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徐怡 等: 基于遗传算法的划分序乘积空间问题求解层选择 1963
Sciences, 2020, 539: 104–135. [doi: 10.1016/j.ins.2020.05.030]
[26] Chen H, Li JH, Min F, Liu WQ, Tsang ECC. Optimal scale selection in dynamic multi-scale decision tables based on sequential three-
way decisions. Information Sciences, 2017, 415–416: 213–232. [doi: 10.1016/j.ins.2017.06.032]
[27] Liu FL, Zhang BW, Ciucci D, Wu WZ, Min F. A comparison study of similarity measures for covering-based neighborhood classifiers.
Information Sciences, 2018, 448–449: 1–17. [doi: 10.1016/j.ins.2018.03.030]
[28] Wang HZ, He CQ, Li ZP. A new ensemble feature selection approach based on genetic algorithm. Soft Computing, 2020, 24(20):
15811–15820. [doi: 10.1007/s00500-020-04911-x]
[29] Li SJ, Zhang KX, Chen QR, Wang SQ, Zhang SQ. Feature selection for high dimensional data using weighted K-nearest neighbors and
genetic algorithm. IEEE Access, 2020, 8: 139512–139528. [doi: 10.1109/ACCESS.2020.3012768]
[30] Mathias HD, Foley SS. A parallel two-stage genetic algorithm for route planning. In: Proc. of the 2020 Genetic and Evolutionary
Computation Conf. Companion. Cancún: ACM, 2020. 1739–1746. [doi: 10.1145/3377929.3398116]
[31] Xu Y, Li BF. Multiview sequential three-way decisions based on partition order product space. Information Sciences, 2022, 600:
401–430. [doi: 10.1016/j.ins.2022.04.007]
[32] Garg H. A hybrid GSA-GA algorithm for constrained optimization problems. Information Sciences, 2019, 478: 499–523. [doi: 10.1016/j.
ins.2018.11.041]
[33] An LP, Liu S. Two-phase genetic algorithm for attributes reduction. Systems Engineering-theory & Practice, 2014, 34(11): 2892–2899 (in
Chinese with English abstract).
[34] Xue Y, Zhu HK, Liang JY, Słowik A. Adaptive crossover operator based multi-objective binary genetic algorithm for feature selection in
classification. Knowledge-based Systems, 2021, 227: 107218. [doi: 10.1016/j.knosys.2021.107218]
[35] Li SQ, Sun X, Sun DH, Bian WP. Summary of crossover operator of genetic algorithm. Computer Engineering and Applications, 2012,
48(1): 36–39 (in Chinese with English abstract). [doi: 10.3778/j.issn.1002-8331.2012.01.011]
[36] Chen ZQ, Huang ZY, Sun MW, Sun QL. Active disturbance rejection control of load frequency based on big probability variation’s
genetic algorithm for parameter optimization. CAAI Trans. on Intelligent Systems, 2020, 15(1): 41–49 (in Chinese with English abstract).
[doi: 10.11992/tis.201906026]
[37] Dua D, Graff C. UCI machine learning repository. 2019. http://archive.ics.uci.edu/ml
[38] Kaggle. Your machine learning and data science community. 2022. https://www.kaggle.com/datasets
[39] Xie JJ, Hu BQ, Jiang HB. A novel method to attribute reduction based on weighted neighborhood probabilistic rough sets. Int’l Journal of
Approximate Reasoning, 2022, 144: 1–17. [doi: 10.1016/j.ijar.2022.01.010]
附中文参考文献:
[13] 吴伟志, 庄宇斌, 谭安辉, 徐优红. 不协调广义多尺度决策系统的尺度组合. 模式识别与人工智能, 2018, 31(6): 485–494. [doi: 10.
16451/j.cnki.issn1003-6059.201806001]
[17] 牛东苒, 吴伟志, 李同军. 广义多尺度决策系统中基于可变精度的最优尺度组合. 模式识别与人工智能, 2019, 32(11): 965–974. [doi:
10.16451/j.cnki.issn1003-6059.201911001]
[19] 吴伟志, 孙钰, 王霞, 郑嘉文. 不协调广义多尺度决策系统的局部最优尺度组合选择. 模式识别与人工智能, 2021, 34(8): 689–700.
[doi: 10.16451/j.cnki.issn1003-6059.202108002]
[21] 柳萌萌, 赵书良, 韩玉辉, 苏东海, 李晓超, 陈敏. 多尺度数据挖掘方法. 软件学报, 2016, 27(12): 3030–3050. http://www.jos.org.cn/
1000-9825/4924.htm [doi: 10.13328/j.cnki.jos.004924]
[23] 徐怡, 姚一豫. 划分序乘积空间: 基于划分的粒计算模型. 计算机研究与发展, 2019, 56(4): 836–843. [doi: 10.7544/issn1000-1239.
2019.20180325]
[33] 安利平, 刘森. 属性约简的两阶段遗传算法. 系统工程理论与实践, 2014, 34(11): 2892–2899.
[35] 李书全, 孙雪, 孙德辉, 边伟朋. 遗传算法中的交叉算子的述评. 计算机工程与应用, 2012, 48(1): 36–39. [doi: 10.3778/j.issn.1002-
8331.2012.01.011]
[36] 陈增强, 黄朝阳, 孙明玮, 孙青林. 基于大变异遗传算法进行参数优化整定的负荷频率自抗扰控制. 智能系统学报, 2020, 15(1):
41–49. [doi: 10.11992/tis.201906026]
徐怡(1981-), 女, 博士, 教授, 博士生导师, CCF 邱紫恒(1998-), 男, 硕士, 主要研究领域为粒
高级会员, 主要研究领域为智能信息处理, 粒计 计算.
算, 边缘计算.