Determining the Ranking of a New Participant in Eurovision Using Cultural Algorithms and Data Mining

Abstract

Evolutionary computation is generic name given to the resolution of computational problems with base in models of an evolutionary process. Most of the evolutionary algorithms propose biological paradigms, and concepts of natural selection, mutation and reproduction. Nevertheless other paradigms exist which can be adopted in the creation of evolutionary algorithms. Many problems involve not structured environments which can be solved from the perspective of cultural paradigms and, which offer plenty of category models where one does not know the possible solutions of a problem, a common situation in real life. The intention of the present work is to apply the computational properties of cultural technology, in this case to corroborate by means of data mining and, to propose the solution of a specific problem, adapted from the literature about society modelling. In addition, we analyze the voting behavior and ratings of judges in a popular song contest held every year in Europe. The dataset makes it possible to analyze the determinants of success, and gives a rare opportunity to run a direct test of vote trading from logrolling. We show that they are rather driven by linguistic and cultural proximities between singers and voting countries. With this information it is possible to predict the score of a new country, redistributed the assigned votes for a lot of the participants, this paper tries to explain this social behaviour.