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The integration of Large Language Models (LLMs) with evolutionary computation (EC) has introduced a promising paradigm for automating the design of metaheuristic algorithms.
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Chang, Y., Wang, X., Wang, J., Wu, Y., Yang, L., Zhu, K., Chen, H., Yi, X., Wang, C., Wang, Y., et al.: A survey on evaluation of large language models. ACM Transactions on Intelligent Systems and Technology 15
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Lange, R., Tian, Y., Tang, Y.: Large language models as evolution strategies. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion. pp. 579–582 (2024)
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Liu, F., Xialiang, T., Yuan, M., Lin, X., Luo, F., Wang, Z., Lu, Z., Zhang, Q.: Evolution of heuristics: Towards efficient automatic algorithm design using large language model. In: Forty-first International Conference on Machine Learning (2024)
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Zhou, Y., Muresanu, A.I., Han, Z., Paster, K., Pitis, S., Chan, H., Ba, J.: Large language models are human-level prompt engineers. In: NeurIPS 2022 Foundation Models for Decision Making Workshop (2022), https://openreview.net/forum?id=YdqwNaCLCx
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van Stein, N., Bäck, T.: Llamea: A large language model evolutionary algorithm for automatically generating metaheuristics. IEEE Transactions on Evolutionary Computation pp. 1–1 (2024). https://doi.org/10.1109/TEVC.2024.3497793
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