A Memetic Algorithm for Solving the Rank Aggregation Problem

Every time some judges are asked to express their preferences on a set of objects we deal with ranking data. Nowadays, the analysis of such data arise in many scientific fields of science, such as computer sciences, social sciences. Political sciences, medical sciences, just to cite a few. For this reason, the interest in rank aggregation problem is growing up in the research community. The main issue in this framework is to find a ranking that best represents the synthesis of a group of judges. In this chapter we propose to use, for the first time, a memetic algorithm in order to address this complex problem. The proposed memetic algorithm extends genetic algorithms with hill climbing search. As shown in an experimental session involving well-known datasets, the proposed algorithm outperforms the evolutionary state-of-the-art approaches.