Selection of Information Sources using a Genetic Algorithm

We address the problem of information sources selection in a context of a large number of distributed sources. We formulate the sources selection problem as a combinatorial optimization problem in order to yield the best set of relevant information sources for a given query. We define a solution as a combination of sources among a huge predefined set of sources. We propose a genetic algorithm to tackle the issue by maximizing the similarity between a selection and the query. Extensive experiments were performed on databases of scientific research documents covering different domains such as computer science and medicine. The results based on the precision measure are very encouraging.