  Fuzzy Cognitive Maps (FCMs) is a complex systems modeling technique which,
due to its unique advantages, has lately risen in popularity. They are based on
graphs that represent the causal relationships among the parameters of the
system to be modeled, and they stand out for their interpretability and
flexibility. With the late popularity of FCMs, a plethora of research efforts
have taken place to develop and optimize the model. One of the most important
elements of FCMs is the learning algorithm they use, and their effectiveness is
largely determined by it. The learning algorithms learn the node weights of an
FCM, with the goal of converging towards the desired behavior. The present
study reviews the genetic algorithms used for training FCMs, as well as gives a
general overview of the FCM learning algorithms, putting evolutionary computing
into the wider context.
