LLaMEA: A Large Language Model Evolutionary Algorithm for Automatically Generating Metaheuristics

M. A. Jaro, “Advances in record-linkage methodology as applied to matching the 1985 census of Tampa, Florida,” Journal of the American Statistical Association , vol. 84, no. 406, pp. 414–420, 1989. [Online]. Available: https://www.tandfonline.com/doi/abs/10.1080/01621459.1989.10478785

1989

Earlier work this paper cites.

T. Bäck, D. B. Fogel, and Z. Michalewicz, “Handbook of evolutionary computation,” Release , vol. 97, no. 1, p. B1, 1997

1997

Earlier work this paper cites.

J. Kennedy and R. C. Eberhart, Swarm Intelligence . Morgan Kaufmann, 2001

2001

Earlier work this paper cites.

M. Dorigo and T. Stützle, Ant Colony Optimization . MIT Press, 2004

2004

Earlier work this paper cites.

N. Hansen, S. Finck, R. Ros, and A. Auger, “Real-parameter black-box optimization benchmarking 2009: Noiseless functions definitions,” INRIA, Tech. Rep. RR6829, 2009

2009

Earlier work this paper cites.

J. Zhang and A. C. Sanderson, “JADE: Adaptive differential evolution with optional external archive,” IEEE Transactions on Evolutionary Computation , vol. 13, no. 5, pp. 945–958, 2009

2009

Earlier work this paper cites.

N. Hansen, S. Finck, R. Ros, and A. Auger, “Real-Parameter Black-Box Optimization Benchmarking 2009: Noiseless Functions Definitions,” INRIA, Research Report RR-6829, 2009. [Online]. Available: https://hal.inria.fr/inria-00362633

2009

Earlier work this paper cites.

N. Hansen and R. Ros, “Black-box optimization benchmarking of NEWUOA compared to BIPOP-CMA-ES: on the BBOB noiseless testbed,” in Proceedings of the 12th annual conference companion on Genetic and evolutionary computation , 2010, pp. 1519–1526

2010

Earlier work this paper cites.

H. Iba and N. Noman, Real-world Applications of Evolutionary Algorithms . Imperial College Press, UK, 2011, pp. 211–262. [Online]. Available: https://www.worldscientific.com/doi/abs/10.1142/9781848166820_0006

2011

Earlier work this paper cites.

R. Chiong, T. Weise, and Z. Michalewicz, Variants of evolutionary algorithms for real-world applications . Springer, 2012, vol. 2

2012

Earlier work this paper cites.

D. Brockhoff, A. Auger, and N. Hansen, “Comparing mirrored mutations and active covariance matrix adaptation in the IPOP-CMA-ES on the noiseless BBOB testbed,” in Proceedings of the 14th annual conference companion on Genetic and evolutionary computation , 2012, pp. 297–304

2012

Earlier work this paper cites.

P. Pošík and V. Klemš, “Benchmarking the differential evolution with adaptive encoding on noiseless functions,” in Proceedings of the 14th annual conference companion on Genetic and evolutionary computation , 2012, pp. 189–196

2012

Earlier work this paper cites.

A. E. Eiben and J. E. Smith, Introduction to evolutionary computing . Springer, 2015

2015

Earlier work this paper cites.

S. van Rijn, H. Wang, M. van Leeuwen, and T. Bäck, “Evolving the structure of evolution strategies,” in 2016 IEEE Symposium Series on Computational Intelligence (SSCI) , 2016, pp. 1–8

2016

Earlier work this paper cites.

C. Doerr, H. Wang, F. Ye, S. van Rijn, and T. Bäck, “IOHprofiler: A benchmarking and profiling tool for iterative optimization heuristics,” CoRR , vol. abs/1810.05281, 2018. [Online]. Available: http://arxiv.org/abs/1810.05281

Original

2018

Earlier work this paper cites.

R. Cheng, C. He, Y. Jin, and X. Yao, “Model-based evolutionary algorithms: a short survey,” Complex & Intelligent Systems , vol. 4, no. 4, pp. 283–292, 2018

2018

Earlier work this paper cites.

M. Lindauer, K. Eggensperger, M. Feurer, A. Biedenkapp, D. Deng, C. Benjamins, T. Ruhkopf, R. Sass, and F. Hutter, “SMAC3: A versatile bayesian optimization package for hyperparameter optimization,” J. Mach. Learn. Res. , vol. 23, pp. 54:1–54:9, 2022. [Online]. Available: https://jmlr.org/papers/v23/21-0888.html

2018

Earlier work this paper cites.

C. Doerr, F. Ye, N. Horesh, H. Wang, O. M. Shir, and T. Bäck, “Benchmarking discrete optimization heuristics with IOHprofiler,” Appl. Soft Comput. , vol. 88, p. 106027, 2020. [Online]. Available: https://doi.org/10.1016/j.asoc.2019.106027

2019

Earlier work this paper cites.

C. L. Camacho-Villalón, M. Dorigo, and T. Stützle, “The intelligent water drops algorithm: why it cannot be considered a novel algorithm - A brief discussion on the use of metaphors in optimization,” Swarm Intell. , vol. 13, no. 3-4, pp. 173–192, 2019. [Online]. Available: https://doi.org/10.1007/s11721-019-00165-y

2019

Earlier work this paper cites.

C. L. Camacho-Villalón, T. Stützle, and M. Dorigo, “Grey wolf, firefly and bat algorithms: Three widespread algorithms that do not contain any novelty,” in Proc. of Swarm Intelligence (ANTS) , ser. LNCS, vol. 12421. Springer, 2020, pp. 121–133. [Online]. Available: https://doi.org/10.1007/978-3-030-60376-2\_10

2020

Earlier work this paper cites.

J. Del Ser, E. Osaba, A. D. Martinez, M. N. Bilbao, J. Poyatos, D. Molina, and F. Herrera, “More is not always better: insights from a massive comparison of meta-heuristic algorithms over real-parameter optimization problems,” in 2021 IEEE Symposium Series on Computational Intelligence (SSCI) . IEEE, 2021, pp. 1–7

2021

Earlier work this paper cites.

J. de Nobel, F. Ye, D. Vermetten, H. Wang, C. Doerr, and T. Bäck, “IOHexperimenter: Benchmarking platform for iterative optimization heuristics,” arXiv e-prints:2111.04077 , nov 2021. [Online]. Available: https://arxiv.org/abs/2111.04077

Original

2021

Cited alongside, same era.

J. de Nobel, D. Vermetten, H. Wang, C. Doerr, and T. Bäck, “Tuning as a means of assessing the benefits of new ideas in interplay with existing algorithmic modules,” in Proc. of Genetic and Evolutionary Computation Conference (GECCO’21, Companion material) . ACM, 2021, pp. 1375–1384. [Online]. Available: https://doi.org/10.1145/3449726.3463167

2021

Cited alongside, same era.

X. Xia, L. Tong, Y. Zhang, X. Xu, H. Yang, L. Gui, Y. Li, and K. Li, “Nfdde: A novelty-hybrid-fitness driving differential evolution algorithm,” Information Sciences , vol. 579, pp. 33–54, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0020025521007726

2021

Cited alongside, same era.

Q. Dong, L. Li, D. Dai, C. Zheng, Z. Wu, B. Chang, X. Sun, J. Xu, L. Li, and Z. Sui, “A survey on in-context learning,” 2023, arXiv:2301.00234

Original

2023

Later among the works it cites.

F. Liu, X. Tong, M. Yuan, and Q. Zhang, “Algorithm evolution using large language model,” 2023, arXiv:2311.15249

Original

2023

Later among the works it cites.

M. Pluhacek, A. Kazikova, T. Kadavy, A. Viktorin, and R. Senkerik, “Leveraging large language models for the generation of novel metaheuristic optimization algorithms,” in Proceedings of the Companion Conference on Genetic and Evolutionary Computation , ser. GECCO ’23 Companion. New York, NY, USA: Association for Computing Machinery, 2023, p. 1812–1820. [Online]. Available: https://doi.org/10.1145/3583133.3596401

2023

Later among the works it cites.

M. I. E. Khaldi and A. Draa, “Surrogate-assisted evolutionary optimisation: a novel blueprint and a state of the art survey,” Evolutionary Intelligence , pp. 1–31, 2023