Optimization in genetically evolved fuzzy cognitive maps supporting decision-making: the limit cycle case

This paper proposes an extension of genetically evolved fuzzy cognitive maps (GEFCMs) aiming at increasing their reliability by overcoming its weakness appearing in cases of a limit cycle behavior. FCMs use notions borrowed from artificial intelligence and neural networks to combine concepts and causal relationships, aimed at creating dynamic models that describe a given cognitive setting. The activation level of the nodes participating in an FCM model can be calculated using specific updating equations in a series of iterations.