杨凯峰，男，中共党员，数学与自然科学博士，副教授。主要研究方向为（单）多目标优化、可解释的人工智能、机器学习、统计学等。主持和参与多项国际科研项目，包括奥地利科学基金（FWF）重点项目、荷兰科学研究基金（NWO）重大项目、欧盟研究与创新计划重大项目等。近年来在《Swarm and Evolutionary Computation》、《Journal of Global Optimization》、ICML等重要国际期刊和高水平学术会议上发表论文多篇，其中多篇为高影响因子期刊（最高IF: 11.5)。研究成果被Facebook、麻省理工、清华大学、北京大学等国际知名机构应用于多个领域。担任Journal of Industrial Information Integration（IF: 15.6）客座编辑，并多次受邀在国际学术会议上作特邀报告。曾获得奥地利科技发展局（FFG）高科技人才引进奖、荷兰政府高科技人才退税津贴等科研奖励。

据Google Scholar统计，截止2025年4月1日，发表文章被引921次，h-index为13，i10-index为21。Google Scholar链接：https://scholar.google.com/citations?user=7WkpjGwAAAAJ&hl=en

研究方向：

• （单）多目标优化

• 可解释人工智能

• 机器学习

• 工业4.0

• 2013-09 至 2017-12    荷兰莱顿大学, 计算机科学，博士

• 2010-09 至 2013-06   中南大学, 控制工程, 硕士

• 2006-09 至 2010-06  中南大学, 自动化, 学士
  

• 2025-03 至 今            西北农林科技大学，中国，副教授
  

• 2019-12 至 2024-12    上奥地利应用科技大学，奥地利，副研究员

• 2018-01 至 2019-12     莱顿大学, 尼德兰（荷兰），博士后

获奖情况：

• 2020：          奥地利科技发展局（FFG）高科技人才引进奖，奥地利科技发展局

• 2018：          尼德兰（荷兰）政府高科技人才退税津贴，尼德兰（荷兰）政府

• 2013-2017： 国家公派留学基金，国家留学基金委

• 2013：          荷兰莱顿市长奖，莱顿市政府

• 2011：          全国飞思卡尔杯智能车竞赛，中国教育部，全国一等奖

• 2006-2013： 优秀学生干部、三好学生，中南大学

特邀学术报告：

• 2023/09/03 - 2023/09/08:         Probability of ‘Improvement’ in Multi-objective Bayesian Optimization (特邀), Dagstuhl

  Seminar 2336 - Multiobjective Optimization on a Budget (邀请制), Schloss Dagstuhl （受邀报告），德国

• 2023/08/03 - 2023/08/17:          Being an 'Optimizer' in Computer Science, Bielefeld University（德国比勒菲尔德大学），德国

• 2022/09/03:                              Multi-objective Bayesian Optimization and Its Application, 中南大学-数学与统计学院，中国

• 2021/04/30:                               Industrial AI and Prescriptive Analytics, “选择山东”国际经贸合作云路演· 数字经济产业链在线合作会---中奥建交 50 周年专场, 线上

多目标获取函数计算复杂度的降低

       研究成果已被 Facebook、麻省理工、美国洛斯阿拉莫斯国家实验室、日本九州大学、科隆应用技术大学等机构用于开源软件设计、生物制药、核反应控制优化、超音速飞机机翼优化、生物沼气优化等不同领域。

多目标获取函数的梯度算法

      研究成果已被普林斯顿大学、美国西北太平洋国家实验室、清华大学和北京大学等机构用于生物材料设计、材料开发、优化算法软件设计等不同领域。

多目标贝叶斯优化的并行计算

      研究成果已被日本明治大学用于化合物合成。

目标函数先验知识的有效利用

      研究成果已被中科院院士、美国 SLAC 国家加速器实验室等用于布雷顿循环优化、粒子加速器等不同领域。

基于偏好的多目标贝叶斯优化算法设计

       研究成果已被用于风力发电机、客机尾翼优化等不同领域。

混合整数的贝叶斯优化算法

       研究成果已被荷兰莱顿大学医学中心、本田欧洲研究所等用于帕金森预测模型优化、汽车碰撞模型优化等不同领域。

2023 - 2024: Python, 硕士，维也纳兽医大学（University of Veterinary Medicine Vienna），奥地利

2023 - 2024: Machine Learning, 硕士，上奥地利应用科技大学（University of Applied Science Upper Austria）,奥地利

2018 - 2019: Reinforcement Learning, 硕士，荷兰莱顿大学（Leiden University），尼德兰（荷兰）

2017 - 2018: Natural Computing，本科，荷兰莱顿大学（Leiden University），尼德兰（荷兰）

2016 - 2019: Evolutionary Algorithms, 硕士，荷兰莱顿大学（Leiden University），尼德兰（荷兰）

期刊论文：

[10] Bogdan Burlacu, Kaifeng Yang, and Michael Affenzeller. “Population diversity and inheritance in genetic programming for symbolic regression”. In: Natural Computing (Jan. 2023). issn: 1572-9796. doi:10.1007/s11047-022-09934-x. IF: 2.1 （CCF C 类期刊, JCR Q3）

[9] Khurram Mushtaq, Runmin Zou, Asim Waris, Kaifeng Yang, Ji Wang, Javaid Iqbal, and Mohammed Jameel. “Multivariate wind power curve modeling using multivariate adaptive regression splines and regression trees”. In:Plos one 18.8 (2023), e0290316. IF: 3.7 （JCR Q2）

[8] Kaifeng Yang, Michael Affenzeller, and Guozhi Dong. “A parallel technique for multi-objective Bayesian global optimization: Using a batch selection of probability of improvement”. In: Swarm and Evolutionary Computation 75 (2022), p. 101183. issn: 2210-6502. doi: https: //doi.org/10.1016/j.swevo.2022.101183. IF: 10.3（JCR Q1）

[7] Runmin Zou, Mengmeng Song, Yun Wang, Ji Wang, Kaifeng Yang, and Michael Affenzeller. “Deep non-crossing probabilistic wind speed forecasting with multi-scale features”. In: Energy Conversion and Management257 (2022), p. 115433. issn: 0196-8904. doi: https://doi. org/10.1016/j.enconman.2022.115433. IF: 11.5（JCR Q1）

[6] Kaifeng Yang, Michael Emmerich, André Deutz, and Thomas Bäck. “Multi-Objective Bayesian Global Optimization using expected hypervolume improvement gradient”. In: Swarm and Evolutionary Computation 44 (2019), pp. 945–956. issn: 2210-6502. doi: https: //doi.org/10.1016/j.swevo.2018.10.007. IF: 10.3 (唯一通讯作者，JCR Q1)

[5] Duc Van Nguyen, Marios Kefalas, Kaifeng Yang, Asteris Apostolidis, Markus Olhofer, Steffen Limmer, and THW Bäck. “A review: Prognostics and health management in automotive and aerospace”. In: International Journal of Prognostics and Health Management 10.2 (2019), p. 35. (SJR Q2)

[4] Duc Van Nguyen, Steffen Limmer, Kaifeng Yang, Markus Olhofer, and Thomas Bäck. “Modeling and Prediction of Remaining Useful Lifetime for Maintenance Scheduling Optimization of a Car Fleet”. In: International Journal of Performability Engineering 15.9 (2019), p. 2318. (SJR Q3)

[3] Kaifeng Yang, Michael Emmerich, André Deutz, and Thomas Bäck. “Efficient computation of expected hypervolume improvement using box decomposition algorithms”. In: Journal of Global Optimization 75.1 (Sept. 2019), pp. 3–34. issn: 1573-2916. doi: 10.1007/s10898- 019-00798-7. IF: 2.1 (CCF B 类期刊,唯一通讯作者, JCR Q2)

[2] Kaifeng Yang and Ji Wang. “A Review on the Algorithms of Distribution Network Reconfiguration”. In: Southern Power System Technology 4 (2013), pp. 022–028.

[1] Kaifeng Yang, Ji Wang, and Hui Peng. “Implementation of Neuro-Fuzzy controller for smartcar based on fuzzyTECH”. In: Journal of Northwest A & F University (Natural Science Edition) 40.012 (2012), pp. 230–234.

会议论文：

[28] Xilu Wang, Kaifeng Yang, Peng Liao, Mengxuan Zhang, Yaochu Jin. “Efficient Federated Bayesian Optimization with Symbolic Regression Model”. In: 2025 IEEE Congress on Evolutionary Computation (CEC). (已接收)

[27] Hao Wang, Kaifeng Yang, Michael Affenzeller. “Probability Distribution of Hypervolume Improvement in Bi-objective Bayesian Optimization”. In: International Conference on Machine Learning. ICML ’24. 2024. (唯一通信作者，CCF A 类会议， Qualis A1 类会议)

[26] Kirill Antonov, Roman Kalkreuth, Kaifeng Yang, Thomas Bäck, Niki van Stein, and Anna V. Kononova. “A Functional Analysis Approach to Symbolic Regression”. In: The Genetic and Evolutionary Computation Conference. GECCO ’24. 2024. (CCF C 类会议， Qualis A1 类会议)

[25] Kaifeng Yang, Bernhard Werth, and Michael Affenzeller. “Age-Layer-Population-Structure with Self- Adaptation in Optimization”. In: The International Conference on Computer Aided Systems Theory. EUROCAST ’24. 2024. (接收，唯一通讯作者， Qualis B3 类会议)

[24] Fu Xing Long, Diederick Vermetten, Anna V. Kononova, Roman Kalkreuth, Kaifeng Yang, Thomas Bäck, and Niki van Stein. “Challenges of ELA-guided Function Evolution using Genetic Programming”. In: The 15thInternational Joint Conference on Computational Intelligence. IJCCI 2023. 2023. (接收)

[23] Hao Wang and Kaifeng Yang. “Bayesian Optimization”. In: Many-Criteria Optimization and Decision Analysis: State-of-the-Art, Present Challenges, and Future Perspectives. Ed. by Dimo Brockhoff, Michael Emmerich, Boris Naujoks, and Robin Purshouse. Cham: Springer International Publishing, 2023, pp. 271–297. isbn: 978-3-031-25263-1. doi: 10.1007/978- 3-031-25263-1_10.

[22] Bernhard Werth, Johannes Karder, Andreas Beham, Erik Pitzer, Kaifeng Yang, and Stefan Wagner. “Walking through the Quadratic Assignment-Instance Space: Algorithm Performance and Landscape Measures”. In: Proceedings of the Companion Conference on Genetic and Evolutionary Computation. GECCO ’23. Lisbon,Portugal, 2023. doi: 10.1145/3583133. 3596374. （CCF C 类会议， Qualis A1 类会议）

[21] Kaifeng Yang and Michael Affenzeller. “Surrogate-assisted Multi-objective Optimization via Genetic Programming Based Symbolic Regression”. In: Evolutionary Multi-Criterion Optimization. Ed. by Michael Emmerich, André Deutz, Hao Wang, Anna V. Kononova, Boris Naujoks, Ke Li, Kaisa Miettinen, and Iryna Yevseyeva. Cham: Springer Nature Switzerland, 2023, pp. 176–190. isbn: 978-3-031-27250-9. (唯一通讯作者，Qualis A2 类会议)

[20] Kaifeng Yang, Kai Chen, Michael Affenzeller, and Bernhard Werth. “A New Acquisition Function for Multi-objective Bayesian Optimization: Correlated Probability of Improvement”. In: Proceedings of the Companion Conference on Genetic and Evolutionary Computation. GECCO ’23. Lisbon, Portugal, 2023. doi: 10.1145/3583133.3596325. （CCF C 类会议， Qualis A1 类会议）

[19] Kaifeng Yang, Sixuan Liu, Michael Affenzeller, and Guozhi Dong. “Gradients of Acquisition Functions for Bi-objective Bayesian Optimization”. In: 2023 19th International Conference on Natural Computation, FuzzySystems and Knowledge Discovery (ICNC-FSKD). ICNC-FSKD ’23. 2023, pp. 1–9. doi: 10.1109/ICNC-FSKD59587.2023.10280812.

[18] Michael Affenzeller, Michael Bögl, Lukas Fischer, Florian Sobieczky, Kaifeng Yang, and Jan Zenisek. “Prescriptive Analytics: When Data- and Simulation-based Models Interact in a Cooperative Way”. In: 2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). 2022, pp. 1–8. doi: 10.1109/SYNASC57785. 2022.00009. ( Qualis C 类会议)

[17] Kaifeng Yang and Michael Affenzeller. “Quantifying Uncertainties of Residuals in Symbolic Regression via Kriging”. In: Procedia Computer Science 200 (2022). 3rd International Conference on Industry 4.0 and Smart Manufacturing, pp. 954–961. issn: 1877-0509. doi: https: //doi.org/10.1016/j.procs.2022.01.293. url: https://www.sciencedirect.com/ science/article/pii/S1877050922003027.

[16] Michael Emmerich, Kaifeng Yang, and André H Deutz. “Infill criteria for multiobjective Bayesian optimization”. In: High-Performance Simulation-Based Optimization. Springer, 2020, pp. 3–16.

[15] Koen van der Blom, Kaifeng Yang, Thomas Bäck, and Michael Emmerich. “Towards Multi-objective Mixed-Integer Evolution Strategies”. In: AIP Conference Proceedings 2070.1 (2019), p. 020046. doi:10.1063/1.5090013. eprint: https://aip.scitation.org/doi/pdf/10. 1063/1.5090013.

[14] André Deutz, Michael Emmerich, and Kaifeng Yang. “The Expected R2-Indicator Improvement for Multi-objective Bayesian Optimization”. In: Evolutionary Multi-Criterion Optimization. Ed. by Kalyanmoy Deb, Erik Goodman, Carlos A. Coello Coello, Kathrin Klamroth, Kaisa Miettinen, Sanaz Mostaghim, and Patrick Reed. Cham: Springer International Publishing, 2019, pp. 359–370. (Qualis A2 类会议)

[13] André Deutz, Kaifeng Yang, and Michael Emmerich. “The R2 Indicator: a Study of its Expected Improvement in Case of Two Objectives”. In: AIP Conference Proceedings 2070.1 (2019), p. 020054. doi: 10.1063/1.5090021. eprint: https://aip.scitation.org/doi/ pdf/10.1063/1.5090021.

[12] Kaifeng Yang, Koen van der Blom, Thomas Bäck, and Michael Emmerich. “Towards Single- and Multiobjective Bayesian Global Optimization for Mixed Integer Problems”. In: AIP Conference Proceedings 2070.1 (2019), p. 020044. doi: 10.1063/1.5090011. eprint: https://aip.scitation.org/doi/pdf/10.1063/1.5090011. (唯一通讯作者)

[11] Kaifeng Yang, Pramudita Satria Palar, Michael Emmerich, Koji Shimoyama, and Thomas Bäck. “A Multi-point Mechanism of Expected Hypervolume Improvement for Parallel Multi-objective Bayesian Global Optimization”. In: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO ’19. Prague, Czech Republic: ACM, 2019, pp. 656–663. isbn: 978-1-4503-6111-8. doi: 10.1145/3321707.3321784. （CCF C 类会议, Qualis A1 类会议)）

[10] Pramudita Satria Palar, Kaifeng Yang, Koji Shimoyama, Michael Emmerich, and Thomas Bäck. “Multi- objective Aerodynamic Design with User Preference Using Truncated Expected Hypervolume Improvement”. In: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO ’18. Kyoto, Japan: ACM, 2018, pp. 1333–1340. isbn: 978-1-4503-5618- 3. doi: 10.1145/3205455.3205497. （CCF C 类会议, Qualis A2 类会议, 同等一作）

[9] Kaifeng Yang, Michael Emmerich, André Deutz, and Carlos M Fonseca. “Computing 3-D Expected Hypervolume Improvement and Related Integrals in Asymptotically Optimal Time”. In: International Conference on Evolutionary Multi-Criterion Optimization. Ed. by Heike Trautmann, Günter Rudolph, Kathrin Klamroth, Oliver Schütze, Margaret Wiecek, Yaochu Jin, and Christian Grimme. Springer. Cham, 2017, pp. 685–700. (唯 一通讯作者, Qualis A2 类会议)

[8] Yali Wang, Longmei Li, Kaifeng Yang, and Michael Emmerich. “A New Approach to Target Region Based Multiobjective Evolutionary Algorithms”. In: 2017 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2017, pp. 1757–1764. (Qualis A2 类会议)

[7] Michael Emmerich, Kaifeng Yang, André Deutz, Hao Wang, and Carlos M. Fonseca. “A Multicriteria Generalization of Bayesian Global Optimization”. In: Advances in Stochastic and Deterministic Global Optimization. Ed. by Panos M. Pardalos, Anatoly Zhigljavsky, and Julius Žilinskas. Cham: Springer, Nov. 2016, pp. 229–243.

[6] Kaifeng Yang, Andre Deutz, Zhiwei Yang, Thomas Bäck, and Michael Emmerich. “Truncated expected hypervolume improvement: Exact computation and application”. In: 2016 IEEE Congress on Evolutionary Computation (CEC). IEEE. 2016, pp. 4350–4357. doi: 10.1109/ CEC.2016.7744343. (Qualis A2 类会议)

[5] Kaifeng Yang, Longmei Li, André Deutz, Thomas Bäck, and Michael Emmerich. “Preference-based Multiobjective Optimization using Truncated Expected Hypervolume Improvement”. In: 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, 2016, pp. 276–281. doi: 10.1109/FSKD.2016.7603186.

[4] Zhiwei Yang, Hao Wang, Kaifeng Yang, Thomas Bäck, and Michael Emmerich. “SMS- EMOA with multiple dynamic reference points”. In: 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE. 2016, pp. 282–288.

[3] Iris Hupkens, André Deutz, Kaifeng Yang, and Michael Emmerich. “Faster exact algorithms for computing expected hypervolume improvement”. In: International Conference on Evolutionary Multi-Criterion Optimization. Ed. by António Gaspar-Cunha, Carlos Henggeler Antunes, and Carlos Coello Coello. Springer. Cham, 2015, pp. 65–79. (Qualis A2 类会议)

[2] Kaifeng Yang, Daniel Gaida, Thomas Bäck, and Michael Emmerich. “Expected hypervolume improvement algorithm for PID controller tuning and the multiobjective dynamical control of a biogas plant”. In: 2015 IEEE Congress on Evolutionary Computation (CEC). May 2015, pp. 1934–1942. doi: 10.1109/CEC.2015.7257122. (Qualis A2 类会议)

[1] Kaifeng Yang, Michael Emmerich, Rui Li, Ji Wang, and Thomas Bäck. “Power distribution network、 reconfiguration by evolutionary integer programming”. In: International Conference on Parallel Problem Solving from Nature–PPSN XIII. Ed. by Thomas Bartz-Beielstein, Jürgen Branke, Bogdan Filipič, and Jim Smith. Springer. Cham, 2014, pp. 11–23. （CCF B 类会议, Qualis A2 类会议）

书：

[1] Kaifeng Yang. “Multi-objective Bayesian Global Optimization for Continuous Problems and Applications”. Leiden University, IBSN: 9789462998018, 2017

其他：

[3] Fu Xing Long, Diederick Vermetten, Anna V. Kononova, Roman Kalkreuth, Kaifeng Yang, Thomas Bäck, and Niki van Stein. Challenges of ELA-guided Function Evolution using Genetic Programming. 2023. arXiv:2305.15245 [cs.NE].

[2] Hao Wang, Kaifeng Yang, Michael Affenzeller. Probability Distribution of Hypervolume Improvement in Bi-objective Bayesian Optimization. 2022. arXiv: 2205.05505 [cs.LG].

[1] Stefan Niculae, Daniel Dichiu, Kaifeng Yang, and Thomas Bäck. Automating penetration testing using reinforcement learning. 2020.

06/2021 [硕士] Mohammad Iman Sayyadzadeh, 第一导师，“Optimization of Hyper-parameters of Artificial Neural Networks using Genetic Algorithm ”

2018-2020 [博士] Marios Kefalas, 第二导师, 第一导师: Prof. Thomas Bäck, Leiden University

2018-2020 [博士] Duc Van Nguyen, 第二导师 , 第一导师 : Prof. Thomas Bäck, Leiden University

06/2019 [硕士] Jelle van den Berg, 第一导师，“Using AI to predict ICU patient mortality”

05/2019 [硕士] Laurens Beljaards, 第一导师，“Towards Environmental Storytelling by Evolutionary Algorithms”

01/2019 [硕士] Martijn J. Post, 第二导师，“Tax data and reinforcement learning”

12/2018 [硕士] Jelle van den Berg, 第一导师，“Artificial intelligence to make accurate predictions for Business Intelligence”

10/2018 [硕士] Lan Jiaqi, 第二导师，“Critical Water Infrastructure Sensor Placement Optimization”

05/2018 [硕士] Wilco Verhoef, 第二导师，“Convolutional Neural Networks for Automatic Classification of Radar Signals in Time Domain: Learning the Micro-Doppler Signature of Human Gait”

05/2018 [硕士] Roy de Winter, 第二导师，“Designing Ships using Constrained Multi-Objective Efficient Global Optimization”