J. B. Yang, J. Liu, J. Wang, H. S Sii, and H. W. Wang, Belief rule-base inference methodology using the evidential reasoning approach-RIMER, IEEE Trans. Syst. Man Cybern. A Syst. Hum., vol. 36, no. 2, pp. 266-285, 2006.
C. L. Hwang and K. Yoon, Methods for multiple attribute decision making, in Multiple Attribute Decision Making: Methods and Applications A State-of-the-Art Survey, C. L. Hwang and K. Yoon, eds. Springer, 1981, pp. 58-191.
A. P. Dempster, A generalization of Bayesian inference, J. Roy. Stat. Soc., vol. 30, no. 2, pp. 205-232, 1968.
G. Shafer, A Mathematical Theory of Evidence. Princeton, UJ, USA: Princeton University Press, 1976.
W. K. Wu, Y. G. Fu, Q. Su, Y. J. Wu, and X. T. Gong, GDA based ensemble learning methods for parameter training in belief rule base, (in Chinese), J. Front. Comput. Sci. Technol., vol. 10, no. 12, pp. 1651-1661, 2016.
W. K. Wu, L. H. Yang, Y. G. Fu, L. Q. Zhang, and X. T. Gong, Parameter training approach for belief rule base using the accelerating of gradient algorithm, (in Chinese), J. Front. Comput. Sci. Technol., vol. 8, no. 8, pp. 989-1001, 2014.
J. B. Yang, Rule and utility based evidential reasoning approach for multi-attribute decision analysis under uncertainties, Eur. J. Oper. Res., vol. 131, no. 1, pp. 31-61, 2001.
W. He, P. L. Qiao, Z. J. Zhou, G. Y Hu, Z. C Feng, and H. Wei, A new belief-rule-based method for fault diagnosis of wireless sensor network, IEEE Access, vol. 6, pp. 9404-9419, 2018.
Z. J. Zhou, G. Y. Hu, B. C. Zhang, C. H. Hu, Z. G. Zhou, and P. L. Qiao, A model for hidden behavior prediction of complex systems based on belief rule base and power set, IEEE Trans. Syst. Man Cybern. Syst., vol. 48, no. 9, pp. 1649-1655, 2018.
X. Yin, B. Zhang, Z. Zhou, Z. Wang, and G. Hu, A novel health estimation model for CNC machine tool servo system based on belief-rule-base, in Prognostics and System Health Management Conference, 2017, p. 8079212.
Z. J. Zhou, Z. C. Feng, C. H. Hu, F. J. Zhao, Y. M. Zhang, and G. Y. Hu, Fault detection based on belief rule base with online updating attribute weight, in Proc. 32nd Youth Academic Annual Conference of Chinese Association of Automation, Hefei, China, 2017, pp. 272-276.
L. L. Chang, Z. J. Zhou, Y. W. Chen, T. J Liao, Y. Hu, and L. H. Yang, Belief rule base structure and parameter joint optimization under disjunctive assumption for nonlinear complex system modeling, IEEE Trans. Syst. Man Cybern. Syst., vol. 48, no. 9, pp. 1542-1554, 2018.
Y. M. Wang, J. B. Yang, D. L. Xu, and K. S. Chin, The evidential reasoning approach for multiple attribute decision analysis using interval belief degrees, Eur. J. Oper. Res., vol. 175, no. 1, pp. 35-66, 2006.
F. Luna, A. J. Nebro, and E. Alba, Observations in using grid-enabled technologies for solving multi-objective optimization problems, Parallel Comput., vol. 32, nos. 5&6, pp. 377-393, 2006.
S. Rostami and A. Shenfield, CMA-PAES: Pareto archived evolution strategy using covariance matrix adaptation for multi-objective optimization, in Proc. 12th UK Workshop on Computational Intelligence, Edinburgh, UK, 2012, pp. 1-8.
Q. M. Fan, Multi-objective optimization design of vehicle transmission system based on Pareto optimal theory, in Proc. 2nd Int. Conf. Intelligent Computation Technology and Automation, Changsha, China, 2009, pp. 198-201.
Y. Y. Jiang, Selective ensemble learning algorithm, in Proc. 2010 Int. Conf. Electrical and Control Engineering, Wuhan, China, 2010, pp. 1859-1862.
D. W. Corne, J. D. Knowles, and M. J. Oates, The pareto envelope-based selection algorithm for multiobjective optimization, in Proc. 2000 Int. Conf. Parallel Problem Solving from Nature, Paris, France, 2000.
R. F. Huang, X. M. Luo, B. Ji, P. Wang, A. Yu, Z. H. Zhai, and J. J. Zhou, Multi-objective optimization of a mixed-flow pump impeller using modified NSGA-II algorithm, Sci. China Technol. Sci., vol. 58, no. 12, pp. 2122-2130, 2015.
X. H. Wu and Q. Xu, Optimization model of multi-objective distribution based on adaptive grid particle swarm optimization algorithm, (in Chinese), J. Highway Transp. Res. Dev., vol. 27, no. 5, pp. 132-136, 2010.
Z. H. Zhou, J. X. Wu, and W. Tang, Ensembling neural networks: Many could be better than all, Artif. Intell., vol. 137, nos. 1&2, pp. 239-263, 2002.
L. L. Chang, Z. J. Zhou, Y. You, L. H. Yang, and Z. G. Zhou, Belief rule based expert system for classification problems with new rule activation and weight calculation procedures, Inform. Sci., vol. 336, pp. 75-91, 2016.
Q. Q. Ye, L. H. Yang, Y. G. Fu, and X. C. Chen, Classification approach based on improved belief rule-base reasoning, (in Chinese), J. Front. Comput. Sci. Technol., vol. 10, no. 5, pp. 709-721, 2016.
R. Y. Rubinstein and D. P. Kroese, The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning. Springer, 2004.