Qi-Te Yang (杨奇特)

Qi-Te Yang (杨奇特)

Life is full of ups and downs.

Qi-Te Yang’s Homepage

Welcome to my homepage!

I am currently a PostDoc in Hanyang University ERICA, Ansan, South Korea.

I received my Master’s degree from Xiangtan University in 2020 (supervisor: Prof. Juan Zou (邹娟)), and received my Ph.D. degree from South China University of Technology in 2024 (supervisor: Prof. Zhi-Hui Zhan (詹志辉)). My research interests in evolutionary computation, multiobjective optimization, surrogate-assisted optimization, and feature selection.

Publications (*corresponding author, 1co-first author)

2025

  1. “Evolutionary multitask optimization for multiform feature selection in classification”, Qi-Te Yang, Xin-Xin Xu, Zhi-Hui Zhan, Jinghui Zhong, Sam Kwong, and Jun Zhang, IEEE Transactions on Cybernetics, vol. 55, no. 4, pp. 1673 - 1686, 2025. Supplement
  2. “Bi-velocity coevolutionary multiswarm particle swarm optimization for many-objective gateway placement optimization”, Zhou-Zhi Lu, Qi-Te Yang*, Ke-Jing Du, Jian-Yu Li, Chun-Hua Chen, Qingrui Zhou, Zhi-Hui Zhan, IEEE Congress on Evolutionary Computation (CEC), 2025, pp. 1-8.

2024

  1. “A hierarchical and ensemble surrogate-assisted evolutionary algorithm with model reduction for expensive many-objective optimization”, Qi-Te Yang, Jian-Yu Li, Zhi-Hui Zhan, Yunliang Jiang, Yaochu Jin, and Jun Zhang, IEEE Transactions on Evolutionary Computation, 2024, early access. Supplement
  2. “Grid classification-based surrogate-assisted particle swarm optimization for expensive multiobjective optimization”, Qi-Te Yang, Zhi-Hui Zhan, Xiao-Fang Liu, Jian-Yu Li, and Jun Zhang, IEEE Transactions on Evolutionary Computation, vol. 28, no. 6, pp. 1867-1881, Dec. 2024. Supplement
  3. “Fine-grain knowledge transfer-based multitask particle swarm optimization with dual clustering-based task generation for high-dimensional feature selection”, Xin-Yu Wang, Qi-Te Yang1, Yi Jiang, Kay Chen Tan, Jun Zhang, and Zhi-Hui Zhan, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), 2024, pp. 1506-1514.
  4. “Surrogate-assisted flip for evolutionary high-dimensional multiobjective feature selection”, Qi-Te Yang, Liu-Yue Luo, Chun-Hua Chen, Jian-Yu Li, Jing-Hui Zhong, Jun Zhang, and Zhi-Hui Zhan, IEEE Congress on Evolutionary Computation (CEC), 2024, pp. 1-8.

2023

  1. “Multiple populations for multiple objectives framework with bias sorting for many-objective optimization”, Qi-Te Yang, Zhi-Hui Zhan, Sam Kwong, and Jun Zhang, IEEE Transactions on Evolutionary Computation, vol. 27, no. 5, pp. 1340-1354, 2023. Supplement
  2. “Bi-directional feature fixation-based particle swarm optimization for large-scale feature selection”, Jia-Quan Yang, Qi-Te Yang1, Ke-Jing Du, Chun-Hua Chen, Hua Wang, Sang-Woon Jeon, Jun Zhang, and Zhi-Hui Zhan, IEEE Transactions on Big Data, vol. 9, no. 3, pp. 1004-1017, 2023.
  3. “The dilemma between eliminating dominance-resistant solutions and preserving boundary solutions of extremely convex Pareto fronts”, Zhenkun Wang, Qingyan Li, Qite Yang, and Hisao Ishibuchi, Complex & Intelligent Systems, vol. 9, no. 2, pp. 1117-1126, 2023.
  4. “Conjugate surrogate for expensive multiobjective optimization”, Qi-Te Yang, Liu-Yue Luo, Xin-Xin Xu, Chun-Hua Chen, Hua Wang, Jun Zhang, and Zhi-Hui Zhan, IEEE Symposium Series on Computational Intelligence (SSCI), 2023, pp. 920-925.

2022

  1. “Social learning particle swarm optimization with two-surrogate collaboration for offline data-driven multiobjective optimization”, Qi-Te Yang, Zhi-Hui Zhan, Yun Li, and Jun Zhang, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), 2022, pp. 49-57.

2021

  1. “Balancing performance between the decision space and the objective space in multimodal multiobjective optimization”, Qite Yang, Zhenkun Wang, Jianping Luo, and Qiang He, Memetic Computing, vol. 13, no. 1, pp. 31-47, 2021. Code
  2. “Hierarchical preference algorithm based on decomposition multiobjective optimization”, Juan Zou, Yongwu He, Jinhua Zheng, Dunwei Gong, Qite Yang, Liuwei Fu, and Tingrui Pei, Swarm and Evolutionary Computation, vol. 60, 2021, Art. no. 100771.
  3. “It is hard to distinguish between dominance resistant solutions and extremely convex Pareto optimal solutions”, Qite Yang, Zhenkun Wang, and Hisao Ishibuchi, International Conference on Evolutionary Multi-Criterion Optimization (EMO), 2021, pp. 3-14.
  4. “On the parameter setting of the penalty-based boundary intersection method in MOEA/D”, Zhenkun Wang, Jingda Deng, Qingfu Zhang, and Qite Yang, International Conference on Evolutionary Multi-Criterion Optimization (EMO), 2021, pp. 413-423.

2020

  1. “Ra-dominance: A new dominance relationship for preference-based evolutionary multiobjective optimization”, Juan Zou, Qite Yang*, Shengxiang Yang, and Jinhua Zheng, Applied Soft Computing, vol. 90, 2020, Art. no. 106192.

Professional Activities

Journal reviewer

  • IEEE Transactions on Evolutionary Computation
  • IEEE Transactions on Cybernetics
  • IEEE Transactions on System, Man, and Cybernetics: System
  • IEEE Transactions on Emerging Topics in Computational Intelligence
  • CAAI Transactions on Intelligence Technology
  • Swarm and Evolutionary Computation
  • Neurocomputing
  • Memetic Computing

Conference reviewer

  • IEEE Congress on Evolutionary Computation (CEC): 2024, 2025
  • International Conference on Machine Intelligence Theory and Applications (MiTA): 2025
  • International Conference on Advanced Computational Intelligence (ICACI): 2023, 2025
  • International Conference on Neural Information Processing (ICONIP): 2023
  • International Conference on Swarm Intelligence (ICSI): 2022