Learning Fuzzy Cognitive Maps Using a Genetic Algorithm with Decision-Making Trial and Evaluation

Abstract

Fuzzy cognitive maps (FCMs) are inference networks, which are the combination of fuzzy logic and neural networks. Various evolutionary-based learning algorithms have been proposed to learn FCMs. However, evolutionary algorithms have shortcomings, such as easy to become premature and the local search ability is weak where the search may trap into local optima. Decision-making trial and evaluation laboratory (DEMATEL) has been widely accepted as one of the best tools to analyze the causal and effect relationships between concepts. Therefore, we combine real-coded genetic algorithm (RCGA) with DEMATEL method, termed as RCGADEMATEL-FCM, to learn FCM models. In RCGADEMATEL-FCM, the DEMATEL method is used as a directed neighborhood search operator to steer the search to the right direction in the objective space, which can overcome the premature problem and make the search jump out of the local optimum. Experimental results on both synthetic and real life data demonstrate the efficiency of the proposed algorithm. The comparison with existing learning algorithms shows that RCGADEMATEL-FCM can learn FCMs with higher accuracy without expert knowledge.