IEEE Computational Intelligence Magazine - February 2023 - 103

[3] K. V. Price, " An introduction to differential evolution, "
in New Ideas in Optimization.U.K., GBR:
McGraw-Hill Ltd., 1999, pp. 79-108.
[4] S. Das and P. N. Suganthan, " Differential evolution:
A survey ofthe state-of-the-art, " IEEETrans. Evol. Comput.,
vol. 15, no. 1, pp. 4-31, Feb. 2011.
[5] R. Eberhart and J. Kennedy, " A new optimizer
using particle swarm theory, " in Proc. 6th Int. Symp.
Micro Mach. Hum. Sci., 1995, pp. 39-43.
[6] N. Hansen, S. D. Muller, and P. Koumoutsakos,
" Reducing the time complexity ofthe derandomized evolution
strategy with covariance matrix adaptation (CMAES), "
Evol.Comput., vol. 11, no. 1, pp. 1-18,Mar. 2003.
[7] Z. Michalewicz, Genetic Algorithms + Data Structures
= Evolution Programs. Berlin, Germany: Springer,
1996.
[8] H. Zhang, J. Sun, and Z. Xu, " Adaptive structural
hyper-parameter configuration by Q-learning, " in
Proc. IEEE Congr. Evol. Comput., 2020, pp. 1-8.
[9] C. Huang, Y. Li, and X. Yao, " A survey of
automatic parameter tuning methods for metaheuristics, "
IEEE Trans. Evol. Comput., vol. 24, no. 2,
pp. 201-216, Apr. 2020.
[10] J. Zhang and A. C. Sanderson, " JADE: Adaptive
differential evolution with optimal external archive, "
IEEE Trans. Evol. Comput., vol. 13, no. 5, pp. 945-
458, Oct. 2009.
[11] R. Tanabe and A. Fukunaga, " Success-history
based parameter adaptation for differential evolution, "
in Proc. IEEE Congr. Evol. Comput., 2013, pp. 71-78.
[12] R. Tanabe and A. S. Fukunaga, " Improving the
search performance of shade using linear population
size reduction, " in Proc. IEEE Congr. Evol. Comput.,
2014, pp. 1658-1665.
[13] A. P. Piotrowski, " Review of differential evolution
population size, " Swarm Evol. Comput., vol. 32,
pp. 1-24, 2017.
[14] M. Andrychowicz et al., " Learning to learn by
gradient descent by gradient descent, " in Proc.
Conf. Workshop Neural Inf. Process. Syst., 2016,
pp. 3981-3989.
[15] K. Li and J. Malik, " Learning to optimize, " in
Proc. Int. Conf. Learn. Representations, 2017.
[16] R. Sutton and A. Barto, Reinforcement Learning:
An Introduction. Cambridge, MA, USA: MIT Press,
1998.
[17] G. Zhang and Y. Shi, " Hybrid sampling evolution
strategy for solving single objective bound constrained
problems, " in Proc. IEEE Congr. Evol. Comput., 2018,
pp. 1-7.
[18] P. I. Frazier, " A tutorial on Bayesian optimization, "
2018, arXiv:1807.02811.
[19] F. Hutter, H. H. Hoos, and K. Leytonbrown,
" Sequential model-based optimization for general
algorithm configuration, " in Proc. Int. Conf. Learn.
Intell. Optim., 2011, pp. 507-523.
[20] F. Hutter, H. H. Hoos, K. Leyton-Brown, and
T. Stutzle, " ParamILS: An automatic algorithm configuration
framework, " J. Artif. Intell. Res., vol. 36,
pp. 267-306, 2009.
[21] I. Roman, J. Ceberio, A. Mendiburu, and J. A.
Lozano, " Bayesian optimization for parameter tuning
in evolutionary algorithms, " in Proc. IEEE Congr.
Evol. Comput., 2016, pp. 4839-4845.
[22] C. Huang, B. Yuan, Y. Li, and X. Yao, " Automatic
parameter tuning using bayesian optimization
method, " in Proc. IEEE Congr. Evol. Comput., 2019,
pp. 2090-2097.
[23] K. Tang, J. Wang, X. Li, and X. Yao, " A
scalable approach to capacitated arc routing problems
based on hierarchical decomposition, " IEEE
Trans. Cybern., vol. 47, no. 11, pp. 3928-3940,
Nov. 2017.
[24] A. K. Qin, V. L. Huang, and P. N. Suganthan,
" Differential evolution algorithm with strategy adaptation
for global numerical optimization, " IEEE
Trans. Evol. Comput., vol. 13, no. 2, pp. 398-417,
Apr. 2009.
[25] Z. Yang, K. Tang, and X. Yao, " Self-adaptive differential
evolution with neighborhood search, " in Proc.
IEEE Congr. Evol. Comput., 2008, pp. 1110-1116.
[26] J. Brest, M. S. Maucec, and B. Boskovic, " iLSHADE:
Improved L-SHADE algorithm for single
objective real-parameter optimization, " in Proc. IEEE
Congr. Evol. Comput., 2016, pp. 1188-1195.
[27] J. Brest, M. S. Maucec, and B. BOskovic, " Single
objective realparameter optimization: Algorithm, " in
Proc. IEEE Congr. Evol. Comput., 2017, pp. 1311-
1318.
[28] Z. H. Zhan, J. Zhang, Y. Li, and H. S. H.
Chung, " Adaptive particle swarm optimization, "
IEEE Trans. Syst. Man, Cybern., vol. 39, no. 6,
pp. 1362-1381, Dec. 2009.
[29] J. Brest, S. Greiner, B. Boskovic, M. Mernik, and
V. Zumer, " Self-adapting control parameters in differential
evolution: A comparative study on numerical
benchmark problems, " IEEE Trans. Evol. Comput.,
vol. 10, no. 6, pp. 646-657, Dec. 2006.
[30] Y. Wang, H. Li, T. Huang, and L. Li, " Differential
evolution based on covariance matrix learning and
bimodal distribution parameter setting, " Appl. Soft
Comput., vol. 18, pp. 232-247, 2014.
[31] J. Teo, " Exploring dynamic self-adaptive populations
in differential evolution, " Soft Comput., vol. 10,
pp. 673-686, 2006.
[32] V. Tirronen and F. Neri, " Differential evolution
with fitness diversity self-adaptation, " in Nature
Inspired Algorithms for Optimisation. Berlin, Germany:
Springer, 2009, pp. 199-234.
[33] H. Wang, S. Rahnamayan, and Z. Wu, " Adaptive
differential evolution with variable population
size for solving high-dimensional problems, " in Proc.
IEEE Congr. Evol. Comput., 2011, pp. 2626-2632.
[34] G. Wu, R. Mallipeddi, P. Suganthan, R. Wang,
and H. Chen, " Differential evolution with multi-population
based ensemble of mutation strategies, " Inf.
Sci., vol. 329, pp. 329-345, 2016.
[35] W. Zhu, Y. Tang, J. an Fang, and W. Zhang,
" Adaptive population tuning scheme for differential
evolution, " Inf. Sci., vol. 223, pp. 164-191, 2013.
[36] A. Ajith, G. Crina, and I. Hisao, Hybrid Evolutionary
Algorithms, vol. 75. Berlin, Germany:Springer,
2007.
[37] T. A. A. Victoire and A. E. Jeyakumar, " Amodified
hybrid EP-SQP approach for dynamic dispatch
with valve-point effect, " Int.J. Elect. Power Energy Syst.,
vol. 27, no. 8, pp. 594-601, 2005.
[38] P.Attaviriyanupap,H.Kita,E.Tanaka,andJ.Hasegawa,
" A hybrid and for dynamic economic dispatch with
nonsmooth fuel cost function, " IEEE Trans. Power Syst.,
vol. 17, no. 2, pp. 411-416, May 2002.
[39] H. Wang, Y. Fu, M. Huang, G. Huang, and
J. Wang, " A hybrid evolutionary algorithm with adaptive
multi-population strategy for multi-objective
optimization problems, " Soft Comput.,vol.
21,
pp. 5975-5987, 2017.
[40] J. Tvrdık, " Competitive differential evolution, "
in Proc. MENDEL, 2006, pp. 7-12.
[41] S. Hochreiter and J. Schmidhuber, " Long shortterm
memory, " Neural Comput., vol. 9, no. 8,
pp. 1735-1780, 1997.
[42] S. Wang, J. Sun, and Z. Xu, " HyperAdam:
A learnable task-adaptive adam for network training, "
in Proc. Nat. Conf. Artif. Intell., 2019,
pp. 5297-5304.
[43] D. P. Kingma and J. Ba, " Adam: A method for
stochastic optimization, " in Proc. Int. Conf. Learn. Representations,
2015.
[44] Y. Chen et al., " Learning to learn without gradient
descent by gradient descent, " in Proc. Int. Conf.
Mach. Learn., 2017, pp. 748-756.
[45] M. Sharma, A. Komninos, M. Lopezibanez, and
D. Kazakov, " Deep reinforcement learning based
parameter control in differential evolution, " in Proc.
Genet. Evol. Comput. Conf., 2019, pp. 709-717.
[46] D. Silver et al., " Mastering the game ofwith deep
neural networks and tree search, " Nature, vol. 529,
no. 7587, pp. 484-489, 2016.
[47] V. Mnih et al., " Playing atari with deep reinforcement
learning, " Comput. Sci., pp. 1-9, 2013.
[48] S. Levine, C. Finn, T. Darrell, and P. Abbeel,
" End-to-end training of deep visuomotor policies, " J.
Mach. Learn. Res., vol. 17, no. 1, pp. 1334-1373, 2015.
[49] M. L. Puterman, Markov Decision Processes: Discrete
Stochastic Dynamic Programming. Hoboken, NJ, USA:
Wiley, 1994.
[50] Y. Tang and A. Kucukelbir, " Variational deep Q
network, " in Proc. Workshop Conf. Workshop Neural
Inf. Process. Syst., 2017.
[51] C. M. Bishop, Pattern Recognition and Machine
Learning. Berlin, Germany:Springer, 2006.
[52] R. Tanabe and A. Fukunaga, " Reviewing and
benchmarking parameter control methods in differential
evolution, " IEEE Trans. Cybern.,vol.50, no. 3,
pp. 1170-1184, Mar. 2020.
[53] J. Bader and E. Zitzler, " HypE: An algorithm
for fast hypervolume-based many-objective
optimization, " Evol. Comput., vol. 19, no. 1,
pp. 45-76, 2011.
FEBRUARY 2023 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 103
IEEE Computational Intelligence Magazine - February 2023

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