Surrogate-assisted evolutionary optimisation: a novel blueprint and a state of the art survey
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Cognitive Neuroscience,Computer Vision and Pattern Recognition,Mathematics (miscellaneous)
Link
https://link.springer.com/content/pdf/10.1007/s12065-023-00882-8.pdf
Reference241 articles.
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2. Jin Y (2011) Surrogate-assisted evolutionary computation: recent advances and future challenges. Swarm Evol Comput 1(2):61–70. https://doi.org/10.1016/j.swevo.2011.05.001
3. Shi L, Rasheed K (2010) Computational intelligence in expensive optimization problems, adaptation learning and optimization, vol 2, Springer, Berlin, Heidelberg, chap A survey of fitness approximation methods applied in evolutionary algorithms, pp 3–28. https://doi.org/10.1007/978-3-642-10701-6_1
4. Giannakoglou K (2002) Design of optimal aerodynamic shapes using stochastic optimization methods and computational intelligence. Progress Aerosp Sci 38(1):43–76. https://doi.org/10.1016/S0376-0421(01)00019-7
5. Stork J, Eiben AE, Bartz-Beielstein T (2020) A new taxonomy of global optimization algorithms. Nat Comput. https://doi.org/10.1007/s11047-020-09820-4
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