本人每年可招收博士研究生1名，硕士研究生3名。欢迎对"智能计算及其相关应用"感兴趣的同学发邮件到wygong@cug.edu.cn.谢谢。

龚文引，博士，教授，博士生导师，湖北省杰出青年基金获得者。分别于2004年、2007年和2010年在中国地质大学（武汉）计算机学院获得学士、硕士和博士学位。主要研究方向为智能计算及其应用。现担任中国仿真学会理事、湖北省计算机学会副秘书长、ECOLE执委会委员，国际期刊Expert Systems with Applications、Memetic Computing、IJBIC、CSMS编委。主持国家重点研发计划项目课题一项、国家自然科学基金项目3项、教育部博士学科点基金一项。在SCI期刊发表论文80余篇，其中ESI高被引论文5篇，出版专著2部、译著1部。曾获得湖北省自然科学奖二等奖两项（R1、R2）、湖北省教学成果奖二等奖一项（R3）、湖北省优秀博士学位论文奖、湖北省优秀硕士学位论文奖、湖北省自然科学优秀学术论文一等奖、GECCO-2010最优论文奖提名等奖励。

更多信息请访问：https://wewnyin.github.io/wenyingong

曾获得第六届和第七届中国地质大学（武汉）“研究生良师益友”称号。

曾获得首届中国地质大学（武汉）“卓越青年研究生导师”称号。

指导学生发表的部分代表性论文（第一作者均为本人指导的研究生）：

Only selected pubs are listed. 

More info & Codes: https://wewnyin.github.io/wenyingong/pubs.htm

• [17] R. Li, W. Gong*, L. Wang, C. Lu, and C. Dong, Co-evolution with deep reinforcement learning for energy-aware distributed heterogeneous flexible job shop scheduling, IEEE Transactions on Systems, Man, and Cybernetics: Systems, August 2023, Accepted.

• [16] F. Ming, W. Gong*, L. Wang, and L. Gao, A constraint-handling technique for decomposition-based constrained many-objective evolutionary algorithms, IEEE Transactions on Systems, Man, and Cybernetics: Systems, July 2023, Accepted.

• [15] R. Li, W. Gong*, L. Wang, C. Lu, and X. Zhuang, Surprisingly popular-based adaptive memetic algorithm for energy-efficient distributed flexible job shop scheduling, IEEE Transactions on Cybernetics, May 2023, Accepted.

• [14] S. Li, W. Gong*, L. Wang, and Q. Gu, Evolutionary multitasking via reinforcement learning, IEEE Transactions on Emerging Topics in Computational Intelligence, May 2023, Accepted.

• [13] F. Ming, W. Gong*, L. Wang, and L. Gao, Constrained multi-objective optimization via multitasking and knowledge transfer, IEEE Transactions on Evolutionary Computation, Dec., 2022, Accepted.

• [12] F. Ming, W. Gong*, D. Li, L. Wang, and L. Gao, A competitive and cooperative swarm optimizer for constrained multi-objective optimization problems, IEEE Transactions on Evolutionary Computation, Aug., 2022, Accepted. 

• [11] F. Ming, W. Gong*, L. Wang, and L. Gao, A constrained many-objective optimization evolutionary algorithm with enhanced mating and environmental selections,  IEEE Transactions on Cybernetics. Feb. 2022, Accepted. 

• [10] Z. Liao, W. Gong*, and S. Li, Two-stage reinforcement learning-based differential evolution for solving nonlinear equations, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 7, pp. 4279-4290, July 2023.

• [9] H. Zhen, W. Gong*, and L. Wang, Evolutionary sampling agent for expensive problems, IEEE Transactions on Evolutionary Computation, vol. 27, no. 3, pp. 716-727, June 2023.

• [8] R. Li, W. Gong*, C. Lu, and L. Wang, A learning-based memetic algorithm for energy-efficient flexible job shop scheduling with type-2 fuzzy processing time, IEEE Transactions on Evolutionary Computation, vol. 27, no. 3, pp. 610-620, June 2023. 

• [7] F. Ming, W. Gong*, and L. Gao, Adaptive auxiliary task selection for multitasking-assisted constrained multi-objective optimization, IEEE Computational Intelligence Magazine, vol. 18, no. 2, pp. 18-30, May 2023.

• [6] F. Ming, W. Gong*, L. Wang, and L. Gao, Balancing convergence and diversity in objective and decision spaces for multimodal multi-objective optimization, IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 7, no. 2, pp. 474-486, April 2023.

• [5] H. Zhen, W. Gong*, L. Wang, F. Ming, and Z. Liao, Two-stage data-driven evolutionary optimization for high-dimensional expensive problems, IEEE Transactions on Cybernetics, vol. 53, no. 4, pp. 2368-2379, April 2023. 

• [4] Z. Zhang, Y. Cai, and W. Gong*, Evolution-driven randomized graph convolutional networks, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 12, pp. 7516-7526, Dec. 2022. 
  

• [3] K. Wang, W. Gong*, Z. Liao, and L. Wang, Hybrid niching-based differential evolution with two archives for nonlinear equations system, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 12, pp. 7469-7481, Dec. 2022. 
  

• [2] F. Ming, W. Gong*, and L. Wang, A two-stage evolutionary algorithm with balanced convergence and diversity for many-objective optimization, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 10, pp. 6222-6234, Oct. 2022. 
  

• [1] Z. Liao, W. Gong*, X. Yan, L. Wang, and C. Hu, Solving nonlinear equations system with dynamic repulsion-based evolutionary algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2020, 50(4): 1590-1601. 

智能优化与学习

本团队致力于与智能优化与机器学习及其应用相关领域的前沿研究