Learning large-scale fuzzy cognitive maps using an evolutionary many-task algorithm

Abstract

Fuzzy cognitive maps (FCMs) are a powerful tool for simulating and analyzing complex systems. Many efficient methods based on evolutionary algorithms have been proposed to learn small-scale FCMs. However, large number of function evaluations of those methods make them difficult to cope with large-scale FCM learning problems. To overcome this issue, we propose a random inactivation-based batch many-task evolutionary algorithm, termed as IBMTEA-FCM. Inspired by the probability of knowledge sharing in different tasks, the problem of FCM learning is first modeled as a many-task optimization problem, in which each task represents learning local connections of a node in a single FCM. To ensure the effectiveness of knowledge transfer, all tasks are randomly divided into multiple batches to optimize separately. In this method, an evolutionary many-task framework is employed to overcome the proposed many-task FCM learning problem and we randomly deactivate weighted edges to ensure the sparsity of FCM in the evolutionary process. The performance of IBMTEA-FCM is validated on both synthetic datasets and a practical study of gene regulatory network reconstruction. Compared with existing classical methods, the experimental results show that IBMTEA-FCM can learn large-scale FCMs with higher accuracy and less computational cost.