Deep Surrogate Network Enhancement of Non-dominated Sorting Genetic Algorithms for Improved Multi-objective Optimization
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-67569-0_30
Reference27 articles.
1. Bakar, A., Li, K., Liu, H., Xu, Z., Alessandrini, M., Wen, D.: Multi-objective optimization of low reynolds number airfoil using convolutional neural network and non-dominated sorting genetic algorithm. Aerospace 9(1), 35 (2022)
2. Yang, Z., et al.: Intelligent optimization strategy for electrochemical removal of ammonia nitrogen by neural network embedded in a non-dominated sorting genetic algorithm. J. Water Process Eng. 56, 104502 (2023)
3. Yuan, Y., Banzhaf, W.: Expensive multiobjective evolutionary optimization assisted by dominance prediction. IEEE Trans. Evol. Comput. 26(1), 159–173 (2021)
4. Espinosa, R., Jiménez, F., Palma, J.: Surrogate-assisted and filter-based multiobjective evolutionary feature selection for deep learning. IEEE Trans. Neural Networks Learn. Syst. (2023)
5. Wu, P., He, Y., Li, Y., He, J., Liu, X., Wang, Y.: Multi-objective optimisation of machining process parameters using deep learning-based data-driven genetic algorithm and TOPSIS. J. Manuf. Syst. 64, 40–52 (2022)
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