IEEE Computational Intelligence Magazine - May 2022 - 65

[14] A. Gupta and Y.-S. Ong, Memetic Computation: The Mainspring of Knowledge Transfer in
a Data-Driven Optimization Era. Springer-Verlag, 2018, vol. 21.
[15] X. Zheng, A. K. Qin, M. Gong, and D. Zhou, " Self-regulated evolutionary multitask
optimization, " IEEE Trans. Evol. Comput., vol. 24, no. 1, pp. 16-28, 2019, doi: 10.1109/
TEVC.2019.2904696.
[16] L. Zhou et al., " Toward adaptive knowledge transfer in multifactorial evolutionary
computation, " IEEE Trans. Cybern., 2020, doi: 10.1109/TCYB.2020.2974100.
[17] J. Yin, A. Zhu, Z. Zhu, Y. Yu, and X. Ma, " Multifactorial evolutionary algorithm enhanced
with cross-task search direction, " in Proc. IEEE Congr. Evol. Comput., 2019, pp. 2244-2251.
[18] X. Hao, R. Qu, and J. Liu, " A unified framework of graph-based evolutionary multitasking
hyper-heuristic, " IEEE Trans. Evol. Comput., 2020, doi: 10.1109/TEVC.2020.2991717.
[19] A. T. W. Min, Y.-S. Ong, A. Gupta, and C.-K. Goh, " Multiproblem surrogates:
Transfer evolutionary multiobjective optimization of computationally expensive problems, "
IEEE Trans. Evol. Comput., vol. 23, no. 1, pp. 15-28, 2017, doi: 10.1109/TEVC.2017.
2783441.
[20] K. K. Bali, A. Gupta, L. Feng, Y. S. Ong, and T. P. Siew, " Linearized domain adaptation
in evolutionary multitasking, " in Proc. IEEE Congr. Evol. Comput. (CEC), 2017, pp.
1295-1302, doi: 10.1109/CEC.2017.7969454.
[21] R. Lim, L. Zhou, A. Gupta, Y.-S. Ong, and A. N. Zhang, " Solution representation
learning in multi-objective transfer evolutionary optimization, " IEEE Access, vol. 9, pp.
41,844-41,860, 2021, doi: 10.1109/ACCESS.2021.3065741.
[22] Z. Liang, H. Dong, C. Liu, W. Liang, and Z. Zhu, " Evolutionary multitasking for
multiobjective optimization with subspace alignment and adaptive differential evolution, "
IEEE Trans. Cybern., 2020, doi: 10.1109/TCYB.2020.2980888.
[23] Z. Tang, M. Gong, Y. Wu, W. Liu, and Y. Xie, " Regularized evolutionary multitask
optimization: Learning to inter-task transfer in aligned subspace, " IEEE Trans. Evol.
Comput., 2020, doi: 10.1109/TEVC.2020.3023480.
[24] Z. Chen, Y. Zhou, X. He, and J. Zhang, " Learning task relationships in evolutionary
multitasking for multiobjective continuous optimization, " IEEE Trans. Cybern., 2020,
doi: 10.1109/TCYB.2020.3029176.
[25] R. Lim, A. Gupta, Y.-S. Ong, L. Feng, and A. N. Zhang, " Non-linear domain adaptation
in transfer evolutionary optimization, " Cogn. Comput., vol. 13, no. 2, pp. 290-307,
2021, doi: 10.1007/s12559-020-09777-7.
[26] K. K. Bali, Y.-S. Ong, A. Gupta, and P. S. Tan, " Multifactorial evolutionary algorithm
with online transfer parameter estimation: MFEA-II, " IEEE Trans. Evol. Comput.,
vol. 24, no. 1, pp. 69-83, 2019, doi: 10.1109/TEVC.2019.2906927.
[27] L. Feng et al., " Evolutionary multitasking via explicit autoencoding, " IEEE Trans.
Cybern., vol. 49, no. 9, pp. 3457-3470, 2018, doi: 10.1109/TCYB.2018.2845361.
[28] L. Bai, W. Lin, A. Gupta, and Y.-S. Ong, " From multitask gradient descent to gradient-free
evolutionary multitasking: A proof of faster convergence, " IEEE Trans. Cybern.,
2021, doi: 10.1109/TCYB.2021.3052509.
[29] T. Rios, B. van Stein, T. Bäck, B. Sendhoff, and S. Menzel, " Multi-task shape optimization
using a 3d point cloud autoencoder as unified representation, " IEEE Trans. Evol.
Comput., 2021, doi: 10.1109/TEVC.2021.3086308.
[30] F. Shen, J. Liu, and K. Wu, " Evolutionary multitasking network reconstruction from
time series with online parameter estimation, " Knowl.-Based Syst., vol. 222, p. 107,019, 2021.
[31] K. Wu, C. Wang, and J. Liu, " Evolutionary multitasking multilayer network reconstruction, "
IEEE Trans. Cybern., 2021, doi: 10.1109/TCYB.2021.3090769.
[32] R. Sagarna and Y.-S. Ong, " Concurrently searching branches in software tests generation
through multitask evolution, " in Proc. IEEE Symp. Ser. Comput. Intell., 2016.
[33] A. Gupta, Y.-S. Ong, L. Feng, and K. C. Tan, " Multiobjective multifactorial optimization
in evolutionary multitasking, " IEEE Trans. Cybern., vol. 47, no. 7, pp. 1652-1665,
2016, doi: 10.1109/TCYB.2016.2554622.
[34] X. Ma et al., " Enhanced multifactorial evolutionary algorithm with meme helpertasks, "
IEEE Trans. Cybern., 2021.
[35] D. Wierstra, T. Schaul, T. Glasmachers, Y. Sun, J. Peters, and J. Schmidhuber, " Natural
evolution strategies, " J. Machine Learning Res., vol. 15, no. 1, pp. 949-980, 2014.
[36] Q. Zhang and H. Muhlenbein, " On the convergence of a class of estimation of distribution
algorithms, " IEEE Trans. Evol. Comput., vol. 8, no. 2, pp. 127-136, 2004, doi:
10.1109/TEVC.2003.820663.
[37] C. Yang, J. Ding, K. C. Tan, and Y. Jin, " Two-stage assortative mating for multiobjective
multifactorial evolutionary optimization, " in Proc. IEEE 56th Annu. Conf. Decision
Control (CDC), 2017, pp. 76-81, doi: 10.1109/CDC.2017.8263646.
[38] L. Feng et al., " An empirical study of multifactorial PSO and multifactorial de, " in Proc.
IEEE Congr. Evol. Comput. (CEC), 2017, pp. 921-928, doi: 10.1109/CEC.2017.7969407.
[39] J. Tang, Y. Chen, Z. Deng, Y. Xiang, and C. P. Joy, " A group-based approach to
improve multifactorial evolutionary algorithm. " in Proc. IJCAI, 2018, pp. 3870-3876.
[40] A. Gupta, Y.-S. Ong, B. Da, L. Feng, and S. D. Handoko, " Landscape synergy in evolutionary
multitasking, " in Proc. IEEE Congr. Evol. Comput. (CEC), 2016, pp. 3076-3083,
doi: 10.1109/CEC.2016.7744178.
[41] R. Hashimoto, H. Ishibuchi, N. Masuyama, and Y. Nojima, " Analysis of evolutionary
multi-tasking as an island model, " in Proc. Genetic Evol. Comput. Conf. Companion,
2018, pp. 1894-1897, doi: 10.1145/3205651.3208228.
[42] R.-T. Liaw and C.-K. Ting, " Evolutionary manytasking optimization based on symbiosis
in biocoenosis, " in Proc. AAAI Conf. Artif. Intell., vol. 33, 2019, pp. 4295-4303, doi:
10.1609/aaai.v33i01.33014295.
[43] J. Lin, H.-L. Liu, B. Xue, M. Zhang, and F. Gu, " Multiobjective multitasking optimization
based on incremental learning, " IEEE Trans. Evol. Comput., vol. 24, pp. 824-
838, 2019, doi: 10.1109/TEVC.2019.2962747.
[44] L. Zhou, L. Feng, A. Gupta, and Y.-S. Ong, " Learnable evolutionary search across
heterogeneous problems via kernelized autoencoding, " IEEE Trans. Evol. Comput.,
vol. 25, no. 3, pp. 567-581, 2021, doi: 10.1109/TEVC.2021.3056514.
[45] D. H. Wolpert and W. G. Macready, " No free lunch theorems for optimization, "
IEEE Trans. Evol. Comput., vol. 1, no. 1, pp. 67-82, 1997, doi: 10.1109/4235.585893.
[46] B. Xue, M. Zhang, W. N. Browne, and X. Yao, " A survey on evolutionary computation
approaches to feature selection, " IEEE Trans. Evol. Comput., vol. 20, no. 4, pp.
606-626, 2015, doi: 10.1109/TEVC.2015.2504420.
[47] S. Huang, J. Zhong, and W. Yu, " Surrogate-assisted evolutionary framework with adaptive
knowledge transfer for multi-task optimization, " IEEE Trans. Emerg. Topics Comput., 2019.
[48] Y. Liu, Y. Sun, B. Xue, M. Zhang, G. G. Yen, and K. C. Tan, " A survey on evolutionary
neural architecture search, " IEEE Trans. Neural Netw. Learning Syst., 2021, doi:
10.1109/TNNLS.2021.3100554.
[49] G. Morse and K. O. Stanley, " Simple evolutionary optimization can rival stochastic
gradient descent in neural networks, " in Proc. Genetic Evol. Comput. Conf. 2016, 2016,
pp. 477-484.
[50] X. Cui, W. Zhang, Z. Tüske, and M. Picheny, " Evolutionary stochastic gradient descent
for optimization of deep neural networks, " in Proc. Adv. Neural Inf. Process. Syst., 2018, vol. 31.
[51] B. Zhang, " Training deep neural networks via multi-task optimisation, " Ph.D. dissertation,
Swinburne Univ. Technol., Melbourne, Australia, 2020.
[52] R. Chandra, A. Gupta, Y.-S. Ong, and C.-K. Goh, " Evolutionary multi-task learning
for modular knowledge representation in neural networks, " Neural Process. Lett., vol.
47, pp. 993-1009, 2018, doi: 10.1007/s11063-017-9718-z.
[53] N. Zhang, G. Abhishek, Z. Chen, and Y.-S. Ong, " Evolutionary machine learning
with minions: A case study in feature selection, " IEEE Trans. Evol. Comput., 2021, doi:
10.1109/TEVC.2021.3099289.
[54] C. Wang, K. Wu, and J. Liu, " Evolutionary multitasking AUC optimization, " 2022,
arXiv:2201.01145.
[55] K. Chen, B. Xue, M. Zhang, and F. Zhou, " An evolutionary multitasking-based
feature selection method for high-dimensional classification, " IEEE Trans. Cybern., 2020,
doi: 10.1109/TCYB.2020.3042243.
[56] K. Chen, B. Xue, M. Zhang, and F. Zhou, " Evolutionary multitasking for feature
selection in high-dimensional classification via particle swarm optimisation, " IEEE Trans.
Evol. Comput., 2021.
[57] D. Golovin, B. Solnik, S. Moitra, G. Kochanski, J. Karro, and D. Sculley, " Google
Vizier: A service for black-box optimization, " in Proc. 23rd ACM SIGKDD Int. Conf.
Knowl. Discovery Data Mining, 2017, pp. 1487-1495.
[58] Y.-W. Wen and C.-K. Ting, " Learning ensemble of decision trees through multifactorial
genetic programming, " in Proc. IEEE Congr. Evol. Comput. (CEC), 2016, pp.
5293-5300, doi: 10.1109/CEC.2016.7748363.
[59] B. Zhang, A. K. Qin, and T. Sellis, " Evolutionary feature subspaces generation for
ensemble classification, " in Proc. Genetic Evol. Comput. Conf., 2018, pp. 577-584.
[60] J. Shi, T. Shao, X. Liu, X. Zhang, Z. Zhang, and Y. Lei, " Evolutionary multitask
ensemble learning model for hyperspectral image classification, " IEEE J. Sel. Topics Appl.
Earth Observations Remote Sens., vol. 14, pp. 936-950, 2020, doi: 10.1109/JSTARS.2020.
3037353.
[61] H. Li, Y.-S. Ong, M. Gong, and Z. Wang, " Evolutionary multitasking sparse reconstruction:
Framework and case study, " IEEE Trans. Evol. Comput., vol. 23, no. 5, pp.
733-747, 2018, doi: 10.1109/TEVC.2018.2881955.
[62] Y. Zhao, H. Li, Y. Wu, S. Wang, and M. Gong, " Endmember selection of hyperspectral
images based on evolutionary multitask, " in Proc. IEEE Congr. Evol. Comput. (CEC),
2020, pp. 1-7, doi: 10.1109/CEC48606.2020.9185673.
[63] J. Li, H. Li, Y. Liu, and M. Gong, " Multi-fidelity evolutionary multitasking optimization
for hyperspectral endmember extraction, " Appl. Soft Comput., p. 107713, 2021.
[64] K. Swersky, J. Snoek, and R. P. Adams, " Multi-task bayesian optimization, " in Proc.
Adv. Neural Inf. Process. Syst., vol. 26, pp. 2004-2012, 2013.
[65] R. P. Adams, R. J. Snoek, and K. Swersky, " Systems and methods for multi-task
Bayesian optimization, " U. S. Patent 9 858 529, Jan. 2, 2018.
[66] S. Jayaratna, " Understanding university students' journey using advanced data analytics, "
Ph.D. dissertation, Swinburne Univ. Technol, Melbourne, Australia, 2021.
[67] Y. Bi, B. Xue, and M. Zhang, " Learning and sharing: A multitasking genetic programming
approach to image feature learning, " IEEE Trans. Evol. Comput., 2021, doi:
10.1109/TEVC.2021.3097043.
[68] J. Zhong, L. Feng, W. Cai, and Y.-S. Ong, " Multifactorial genetic programming for
symbolic regression problems, " IEEE Trans. Syst., Man, Cybern., Syst., 2018.
[69] A. D. Martinez, J. Del Ser, E. Osaba, and F. Herrera, " Adaptive multi-factorial evolutionary
optimization for multi-task reinforcement learning, " IEEE Trans. Evol. Comput.,
2021, doi: 10.1109/TEVC.2021.3083362.
[70] E. O. Scott and K. A. De Jong, " Multitask evolution with cartesian genetic programming, "
in Proc. Genetic Evol. Comput. Conf. Companion, 2017, pp. 255-256.
[71] E. O. Scott and K. A. De Jong, " Automating knowledge transfer with multi-task
optimization, " in Proc. IEEE Congr. Evolutionary Comput. (CEC), 2019, pp. 2252-2259.
[72] J. Zhong, Y.-S. Ong, and W. Cai, " Self-learning gene expression programming, " IEEE
Trans. Evol. Comput., vol. 20, no. 1, pp. 65-80, 2015, doi: 10.1109/TEVC.2015.2424410.
[73] J. C. Bongard, " Evolutionary robotics, " Commun. ACM, vol. 56, no. 8, pp. 74-83,
2013, doi: 10.1145/2492007.2493883.
[74] A. Cangelosi, J. Bongard, M. H. Fischer, and S. Nolfi, " Embodied intelligence, " pp.
697-714, 2015.
[75] A. Moshaiov and A. Tal, " Family bootstrapping: A genetic transfer learning approach
for onsetting the evolution for a set of related robotic tasks, " in Proc. IEEE Congr. Evol.
Comput. (CEC), 2014, pp. 2801-2808, doi: 10.1109/CEC.2014.6900571.
[76] J.-B. Mouret and G. Maguire, " Quality diversity for multi-task optimization, " in
Proc. Genetic Evol. Comput. Conf., 2020, pp. 121-129.
[77] J.-B. Mouret and J. Clune, " Illuminating search spaces by mapping elites, " 2015,
arXiv:1504.04909.
MAY 2022 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 65

IEEE Computational Intelligence Magazine - May 2022

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