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

Fuzzy cognitive maps (FCMs) are 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 evaluations of the cause-effect relationships describing FCM-based systems. External intervention (typically from experts) for the determination and fine-tuning of the 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 at a holistic interpretation. The introduction and implementation of a training procedure based on a robust and flexible optimization tool constitutes a promising alternative. This study focuses on evolutionary computation, since this domain encompasses optimization techniques possessing the needed features for this type of problems. Evolution strategies (ESs) appear to be the most appropriate methodology and, as such, they are tested in this work for a potential implementation in FCM-based systems. The proposed approach combines FCM and ES concepts and sets the basis for the establishment and deployment of structural evolution, which broadens the applicability of FCMs.