Evolutionary Algorithms for Fuzzy Cognitive Maps

TL;DR

This paper reviews the use of evolutionary algorithms, particularly genetic algorithms, for training Fuzzy Cognitive Maps, highlighting their role in optimizing model learning and convergence.

Contribution

It provides a comprehensive overview of genetic algorithms applied to FCM training within the broader context of FCM learning methods.

Findings

Genetic algorithms effectively optimize FCM node weights.

Evolutionary algorithms improve FCM convergence towards desired behavior.

The paper contextualizes evolutionary computing in FCM learning methods.

Abstract

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.