fail in the latter case. In this paper we introduce background and initial version of Genetic Algorithm for binary matrix factorization.