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Evolutionary algorithms (EAs) have achieved remarkable success in tackling complex combinatorial optimization problems.
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2019
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2020
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K. Zhao, S. Liu, J. X. Yu, and Y. Rong, “Towards feature-free tsp solver selection: A deep learning approach,” in Proceedings of the 2021 International Joint Conference on Neural Networks, IJCNN’2021 , Virtual Event, Jul 2021, pp. 1–8
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Y. Bengio, A. Lodi, and A. Prouvost, “Machine learning for combinatorial optimization: a methodological tour d’horizon,” European Journal of Operational Research , vol. 290, no. 2, pp. 405–421, 2021
2021
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K. Tang, S. Liu, P. Yang, and X. Yao, “Few-shots parallel algorithm portfolio construction via co-evolution,” IEEE Transactions on Evolutionary Computation , vol. 25, no. 3, pp. 595–607, 2021
2021
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C. Wang, H. Ma, G. Chen, and S. Hartmann, “Memetic eda-based approaches to qos-aware fully automated semantic web service composition,” IEEE Transactions on Evolutionary Computation , vol. 26, no. 3, pp. 570–584, 2022
2022
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Z. Pan, D. Lei, and L. Wang, “A knowledge-based two-population optimization algorithm for distributed energy-efficient parallel machines scheduling,” IEEE transactions on cybernetics , vol. 52, no. 6, pp. 5051–5063, 2022
2022
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2023
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D. Zhou, N. Schärli, L. Hou, J. Wei, N. Scales, X. Wang, D. Schuurmans, C. Cui, O. Bousquet, Q. V. Le, and E. H. Chi, “Least-to-most prompting enables complex reasoning in large language models,” in Proceedings of the 11th International Conference on Learning Representations, ICLR’2023 , 2023
2023
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S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. R. Narasimhan, and Y. Cao, “React: Synergizing reasoning and acting in language models,” in Proceedings of the 11th International Conference on Learning Representations, ICLR’2023 , 2023
2023
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2023
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2023
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2023
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2023
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2023
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A. Chen, D. M. Dohan, and D. R. So, “Evoprompting: Language models for code-level neural architecture search,” arXiv preprint arXiv: 2302.14838 , 2023
2023
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M. Pluhacek, A. Kazikova, T. Kadavy, A. Viktorin, and R. Senkerik, “Leveraging large language models for the generation of novel metaheuristic optimization algorithms,” in Companion Proceedings of the 2023 Conference on Genetic and Evolutionary Computation, GECCO’2023 , S. Silva and L. Paquete, Eds., 2023, pp. 1812–1820
2023
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Q. Guo, R. Wang, J. Guo, B. Li, K. Song, X. Tan, G. Liu, J. Bian, and Y. Yang, “Connecting large language models with evolutionary algorithms yields powerful prompt optimizers,” arXiv preprint arXiv: 2309.08532 , 2023
2023
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C. Yang, X. Wang, Y. Lu, H. Liu, Q. V. Le, D. Zhou, and X. Chen, “Large language models as optimizers,” arXiv preprint arXiv: 2309.03409 , 2023
2023
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F. Liu, X. Lin, Z. Wang, S. Yao, X. Tong, M. Yuan, and Q. Zhang, “Large language model for multi-objective evolutionary optimization,” arXiv preprint arXiv: 2310.12541 , 2023
2023
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2023
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P. Yang, L. Zhang, H. Liu, and G. Li, “Reducing idleness in financial cloud services via multi-objective evolutionary reinforcement learning based load balancer,” Science China Information Sciences , vol. 67, no. 2, p. 120102, 2024
2024
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S. Liu, N. Lu, W. Hong, C. Qian, and K. Tang, “Effective and imperceptible adversarial textual attack via multi-objectivization,” ACM Transactions on Evolutionay Learnning and Optimization , 2024, just Accepted
2024
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K. Tang and X. Yao, “Learn to optimize - A brief overview,” National Science Review , p. nwae132, 04 2024
2024
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