Learning fuzzy cognitive maps using evolution strategies: A novel schema for modeling and simulating high-level behavior

FCM is recognized as a flexible and powerful modeling and simulating technique; however, it is a relatively new methodology, which exhibits weaknesses mainly in the algorithmic background. Such weaknesses become evident during heuristic evaluation of the cause-effect relationships describing FCM-based systems. The external intervention (typically from experts) for the determination and fine-tuning of FCM parameters cannot be regarded as an accurate and efficient way to design and manage FCMs, especially in the case of highly complicated structures, where even experts meet difficulties in their attempts for an holistic interpretation. The introduction and implementation of a training procedure based on a robust and flexible optimization tool constitutes a promising alternative. The present study focuses on Evolutionary Computation, since this domain encompasses optimization techniques possessing the needed features for this type of problems. Evolution Strategies appear as the most appropriate methodology, and as such, they are tested herein for a potential implementation in FCM-based systems. The proposed approach combines FCM & ES concepts and sets the basis for establishment and deployment of structural evolution, which will broaden the applicability of FCMs.