郑州大学计算智能实验室

      研究新型进化计算（遗传规划、进化多任务、多目标优化算法等）技术应用于解决机器学习及计算机视觉中的各种学习与优化问题，应用领域包括生物、遥感、工业、机械等。具体方向包括但不限于以下：

●  进化计算机视觉和图像处理，包括图像分析、图像分类、图像分割、边缘检测、目标检测等；

●  进化机器学习，包括监督学习、分类、回归、集成学习、迁移学习、小样本学习、进化深度学习等；

●  进化计算与优化的理论研究，包括遗传规划/编程、粒子群优化算法、基于代理模型的进化算法、进化多目标优化等；

●  进化特征工程与学习，包括特征提取、特征选择、特征构建、特征学习；

●  其他应用，包括故障诊断、遥感图像分析、海洋水产数据分析、人脸识别、表情分类等。

‖ 出版专著：

■  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.

‖ 代表性论文：

[1] Y. Bi, J. Liang, B. Xue, and M. Zhang. “A Genetic Programming Approach with Building Block Evolving and Reusing to Image Classification,” IEEE Transactions on Evolutionary Computation, DOI: 10.1109/TEVC.2023.3284712 , 2023.

[2] 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.

[3] 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.

[4] 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

[5] 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

[6] 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.

[7] 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.

[8] 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.

[9] 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.

[10] 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.

[11] 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.

[12] 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.

[13] 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.

[14] 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.

[15] 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.

[16] 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.

[17] 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.

[18] Y. Bi, 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

[19] 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

[20] 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.

[21] 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.

[22] 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 2

[23] C. Wen, M. Lu, Y. Bi（毕莹）, 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

[24] 毕莹, 薛冰, 张孟杰. GP 算法在图像分析上的应用综述. 郑州大学学报(工学版), 2018, 39(06):3-13.

[25] 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.

[26] 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.

[27] 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.

‖ 相关重要活动：    

●  Special Session on Evolutionary Computer Vision and Image Processing (ECVIP) at 2022 IEEE Congress on Evolutionary Computation (IEEE CEC 2023)

●  IEEE Symposium on Computational Intelligence for Multimedia Signal and Vision Processing (IEEE CIMSIVP) at 2023 IEEE Symposium Series On Computational Intelligence (IEEE SSCI 2023)

●  Workshop on Evolutionary Data Mining and Machine Learning (EDMML) at 2023 IEEE International Conference on Data Mining (IEEE ICDM 2023)

●  Workshop on Evolutionary Data Mining and Machine Learning (EDMML) at 2022 IEEE International Conference on Data Mining (IEEE ICDM 2022)

●  IEEE Symposium on Computational Intelligence for Multimedia Signal and Vision Processing (IEEE CIMSIVP) at 2022 IEEE Symposium Series On Computational Intelligence (IEEE SSCI 2022)

●  Special Session on Evolutionary Computer Vision and Image Processing (ECVIP) at 2022 IEEE World Congress on Computational Intelligence (IEEE WCCI 2022)

●  Workshop on Evolutionary Data Mining and Machine Learning (EDMML) at 2021 IEEE International Conference on Data Mining (IEEE ICDM 2021)

●  Special Session on Evolutionary Computation for Computer Vision and Image Analysis (ECCVIA)  at 2021 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2021)

●  Special Session on Computational Intelligence for Computer Vision and Image Processing (CICVIP) at 2021 22nd International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)