Learning fuzzy cognitive maps using evolutionary algorithm based on system performance indicators

Abstract

Fuzzy cognitive map (FCM) is a soft computing technique for modeling decision support systems. Construction of the FCM model is based on the selection of concepts important for the analyzed problem and determining significant connections between them. Fuzzy cognitive map can be initialized based on expert knowledge or automatic constructed from data with the use of supervised or evolutionary learning algorithm. FCM models learned from data are much denser than those created by experts. This paper proposes a new evolutionary approach for fuzzy cognitive maps learning based on system performance indicators. The learning process has been carried out with the use of Elite Genetic Algorithm and Individually Directional Evolutionary Algorithm. The developed approach allows to receive FCM model more similar to the reference system than standard methods for fuzzy cognitive maps learning.