This repository provides the MATLAB implementations of an empirical investigation of transfer evolutionary algorithms. Particularly, a wide variety of techniques surrounding the three central issues in knowledge transfer, i.e., what to transfer, when to transfer, and how to transfer, are separately studied in this work, which are summarized as follows:
| Central Issue | Description | Abbreviation |
|---|---|---|
| What to Transfer | The hamming distance between intermediate solutions [1] The Euclidean distance between the population means [2-4] The KLD distance [5,6] The WD distance [7,8] The ordinal correlation [9-11] The relaxed ordinal correlation [12,13] The subspace alignment [14,15] |
H M1 KLD WD OC ROC SA |
| When to Transfer | The fixed generation interval for knowledge transfer [16-19] The estimated transfer intensity based on the mixture model [20] The estimated transfer intensity based on the population distributions [21] The estimated transfer intensity based on the representation models [22] |
F-G_t D-M D-P D-G |
| How to Transfer | The translation transformation based on the elite solutions [2] The translation transformation based on the randomly selected solutions [3] The translation transformation based on the population means The multiplication transformation based on the estimated means [4] The affine transformation [8] The linear transformation [23] The affine transformation [16] The kernelized mapping [11] The neural network model [10,17] The linear transformations connected by a latent space [12,13] The linear transformations connected by the subspaces [14,15] |
M1-Te M1-Tr M1-Tm M1-M M2-A OC-L OC-A OC-K OC-N ROC-L SA-L |
[1] Learning with case-injected genetic algorithms. S. J. Louis and J. McDonnell. TEVC 2004. paper
[2] Generalized multitasking for evolutionary optimization of expensive problems. J. Ding, C. Yang, Y. Jin, and T. Chai. TEVC 2019. paper
[3] Multifactorial evolutionary algorithm enhanced with cross-task search direction. J. Yin, A. Zhu, Z. Zhu, Y. Yu, and X. Ma. CEC 2019. paper
[4] A hybrid of genetic transform and hyper-rectangle search strategies for evolutionary multi-tasking. Z. Liang, J. Zhang, L. Feng, and Z. Zhu. ESWA 2019. paper
[5] An adaptive archive-based evolutionary framework for many-task optimization. Y. Chen, J. Zhong, L. Feng, and J. Zhang. TETCI 2020. paper
[6] Surrogate-assisted evolutionary framework with adaptive knowledge transfer for multi-task optimization. S. Huang, J. Zhong, and W.-J. Yu. TETC 2021. paper
[7] Multisource selective transfer framework in multiobjective optimization problems. J. Zhang, W. Zhou, X. Chen, W. Yao, and L. Cao. TEVC 2020. paper
[8] Affine transformation-enhanced multifactorial optimization for heterogeneous problems. X. Xue, K. Zhang, K. C. Tan, L. Feng, J. Wang, G. Chen, X. Zhao, L. Zhang, and J. Yao. TCYB 2022. paper
[9] Evolutionary multitasking via explicit autoencoding. L. Feng, L. Zhou, J. Zhong, A. Gupta, Y.-S. Ong, K.-C. Tan, and A. K. Qin. TCYB 2018. paper
[10] Solution representation learning in multi-objective transfer evolutionary optimization. R. Lim, L. Zhou, A. Gupta, Y.-S. Ong, and A. N. Zhang. IEEE Access 2021. paper
[11] Learnable evolutionary search across heterogeneous problems via kernelized autoencoding. L. Zhou, L. Feng, A. Gupta, and Y.-S. Ong. TEVC 2021. paper
[12] Evolutionary sequential transfer optimization for objective-heterogeneous problems. X. Xue, C. Yang, Y. Hu, K. Zhang, Y.-M. Cheung, L. Song, and K. C. Tan. TEVC 2022. paper
[13] Learning task relationships in evolutionary multitasking for multiobjective continuous optimization. Z. Chen, Y. Zhou, X. He, and J. Zhang. TCYB 2022. paper
[14] Regularized evolutionary multitask optimization: Learning to intertask transfer in aligned subspace. Z. Tang, M. Gong, Y. Wu, W. Liu, and Y. Xie. TEVC 2021. paper
[15] Evolutionary multitasking for multiobjective optimization with subspace alignment and adaptive differential evolution. Z. Liang, H. Dong, C. Liu, W. Liang, and Z. Zhu. TCYB 2022. paper
[16] Autoencoding evolutionary search with learning across heterogeneous problems. L. Feng, Y. Ong, S. Jiang, and A. Gupta. TEVC 2017. paper
[17] Non-linear domain adaptation in transfer evolutionary optimization. R. Lim, A. Gupta, Y.-S. Ong, L. Feng, and A. N. Zhang. Cognitive Computation 2021. paper
[18] Multitasking multiobjective optimization based on transfer component analysis. Z. Hu, Y. Li, H. Sun, and X. Ma. Information Sciences 2022. paper
[19] Multitasking optimization via an adaptive solver multitasking evolutionary framework. Y. Li, W. Gong, and S. Li. Information Sciences 2023. paper
[20] Multifactorial Evolutionary Algorithm With Online Transfer Parameter Estimation: MFEA-II. K. K. Bali, Y. S. Ong, A. Gupta, and P. S. Tan. TEVC 2019. paper
[21] Evolutionary multi-task optimization with hybrid knowledge transfer strategy. Y. Cai, D. Peng, P. Liu, and J. Guo. Information Sciences 2021. paper
[22] Self-adaptive multifactorial evolutionary algorithm for multitasking production optimization. J. Yao, Y. Nie, Z. Zhao, X. Xue, K. Zhang, C. Yao, L. Zhang, J. Wang, and Y. Yang. JPSE 2021. paper
[23] Linearized domain adaptation in evolutionary multitasking. K. K. Bali, A. Gupta, L. Feng, Y. S. Ong, and T. P. Siew. CEC 2017. paper
If you find this repo useful for your research, please consider to cite:
@article{Xue2023,
title = {An Empirical Study on Knowledge Transfer in Evolutionary Sequential Transfer Optimization},
author = {Xue, Xiaoming and Yang, Cuie and Feng, Liang and Zhang, Kai and Song, Linqi and Tan, Kay Chen}
journal = {...},
volume = {...},
pages = {...},
year = {2023},
doi = {https://...},
url = {http://...},
}...