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in Computing Conference (CLEI), 2015 Latin American (IEEE, New York, 2015), pp. 1–11
85. Y.-N. Wang, L.-H. Wu, X.-F. Yuan, Multi-objective self-adaptive differential evolution with
elitist archive and crowding entropy-based diversity measure. Soft Comput. 14(3), 193–209
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86. R. Cao, G. Li, Y. Wu, A self-adaptive evolutionary algorithm for multi-objective optimization,
in Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial
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67. A. Zamuda, J. Brest, B. Boškovi´ c, V. Žumer, Large scale global optimization using differential
evolution with self-adaptation and cooperative co-evolution, in IEEE Congress on Evolutionary
Computation (IEEE World Congress on Computational Intelligence), June 2008, pp. 3718–
3725
68. S. Das, S.S. Mullick, P. Suganthan, Recent advances in differential evolution—an updated
survey. Swarm Evol. Comput. 27, 1–30 (2016) [Online]. Available http://www.sciencedirect.
com/science/article/pii/S2210650216000146
69. T.T. Nguyen, S. Yang, J. Branke, Evolutionary dynamic optimization: a survey of the state of
the art. Swarm Evol. Comput. 6, 1–24 (2012) [Online]. Available http://www.sciencedirect.
com/science/article/pii/S2210650212000363
70. H. Wang, D. Wang, S. Yang, A memetic algorithm with adaptive hill climbing strategy for
dynamic optimization problems. Soft Comput. 13(8–9), 763–780 (2009)
71. J. Brest, A. Zamuda, B. Boškovi´ c, M. S. Mauˇ cec, V. Žumer, Dynamic optimization using selfadaptive differential evolution, in IEEE Congress on Evolutionary Computation, 2009. CEC’09
(IEEE, New York, 2009), pp. 415–422
72. J. Branke, Evolutionary approaches to dynamic optimization problems - updated survey, in
GECCO Workshop on Evolutionary Algorithms for Dynamic Optimization Problems (2001),
pp. 27–30
73. Y. Jin, J. Branke, Evolutionary optimization in uncertain environments-a survey. IEEE Trans.
Evol. Comput. 9(3), 303–317 (2005)
74. H. Greiner, Robust optical coating design with evolutionary strategies. Appl. Opt. 35(28),
5477–5483 (1996) [Online]. Available http://ao.osa.org/abstract.cfm?URI=ao-35-28-5477
75. B.L. Miller, Noise, sampling, and efficient genetic algorithms, Ph.D. dissertation, University
of Illinois at Urbana-Champaign, 1997
76. J.M. Fitzpatrick, J.J. Grefenstette, Genetic algorithms in noisy environments. Mach. Learn.
3(2–3), 101–120 (1988)
77. L.T. Bui, H.A. Abbass, D. Essam, Fitness inheritance for noisy evolutionary multi-objective
optimization, in Proceedings of the 7th Annual Conference on Genetic and Evolutionary
Computation (ACM, New York, 2005), pp. 779–785
78. J. Teich, Pareto-front exploration with uncertain objectives, in International Conference on
Evolutionary Multi-Criterion Optimization (Springer, New York, 2001), pp. 314–328
79. M. Vallejo, D.W. Corne, Evolutionary algorithms under noise and uncertainty: a locationallocation case study, in 2016 IEEE Symposium Series on Computational Intelligence (SSCI)
(2016), pp. 1–10
80. D. Yang, L. Jiao, M. Gong, Adaptive multi-objective optimization based on nondominated
solutions. Computat. Intell. 25(2), 84–108 (2009)
81. B. Naujoks, H. Trautmann, Online convergence detection for multiobjective aerodynamic
applications, in 2009 IEEE Congress on Evolutionary Computation, May 2009, pp. 332–339
82. C. Igel, N. Hansen, S. Roth, Covariance matrix adaptation for multi-objective optimization.
Evol. Comput. 15(1), 1–28 (2007)
83. N. Beume, B. Naujoks, M. Emmerich, SMS-EMOA: multiobjective selection based on
dominated hypervolume. Eur. J. Oper. Res. 181(3), 1653–1669 (2007) [Online]. Available
http://www.sciencedirect.com/science/article/pii/S0377221706005443
84. N. Riquelme, C. Von Lücken, B. Baran, Performance metrics in multi-objective optimization,
in Computing Conference (CLEI), 2015 Latin American (IEEE, New York, 2015), pp. 1–11
85. Y.-N. Wang, L.-H. Wu, X.-F. Yuan, Multi-objective self-adaptive differential evolution with
elitist archive and crowding entropy-based diversity measure. Soft Comput. 14(3), 193–209
(2010)
86. R. Cao, G. Li, Y. Wu, A self-adaptive evolutionary algorithm for multi-objective optimization,
in Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial
Intelligence, ed. by D.-S. Huang, L. Heutte, M. Loog (Springer, Berlin, Heidelberg, 2007),
pp. 553–564