Nowadays, one of the main problems in information retrieval is filtering the great amount of information currently available. Late fusion techniques merge the outcomes of different information retrieval systems to generate a single result that, hopefully, could increase the overall performance by taking advantage of the strengths of all the individual systems. These techniques have a great flexibility and allow an efficient development of multimedia retrieval systems. The growing interest on these technologies has led to the creation of a subtrack in the ImageCLEF entirely devoted to them: the information fusion task. In this work, Intelligent Systems and Data Mining group approach to that task is presented. We propose the use of an evolutive algorithm to estimate the parameters of three of all the fusion approaches present in the literature.