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2020 – today
- 2024
[j9]Bogdan Burlacu
, Kaifeng Yang, Michael Affenzeller:
Population diversity and inheritance in genetic programming for symbolic regression. Nat. Comput. 23(3): 531-566 (2024)- [j8]Bowei Dong, Yuliang Liu, Kaifeng Yang, Jiajian Cao:
Realistic Pulse Waveforms Estimation via Contrastive Learning in Remote Photoplethysmography. IEEE Trans. Instrum. Meas. 73: 1-15 (2024) - [c20]Kirill Antonov, Roman Kalkreuth, Kaifeng Yang, Thomas Bäck, Niki van Stein, Anna V. Kononova:
A Functional Analysis Approach to Symbolic Regression. GECCO 2024 - [c19]Hao Wang, Kaifeng Yang, Michael Affenzeller:
Probability Distribution of Hypervolume Improvement in Bi-objective Bayesian Optimization. ICML 2024 - [i5]Kirill Antonov, Roman Kalkreuth, Kaifeng Yang, Thomas Bäck, Niki van Stein, Anna V. Kononova:
A Functional Analysis Approach to Symbolic Regression. CoRR abs/2402.06299 (2024) - 2023
- [j7]Weidong Zhu, Jun Sun, Simin Wang, Kaifeng Yang, Jifeng Shen, Xin Zhou:
Segmentation and recognition of filed sweet pepper based on improved self-attention convolutional neural networks. Multim. Syst. 29(1): 223-234 (2023) - [c18]Kaifeng Yang, Michael Affenzeller:
Surrogate-assisted Multi-objective Optimization via Genetic Programming Based Symbolic Regression. EMO 2023: 176-190 - [c17]Bernhard Werth, Johannes Karder, Andreas Beham, Erik Pitzer, Kaifeng Yang, Stefan Wagner:
Walking through the Quadratic Assignment-Instance Space: Algorithm Performance and Landscape Measures. GECCO Companion 2023: 2108-2114 - [c16]Kaifeng Yang, Kai Chen, Michael Affenzeller, Bernhard Werth:
A New Acquisition Function for Multi-objective Bayesian Optimization: Correlated Probability of Improvement. GECCO Companion 2023: 2308-2317 - [c15]Kaifeng Yang, Sixuan Liu, Michael Affenzeller, Guozhi Dong:
Gradients of Acquisition Functions for Bi-objective Bayesian optimization. ICNC-FSKD 2023: 1-9 - [c14]Fu Xing Long, Diederick Vermetten, Anna V. Kononova, Roman Kalkreuth, Kaifeng Yang, Thomas Bäck, Niki van Stein:
Challenges of ELA-Guided Function Evolution Using Genetic Programming. IJCCI 2023: 119-130 - [p3]Hao Wang, Kaifeng Yang:
Bayesian Optimization. Many-Criteria Optimization and Decision Analysis 2023: 271-297 - [d1]Fu Xing Long, Diederick Vermetten, Anna V. Kononova, Roman Kalkreuth, Kaifeng Yang, Thomas Bäck, Niki van Stein:
Challenges of ELA-based Function Evolution using Genetic Programming - Reproducability files. Zenodo, 2023
- [i4]Fu Xing Long, Diederick Vermetten, Anna V. Kononova, Roman Kalkreuth, Kaifeng Yang, Thomas Bäck, Niki van Stein:
Challenges of ELA-guided Function Evolution using Genetic Programming. CoRR abs/2305.15245 (2023) - 2022
- [j6]Jun Sun, Kaifeng Yang, Chen Chen, Jifeng Shen, Yu Yang, Xiaohong Wu, Tomas Norton:
Wheat head counting in the wild by an augmented feature pyramid networks-based convolutional neural network. Comput. Electron. Agric. 193: 106705 (2022) - [j5]Jun Sun, Kaifeng Yang, Xiaofei He, Yuanqiu Luo, Xiaohong Wu, Jifeng Shen:
Beet seedling and weed recognition based on convolutional neural network and multi-modality images. Multim. Tools Appl. 81(4): 5239-5258 (2022) - [j4]Kaifeng Yang, Michael Affenzeller, Guozhi Dong:
A parallel technique for multi-objective Bayesian global optimization: Using a batch selection of probability of improvement. Swarm Evol. Comput. 75: 101183 (2022) - [j3]Kaifeng Yang, Yuliang Liu, Shiwen Zhang, Jiajian Cao:
Surface Defect Detection of Heat Sink Based on Lightweight Fully Convolutional Network. IEEE Trans. Instrum. Meas. 71: 1-12 (2022) - [c13]Michael Affenzeller, Michael Bögl, Lukas Fischer, Florian Sobieczky, Kaifeng Yang, Jan Zenisek:
Prescriptive Analytics: When Data- and Simulation-based Models Interact in a Cooperative Way. SYNASC 2022: 1-8 - [i3]Hao Wang, Kaifeng Yang, Michael Affenzeller, Michael Emmerich:
Probability Distribution of Hypervolume Improvement in Bi-objective Bayesian Optimization. CoRR abs/2205.05505 (2022) - [i2]Kaifeng Yang, Guozhi Dong, Michael Affenzeller:
A Parallel Technique for Multi-objective Bayesian Global Optimization: Using a Batch Selection of Probability of Improvement. CoRR abs/2208.03685 (2022) - 2021
- [c12]Kaifeng Yang, Michael Affenzeller:
Quantifying Uncertainties of Residuals in Symbolic Regression via Kriging. ISM 2021: 954-961 - 2020
- [p2]Michael T. M. Emmerich, Kaifeng Yang, André H. Deutz:
Infill Criteria for Multiobjective Bayesian Optimization. High-Performance Simulation-Based Optimization 2020: 3-16
2010 – 2019
- 2019
- [j2]Kaifeng Yang, Michael Emmerich, André H. Deutz, Thomas Bäck:
Efficient computation of expected hypervolume improvement using box decomposition algorithms. J. Glob. Optim. 75(1): 3-34 (2019) - [j1]Kaifeng Yang, Michael Emmerich, André H. Deutz, Thomas Bäck:
Multi-Objective Bayesian Global Optimization using expected hypervolume improvement gradient. Swarm Evol. Comput. 44: 945-956 (2019) - [c11]André H. Deutz, Michael Emmerich, Kaifeng Yang:
The Expected R2-Indicator Improvement for Multi-objective Bayesian Optimization. EMO 2019: 359-370 - [c10]Kaifeng Yang, Pramudita Satria Palar, Michael Emmerich, Koji Shimoyama, Thomas Bäck:
A multi-point mechanism of expected hypervolume improvement for parallel multi-objective bayesian global optimization. GECCO 2019: 656-663 - [i1]Kaifeng Yang, Michael Emmerich, André H. Deutz, Thomas Bäck:
Efficient Computation of Expected Hypervolume Improvement Using Box Decomposition Algorithms. CoRR abs/1904.12672 (2019) - 2018
- [c9]Pramudita Satria Palar, Kaifeng Yang, Koji Shimoyama, Michael Emmerich, Thomas Bäck:
Multi-objective aerodynamic design with user preference using truncated expected hypervolume improvement. GECCO 2018: 1333-1340 - 2017
- [c8]Yali Wang, Longmei Li, Kaifeng Yang, Michael T. M. Emmerich:
A new approach to target region based multiobjective evolutionary algorithms. CEC 2017: 1757-1764 - [c7]Kaifeng Yang, Michael Emmerich, André H. Deutz, Carlos M. Fonseca:
Computing 3-D Expected Hypervolume Improvement and Related Integrals in Asymptotically Optimal Time. EMO 2017: 685-700 - 2016
- [c6]Kaifeng Yang, André H. Deutz, Zhiwei Yang, Thomas Bäck, Michael T. M. Emmerich:
Truncated expected hypervolume improvement: Exact computation and application. CEC 2016: 4350-4357 - [c5]Kaifeng Yang, Longmei Li, André H. Deutz, Thomas Bäck, Michael Emmerich:
Preference-based multiobjective optimization using truncated expected hypervolume improvement. ICNC-FSKD 2016: 276-281 - [c4]Zhiwei Yang, Hao Wang, Kaifeng Yang, Thomas Bäck, Michael Emmerich:
SMS-EMOA with multiple dynamic reference points. ICNC-FSKD 2016: 282-288 - [p1]Michael Emmerich, Kaifeng Yang, André H. Deutz, Hao Wang, Carlos M. Fonseca:
A Multicriteria Generalization of Bayesian Global Optimization. Advances in Stochastic and Deterministic Global Optimization 2016: 229-242 - 2015
- [c3]Kaifeng Yang, Daniel Gaida, Thomas Bäck, Michael Emmerich:
Expected hypervolume improvement algorithm for PID controller tuning and the multiobjective dynamical control of a biogas plant. CEC 2015: 1934-1942 - [c2]Iris Hupkens, André H. Deutz, Kaifeng Yang, Michael T. M. Emmerich:
Faster Exact Algorithms for Computing Expected Hypervolume Improvement. EMO (2) 2015: 65-79 - 2014
- [c1]Kaifeng Yang, Michael T. M. Emmerich, Rui Li, Ji Wang, Thomas Bäck:
Power Distribution Network Reconfiguration by Evolutionary Integer Programming. PPSN 2014: 11-23
Coauthor Index
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