Ying Bi，Doctor
Distinguished Professor
E-mail：yingbi@zzu.edu.cn
Bi Ying, PhD, Distinguished Professor, selected by National Young Talent Program. She received her PhD from Victoria University of Wellington, New Zealand, where she studied under Professor MengjieZhang (Fellow of the Royal New Zealand Academy of Sciences, Fellow of the New Zealand Academy of Engineering, IEEEFellow) and Professor BingXue (Fellow of the New Zealand Academy of Engineering and Associate Dean of the School of Engineering and Computing, Victoria University of Wellington). She has been engaged in theoretical and applied research in genetic programming, evolutionary computing, machine learning, computer vision and other fields for a long time, published the world's first English monograph on image classification based on genetic programming, published 55 academic papers in international academic journals and conferences, including 25 SCI journal articles, published 16 papers as the first or corresponding author of TOP journals of the first region of the Chinese Academy of Sciences.15 in the IEEE Journal series. Ranked 93rd (> 16,000 researchers) on TheGPBibliography, the website for global genetic planning algorithms. Served as the guest editor of AppliedSoftComputing and MemeticComputing, the SCI Region I journals. Organized the workshop on evolutionary data mining and machine learning in IEEE International Data Mining Conference (2021, 2022) for two consecutive years. She is the Vice Chair of the IEEECIS Symposium on Evolutionary Computer Vision and Image Processing, a member of the IEEE CIS Symposium on Evolutionary Feature Selection and Construction, and has organized several relevant symposiums at the famous international conferences on evolutionary computing, such as IEEE CEC and IEEE SSCI. She has been the reviewer of important journals in more than 20 fields and the member of the program committee of more than 20 internationally renowned academic conferences. Member of the IEEE Women in Computing Intelligence Committee, Chair of the 2024 International Conference on Evolutionary Computing (IEEE CEC) Symposium, Vice President of Student Affairs for the 2023 Conference on Genetic and Evolutionary Computing (GECCO), Participated in the organization of two international academic conferences IEEE CEC 2019 and Australasian AI 2018.
Published monographs:
l Y. Bi, B. Xue, and M. Zhang. Genetic Programming for Image Classification: An Automated Approach to Feature Learning, Springer International Publishing 2021, DOI: https:doi.org10.1007978-3-030-65927-1. (The world's first English monograph on image classification based on genetic programming)
Main published papers:
[1] Y. Bi, B. Xue, and M. Zhang. “Dual-Tree Genetic Programming for Few-Shot Image Classification,”IEEE Transactions on Evolutionary Computation, vol.26, no.3, pp.555–569, 2022. (IF=16.497 )
[2] Y. Bi, B. Xue, and M. Zhang. “Genetic Programming-Based Evolutionary Deep Learning for Data-Efficient Image Classification,” IEEE Transactions on Evolutionary Computation, early access, DOI: 10.1109/TEVC.2022.3214503, 2022. (IF=16.497 )
[3] Y. Bi, B. Xue, and M. Zhang. “Learning and Sharing: A Multi-Task Genetic Programming Approach to Image Feature Learning,” IEEE Transactions on Evolutionary Computation, vol. 26, no. 2, pp. 218-232, 2022. (IF =16.497 )
[4] Y. Bi, B. Xue, and M. Zhang. “Genetic Programming with Image-Related Operators and A Flexible Program Structure for Feature Learning in Image Classification,” IEEE Transactions on Evolutionary Computation, vol. 25, no. 1, pp. 87-101, 2021. (IF=16.497 )
[5] Y. Bi, B. Xue, and M. Zhang. “A Divide-and-Conquer Genetic Programming Algorithm with Ensembles for Image Classification,” IEEE Transactions on Evolutionary Computation, vol. 25, no. 6, pp. 1148-1162, 2021. (IF=16.497 )
[6] Y. Bi, B. Xue, and M.Zhang. “Multitask Feature Learning as Multiobjective Optimization: A New Genetic Programming Approach to Image Classification,” IEEE Transactions on Cybernetics, early access, DOI: 10.1109/TCYB.2022.3174519, 2022. (IF=19.118 )
[7] Y. Bi, B. Xue, P. Mesejo, S. Cagnoni, M. Zhang. “A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends,” IEEE Transactions on Evolutionary Computation, vol. 27, no. 1, pp. 5-25, 2022. (IF=16.497)
[8] Q. Fan, Y. Bi*, B. Xue, and M. Zhang. “A Global and Local Surrogate-Assisted Genetic Programming Approach to Image Classification,” IEEE Transactions on Evolutionary Computation, early access, DOI: 10.1109/TEVC.2022.3214607, 2022. (IF=16.497 )
[9] Q. Fan, Y. Bi*, B. Xue, M. Zhang. “Genetic Programming for Image Classification: A New Program Representation with Flexible Feature Reuse. IEEE Transactions on Evolutionary Computation, early access, DOI: 10.1109/TEVC .2022.3169490, 2022. (IF=16.497 )
[10] Y. Bi, B. Xue, and M. Zhang. “Instance Selection Based Surrogate-Assisted Genetic Programming for Feature Learning in Image Classification,” IEEE Transactions on Cybernetics, vol. 53, no. 2, pp. 1118-1132, 2023. (IF=19.118 )
[11] Y. Bi, B. Xue, and M. Zhang. “Genetic Programming-Based Discriminative Feature Learning for Low-Quality Image Classification,” IEEE Transactions on Cybernetics, vol. 52, no. 8, pp. 8272 - 8285, 2022. (IF=19.118 )
[12] Y. Bi, B. Xue, and M. Zhang. “Genetic Programming with A New Representation to Automatically Learn Features and Evolve Ensembles for Image Classification,” IEEE Transactions on Cybernetics, vol. 51, no. 5, pp. 1769 - 1783, 2020. (IF=19.118 )
[13] B. Peng, S. Wan, Y. Bi*, B. Xue, and M. Zhang. “Automatic Feature Extraction and Construction Using Genetic Programming for Rotating Machinery Fault Diagnosis,” IEEE Transactions on Cybernetics, vol. 51, no. 10, pp. 4909-4923, 2021. (IF=19.118 )
[14] Y. Bi, B. Xue, and M. Zhang. “An Effective Feature Learning Approach Using Genetic Programming with Image Descriptors for Image Classification,” IEEE Computational Intelligence Magazine, vol. 15, no. 2, pp. 65-77, 2020. (IF=9.809 )
[15] Y. Bi, B. Xue, M. Zhang. “Using a Small Number of Training Instances in Genetic Programming for Face Image Classification,” Information Sciences, vol. 593, pp. 488-504, 2022. (IF=8.233 )
[16] B. Peng, Y. Bi*, B. Xue, M. Zhang, and S. Wan. “Multi-View Feature Construction Using Genetic Programming for Rolling Bearing Fault Diagnosis,” IEEE Computational Intelligence Magazine, vol. 16, no. 3, pp. 79-94, 2021. (IF=9.809 ) (通讯作者)
[17] Y. Bi（毕莹）, B. Xue, M. Zhang. “Multi-Objective Genetic Programming for Feature Learning in Face Recognition,” Applied Soft Computing, vol. 103, Doi: https://doi.org/10.1016/j.asoc.2021.107152, 2021.( IF=8.263)
[18] Y. Bi, B. Xue, D. Briscoe, R. Vennell, M. Zhang. “A New Artificial Intelligent Approach to Buoy Detection for Mussel Farming,” Journal of Royal Society of New Zealand, early access, DOI: https://doi.org/10.1080/03036758.2022.2090966, 2022.
[19] Q. Fan, Y. Bi*, B. Xue, M. Zhang. “Genetic Programming for Feature Extraction and Construction in Image Classification,” Applied Soft Computing, vol. 118, DOI: https://doi.org/10.1016/j.asoc.2022.108509, 2022. ( IF=8.263)
[20] H. Al-Sahaf, Y. Bi, et al. “A Survey on Evolutionary Machine Learning,” Journal of the Royal Society of New Zealand, vol. 49, no. 2, pp. 205-228, 2019.
[21] M. Lu, Y. Bi, et al. “Genetic Programming for High-Level Feature Learning in Crop Classification,” Remote Sensing, vol. 14, no.16, pp. 3982, 2022. (IF=4.848)
[22] C. Wen, M. Lu, Y. Bi, S. Zhang, B. Xue, Q. Zhou, M. Zhang , and W. Wu. “An Object-based Genetic Programming Approach to Cropland Field Extraction,” Remote Sensing, vol. 15, no.10, pp. 347, 2022. (IF=4.848)
[23] Y. Bi, B. Xue, M. Zhang. “Evolving Deep Forest with Automatic Feature Extraction for Image Classification Using Genetic Programming,” Proceedings of The Sixteenth International Conference on Parallel Problem Solving from Nature (PPSN 2020), Lecture Notes in Computer Science. vol. 12269. Springer. Leiden, The Netherlands, September 5-9, 2020. pp. 3-18. (CCF B )
[24] Y. Bi, M. Zhang, and B. Xue. “Genetic Programming for Automatic Global and Local Feature Extraction to Image Classification,” Proceedings of IEEE Congress on Evolutionary Computation (CEC, 2018). IEEE Press. Rio de Janeiro, Brazil. 8-13 July 2018. pp. 1-8. (Outstanding student paper nomination, top 1%)
[25] Y. Bi, B. Xue, and M. Zhang. “An Automated Ensemble Learning Framework Using Genetic Programming for Image Classification,” Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2019). ACM Press. Prague, Czech Republic. 13-17 July 2019. pp. 365-373. (CCF C)