期刊
APPLIED SOFT COMPUTING
卷 108, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.asoc.2021.107441
关键词
Fuzzy cognitive maps (FCMs); Many-task optimization; Multi-task optimization; Evolutionary algorithm; Random inactivation; Batch learning
资金
- Ministry of Science and Technology of China [2018AAA0101302]
- General Program of National Natural Science Foundation of China (NSFC) [61773300]
- Key Project of Science and Technology Innovation 2030
IBMTEA-FCM is a random inactivation-based batch many-task evolutionary algorithm proposed to learn large-scale FCMs. By modeling the FCM learning problem as a many-task optimization problem, separating tasks into batches, and employing a many-task framework, IBMTEA-FCM achieves higher accuracy and lower computational cost in learning large-scale FCMs compared to existing classical methods.
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. (C) 2021 Elsevier B.V. All rights reserved.
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