IEEE Computational Intelligence Magazine - February 2021 - 32

❏❏ Theoretical study on whether knowledge acquired in a partic-

ular source problem can improve the evolutionary search in a
target problem (for a specific type of optimization problem).
❏❏ Theoretical analysis of the correlation between the amount
of knowledge transferred and the improvement achieved in
the target problem.
❏❏ Definition on optimal inter-task mapping and how transfer efficacy can be impacted by the constructed inter-task mapping.
❏❏ Definition on useful representation of knowledge that can
be transferred across heterogeneous problem domains.
V. Conclusions

ETO is a generic and emerging paradigm that integrates EA
solvers with knowledge learning and transfer across related
domains to achieve efficient and better optimization performance. In this paper, we have presented an overview of existing
ETO approaches based on the type of problems being solved,
which includes ETO for Optimization in Uncertain Environment, ETO for Multitask Optimization, ETO for Complex
Optimization, ETO for Multi/Many-Objective Optimization,
and ETO for Machine Learning Applications. This study shows
that with knowledge learned and transferred across related
problem domains, the search and optimization capability of
EAs can be improved, when properly harnessed.
As one of the emerging research areas in computational
intelligence, there are many challenges and open research questions in ETO. We have discussed some of the challenges and
potential research directions, such as ETO for large-scale optimization, ETO for multi-form optimization, ETO for deep
learning, ETO in complex data environment, and theoretical
study of ETO. Since problems often share certain correlations in
nature, it is believed that ETO can become an important technology for tackling the ever-increasing complexity and interdependency of optimization problems currently encountered in
real-world applications.
Acknowledgment

This work is partially supported by the National Key Research and
Development Project, Ministry of Science and Technology, China
(Grant No. 2018AAA0101301), the National Natural Science
Foundation of China (NSFC) under Grant No. 61876162, 61876025,
and 61673328, the Venture & Innovation Support Program for
Chongqing Overseas Returnees under Grant No. cx2018044 and
cx2019020, and the Research Grants Council of the Hong Kong
SAR under grant No. CityU11202418 and No. CityU11209219.
References

[1] R. Salomon, " Evolutionary algorithms and gradient search: similarities and differences, " IEEE Trans. Evol. Comput., 2(2):45-55, 1998. doi: 10.1109/4235.728207.
[2] G. B. Dantzig and N. N. Thapa, Linear Programming 1: Introduction. Berlin: SpringerVerlag, 1997.
[3] Z. Dostl, Optimal Quadratic Programming Algorithms: With Applications to Variational Inequalities, 1st ed. Springer-Verlag, 2009.
[4] S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge Univ. Press, 2004.
[5] M. M. Islam, H. K. Singh, and T. Ray, " A surrogate assisted approach for single-objective bilevel optimization, " IEEE Trans. Evol. Comput., 21(5):681-696, 2017. doi: 10.1109/
TEVC.2017.2670659.
[6] B. Li, J. Li, K. Tang, and X. Yao, " Many-objective evolutionary algorithms: A survey, "
ACM Comput. Surv., 48(1):1-35, 2015. doi: 10.1145/2792984.

32

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | FEBRUARY 2021

[7] S. Yang and X. Yao, Eds. Evolutionary Computation for Dynamic Optimization Problems
(Studies in Computational Intelligence). Berlin: Springer-Verlag, 2013.
[8] Y. Jin, Ed. Knowledge Incorporation in Evolutionary Computation. Berlin: Springer-Verlag, 2005.
[9] S. J. Pan and Q. Yang, A survey on transfer learning. IEEE Trans. Knowl. Data Eng.,
22(10):1345-1359, 2010. doi: 10.1109/TKDE.2009.191.
[10] A. Ng. 2016. https://nips.cc/conferences/2016/tutorialsintros
[11] C. Sferrazza and R. DAndrea, " Transfer learning for vision-based tactile sensing, " in
Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), 2019, pp. 7961-7967. doi: 10.1109/
IROS40897.2019.8967571.
[12] S. Ruder, M. E. Peters, S. Swayamdipta, and T. Wolf, " Transfer learning in natural
language processing, " in Proc. 2019 Conf. North Amer. Chap. Assoc. Comput. Linguist: Tuts.,
pp. 15-18. doi: 10.18653/v1/N19-5004.
[13] J. Kunze, L. Kirsch, I. Kurenkov, A. Krug, J. Johannsmeier, and S. Stober, " Transfer
learning for speech recognition on a budget, " in Proc. 2nd Workshop Represent. Learn. NLP,
2017, pp. 168-177. doi: 10.18653/v1/W17-2620.
[14] A. Gupta and Y. S. Ong, Memetic Computation: The Mainspring of Knowledge Transfer in
a Data-Driven Optimization Era. Springer-Verlag, 2019.
[15] M. Jiang, Z. Huang, L. Qiu, W. Huang, and G. G. Yen, " Transfer learning-based
dynamic multiobjective optimization algorithms, " IEEE Trans. Evol. Comput., 22(4):501-
514, 2018. doi: 10.1109/TEVC.2017.2771451.
[16] C. Yang, J. Ding, Y. Jin, C. Wang, and T. Chai, " Multitasking multiobjective evolutionary operational indices optimization of beneficiation processes, " IEEE Trans. Automat. Sci. Eng., 16(3):1046-1057, 2019. doi: 10.1109/TASE.2018.2865593.
[17] A. Gupta, Y. Ong, and L. Feng, " Multifactorial evolution: Toward evolutionary multitasking, " IEEE Trans. Evol. Comput., 20(3):343-357, 2016. doi: 10.1109/TEVC.2015.2458037.
[18] L. Feng et al., " Explicit evolutionary multitasking for combinatorial optimization: A
case study on capacitated vehicle routing problem, " IEEE Trans. Cybern., pp. 1-14, 2020.
doi: 10.1109/TCYB.2019.2962865.
[19] A. T. W. Min, Y. Ong, A. Gupta, and C. Goh, " Multiproblem surrogates: Transfer
evolutionary multiobjective optimization of computationally expensive problems, " IEEE
Trans. Evol. Comput., 23(1):15-28, 2019. doi: 10.1109/TEVC.2017.2783441.
[20] H. Nazif and L. S. Lee, " Optimised crossover genetic algorithm for capacitated
vehicle routing problem, " Appl. Math. Model., 36(5):2110-2117, 2012. doi: 10.1016/j.
apm.2011.08.010.
[21] M. N. Omidvar, X. Li, Y. Mei, and X. Yao, " Cooperative co-evolution with differential grouping for large scale optimization, " IEEE Trans. Evol. Comput., 18(3):378-393,
2014. doi: 10.1109/TEVC.2013.2281543.
[22] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, " A fast and elitist multiobjective genetic algorithm: NSGA-II, " IEEE Trans. Evol. Comput., 6(2):182C-197, 2002. doi:
10.1109/4235.996017.
[23] V. Maniscalco, S. G. Polito, and A. Intagliata, " Tree-based genetic algorithm with
binary encoding for QoS routing, " in Proc. 7th Int. Conf. Innov. Mobile Internet Serv. Ubiquitous Comput., 2013, pp. 101-107. doi: 10.1109/IMIS.2013.26.
[24] U. K. Chakraborty and C. Z. Janikow, " An analysis of gray versus binary encoding in
genetic search, " Inf. Sci., 156(3):253-269, 2003. doi: 10.1016/S0020-0255(03)00178-6.
[25] D. E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning, 1st
ed. Addison-Wesley, 1989.
[26] A. Gupta, Y. Ong, and L. Feng, " Insights on transfer optimization: Because experience is the best teacher, " IEEE Trans. Emerg. Topics Comput. Intell., 2(1):51-64, 2018. doi:
10.1109/TETCI.2017.2769104.
[27] S. Marsland, Machine Learning: An Algorithmic Perspective, 2nd ed. Chapman & Hall, 2014.
[28] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. The MIT Press, 2016.
[29] L. Shao, F. Zhu, and X. Li, " Transfer learning for visual categorization: A survey, "
IEEE Trans. Neural Netw. Learn. Syst., 26(5):1019-1034, 2015. doi: 10.1109/TNNLS.
2014.2330900.
[30] K. Weiss, T. M. Khoshgoftaar, and D. D. Wang, " A survey of transfer learning, " J. Big
Data, 3(9), 2016. doi: 10.1186/s40537-016-0043-6.
[31] Y. Jin and J. Branke, " Evolutionary optimization in uncertain environments-a survey, " IEEE Trans. Evol. Comput., 9(3):303-317, 2005. doi: 10.1109/TEVC.2005.846356.
[32] H. Fu, P. R. Lewis, B. Sendhoff, K. Tang, and X. Yao, " What are dynamic optimization problems? " in Proc. IEEE Congr. Evol. Comput. (CEC), 2014, pp. 1550-1557. doi:
10.1109/CEC.2014.6900316.
[33] L. Zhou et al., " Solving dynamic vehicle routing problem via evolutionary search
with learning capability, " in Proc. IEEE Congr. Evol. Comput. (CEC), 2017, pp. 890-896.
doi: 10.1109/CEC.2017.7969403.
[34] I. Hatzakis and D. Wallace, " Dynamic multi-objective optimization with evolutionary algorithms: A forward-looking approach, " in Proc. 8th Annu. Conf. Genetic Evol.
Comput., 2006, pp. 1201-1208. doi: 10.1145/1143997.1144187.
[35] G. Ruan, L. L. Minku, S. Menzel, B. Sendhoff, and X. Yao, " When and how to
transfer knowledge in dynamic multi-objective optimization, " in Proc. 2019 IEEE Symp.
Series Comput. Intell. (SSCI), pp. 2034-2041. doi: 10.1109/SSCI44817.2019.9002815.
[36] M. Jiang, Z. Wang, L. Qiu, S. Guo, X. Gao, and K. C. Tan, " A fast dynamic evolutionary multiobjective algorithm via manifold transfer learning, " IEEE Trans. Cybern.,
early access, 2020. doi: 10.1109/TCYB.2020.2989465.
[37] M. Jiang, Z. Wang, H. Hong, and G. G. Yen, " Knee point based imbalanced transfer
learning for dynamic multi-objective optimization, " IEEE Trans. Evol. Comput., early
access, 2020. doi: 10.1109/TEVC.2020.3004027.
[38] J. Ding, C. Yang, Y. Jin, and T. Chai, " Generalized multitasking for evolutionary
optimization of expensive problems, " IEEE Trans. Evol. Comput., 23(1):44-58, 2019. doi:
10.1109/TEVC.2017.2785351.
[39] K. K. Bali, A. Gupta, L. Feng, Y. S. Ong, and Tan Puay Siew, " Linearized domain
adaptation in evolutionary multitasking, " in Proc. IEEE Congr. Evol. Comput. (CEC), 2017,
pp. 1295-1302. doi: 10.1109/CEC.2017.7969454.
https://www.nips.cc/conferences/2016/tutorialsintros
IEEE Computational Intelligence Magazine - February 2021

Table of Contents for the Digital Edition of IEEE Computational Intelligence Magazine - February 2021
