Concepts selection in fuzzy cognitive map using Evolutionary Learning Algorithm based on Graph Theory Metrics

Fuzzy cognitive map (FCM) allows to discover knowledge in the form of concepts significant for the analyzed problem and causal connections between them. The FCM model can be developed by experts or using learning algorithms and available data. The main aspect of building of the FCM model is concepts selection. It is usually based on the expert knowledge. The aim of this paper is to develop and analyze a new evolutionary algorithm for selection of key concepts and determining the weights of the connections between them on the basis of available data. The proposed approach allows to reduce concepts during learning process based on metrics from the area of graph theory: significance of each node and total influence of the concept. A simulation analysis of the developed algorithm was done with the use of real-life data.