期刊
2019 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
卷 -, 期 -, 页码 966-973出版社
IEEE
DOI: 10.1109/cec.2019.8789975
关键词
fuzzy cognitive maps; boosting; real-coded genetic algorithm
资金
- General Program of National Natural Science Foundation of China (NSFC) [61773300]
- Key Program of Fundamental Research Project of Natural Science of Shaanxi Province, China [2017JZ017]
Fuzzy cognitive maps (FCMs) are generally applied to model and analyze complex dynamical systems. However, the accuracy of population-based FCM learning algorithms is relatively low. Boosting is an effective method to improve the accuracy of any learning algorithm. To this end, we combine FCMs with boosting, termed as boosting fuzzy cognitive maps (BFCMs). The BFCM is an extension of FCMs and has a better performance on fast numerical reasoning than FCMs. In this paper, a real-coded genetic algorithm, which is a popular population-based learning algorithm, is improved on mutation operator and applied to learn the BFCM models, termed as RCGA-BFCM. In the experiments, RCGA-BFCM is applied to learn the BFCM from synthetic data with varying sizes and densities. The experimental results show that RCGA-BFCM can learn BFCMs with high accuracy from synthetic data. In addition, the performance of RCGA-BFCM is validated on the benchmark datasets DREAM3 and DREAM4. The experimental results show that RCGA-BFCM outperforms other learning algorithms obviously, which illustrates that RCGA-BFCM can reconstruct gene regulatory networks (GRNs) effectively.
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