赵琪

研究助理教授 计算机科学与工程系

赵琪,2019年毕业于北京工业大学管理科学与工程专业,获博士学位,并获北京市优秀毕业生;2017至2018年由国家留学基金委公派在澳大利亚新南威尔士大学计算机科学专业进行博士联合培养。2019至2021年在南方科技大学计算机科学与工程系进行博士后研究,合作导师:史玉回教授。2021年至今在南方科技大学计算机科学与工程系任研究助理教授(副研究员)。

个人简介

研究领域

自动算法设计,演化计算,运筹优化

 

Automated Algorithm Design: Developing methods, techniques, and software for automated algorithm design for one-off systems and autonomous life-long systems. Currently focusing on automatically designing heuristics/metaheuristics for optimization problems in autonomous systems.

 

Metaheuristics and Operations Research: Developing metaheuristics and operations research methods to solve scientific problems (e.g., data clustering, multi-objective optimization, sparse optimization) and practical problems (e.g., bin packing, deployment, scheduling).

 

Some of my research findings have been successfully applied in industry. For example, for the supply chain management of a leading biomedical electronics company Mindray, our AutoOpt technique has been verified to significantly improve the efficiency of raw material stacking, save space for rack placement, and automatically design solvers for demand changes.


学术成果 查看更多

代表论文:

  1. Gridless evolutionary approach for line spectral estimation with unknown model order

    Yan B, Zhao Q, Zhang J*, Zhang J A, and Yao X

    IEEE Transactions on Cybernetics, 2022, early access, doi: 10.1109/TCYB.2022.3179378.

  2. AutoOpt: A methodological framework of automatically designing metaheuristics for optimization problems

    Zhao Q, Yan B, and Shi Y*

    arXiv Preprint, 2022, https://arxiv.org/abs/2204.00998.

  3. Evolutionary robust clustering over time for temporal data

    Zhao Q, Yan B, Yang J, and Shi Y*

    IEEE Transactions on Cybernetics, 2022, early access, doi: 10.1109/TCYB.2022.3167711.

  4. Bilevel evolutionary approach for off-grid direction-of-arrival estimation

    Yan B, Zhao Q, Zhang J*, Zhang J A, and Yao X

    Applied Soft Computing, 2021, 113: 107954.

  5. Evolutionary dynamic multi-objective optimization via learning from historical search process

    Zhao Q, Yan B, Shi Y*, and Middendorf M

    IEEE Transactions on Cybernetics, 2021, early access, doi: 10.1109/TCYB.2021.3059252.

  6. Adaptive sorting-based evolutionary algorithm for many-objective optimization

    Liu C, Zhao Q*, Yan B, Elsayed S, Ray T, and Sarker R

    IEEE Transactions on Evolutionary Computation, 2019, 23(2): 247-257.

  7. Transfer learning-assisted multi-objective evolutionary clustering framework with decomposition for high-dimensional data

    Liu C, Zhao Q*, Yan B, Elsayed S, and Sarker R

    Information Sciences, 2019, 505: 440-456.

  8. Reference vector-based multi-objective clustering for high-dimensional data

    Liu C, Li Y*, Zhao Q, and Liu C

    Applied Soft Computing, 2019, 78: 614-629.

  9. Novel evolutionary multi-objective soft subspace clustering algorithm for credit risk assessment

    Liu C, Xie J*, Zhao Q, and Liu C

    Expert Systems with Applications, 2019, 138: 112827.

  10. Adaptive decomposition-based evolutionary approach for multiobjective sparse reconstruction

    Yan B*, Zhao Q, Wang Z, and Zhang J A

    Information Sciences, 2018, 462: 141-159.

更多信息请见个人主页.

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电子邮箱

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