A multiple statistical comparison of nature-inspired algorithms for learning Fuzzy Cognitive Maps

Fuzzy Cognitive Maps (FCMs) are a very simple and powerful technique for simulation and analysis of dynamic systems. In spite of their wide applicability in different domain areas, the manual development of FCMs suffers from several drawbacks such as the human difficulty to deal with systems characterised by a large number of variables. Therefore, several evolutionary learning approaches aimed at automatically building FCM models by using historical data have been developed over years. Nevertheless, there is no a formal and complete comparison able to evaluate the performance of evolutionary algorithms in learning FCMs. Consequently, the goal of this paper is to bridge this experimental gap by performing a multiple statistical procedure able to compare the best known nature-inspired algorithms-based learning methods for FCM models.