1. Burke, E.K., et al.: Hyper-heuristics: a survey of the state of the art. J. Oper. Res. Soc.64, 1695–1724 (2013)

   Article  Google Scholar 

2. Stützle, T., López-Ibáñez, M.: Automated design of metaheuristic algorithms. In: Handbook of Metaheuristics, pp. 541–579 (2019)

   Google Scholar 

3. Wu, X., Consoli, P., Minku, L., Ochoa, G., Yao, X.: An evolutionary hyper-heuristic for the software project scheduling problem. In: International Conference on Parallel Problem Solving from Nature (2016)

   Google Scholar 

4. Chen, T., et al.: Learning to optimize: a primer and a benchmark. J. Mach. Learn. Res.23(189), 1–59 (2022)

   MathSciNet  Google Scholar 

5. Cowling, P., Kendall, G., Soubeiga, E.: A hyperheuristic approach to scheduling a sales summit. In: Practice and Theory of Automated Timetabling (2001)

   Google Scholar 

6. Mockus, J.: Application of bayesian approach to numerical methods of global and stochastic optimization. J. Global Optim.4, 347–365 (1994)

   Article MathSciNet  Google Scholar 

7. Koza, J.R.: Genetic programming as a means for programming computers by natural selection. Stat. Comput.4, 87–112 (1994)

   Article  Google Scholar 

8. Langdon, W.B., Poli, R.: Foundations of Genetic Programming. Springer, Heidelberg (2013).https://doi.org/10.1007/978-3-662-04726-2

   Book  Google Scholar 

9. Zhang, F., Mei, Y., Nguyen, S., Zhang, M.: Survey on genetic programming and machine learning techniques for heuristic design in job shop scheduling. IEEE Trans. Evol. Comput.28(1), 147–167 (2024)

   Article  Google Scholar 

10. Zhang, F., Mei, Y., Nguyen, S., Zhang, M.: Importance-aware genetic programming for automated scheduling heuristics learning in dynamic flexible job shop scheduling. In: International Conference on Parallel Problem Solving from Nature (2022)

    Google Scholar 

11. O’Neill, M., Vanneschi, L., Gustafson, S., Banzhaf, W.: Open issues in genetic programming. Genet. Program. Evol. Mach.11(3), 339–363 (2010)

    Article  Google Scholar 

12. Romera-Paredes, B., et al.: Mathematical discoveries from program search with large language models. Nature625(7995), 468–475 (2024)

    Article  Google Scholar 

13. Tao, T., Vu, V.H.: Additive Combinatorics. Cambridge University Press, Cambridge (2006)

    Book  Google Scholar 

14. Liu, F., et al.: Evolution of heuristics: towards efficient automatic algorithm design using large language model. In: International Conference on Machine Learning (2024)

    Google Scholar 

15. Ye, H., Wang, J., Cao, Z., Song, G.: Reevo: large language models as hyper-heuristics with reflective evolution. arXiv preprintarXiv:2402.01145 (2024)

16. Matai, R., Singh, S.P., Mittal, M.L.: Traveling salesman problem: an overview of applications, formulations, and solution approaches. Travel. Salesman Prob. Theory Appl.1(1), 1–25 (2010)

    Google Scholar 

17. Seiden, S.S.: On the online bin packing problem. J. ACM49(5), 640–671 (2002)

    Article MathSciNet  Google Scholar 

18. Hansen, N.: The CMA evolution strategy: a tutorial. arXiv preprintarXiv:1604.00772 (2016)

19. Brown, T., et al.: Language models are few-shot learners. Adv. Neural Inf. Process. Syst. (2020)

    Google Scholar 

20. Burke, E.K., Hyde, M., Kendall, G., Ochoa, G., Özcan, E., Woodward, J.R.: A Classification of Hyper-heuristic Approaches. In: Gendreau, M., Potvin, J.Y. (eds.) Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol. 146, pp. 449–468. Springer, Boston (2010)

    Google Scholar 

21. He, X., Zhao, K., Chu, X.: Automl: a survey of the state-of-the-art. Knowl.-Based Syst.212, 106622 (2021)

    Article  Google Scholar 

22. Burke, E.K., Petrovic, S., Qu, R.: Case-based heuristic selection for timetabling problems. J. Sched.9, 115–132 (2006)

    Article  Google Scholar 

23. Ross, H.-L. F.P., Corne, D.: A promising hybrid GA/heuristic approach for open-shop scheduling problems. In: European Conference on Artificial Intelligence (1994)

    Google Scholar 

24. Hart, E., Ross, P., Nelson, J.: Solving a real-world problem using an evolving heuristically driven schedule builder. Evol. Comput.6(1), 61–80 (1998)

    Article  Google Scholar 

25. Terashima-Marín, H., Flores-Alvarez, E., Ross, P.: Hyper-heuristics and classifier systems for solving 2d-regular cutting stock problems. In: Annual Conference on Genetic and Evolutionary Computation (2005)

    Google Scholar 

26. Rodríguez, J.V., Petrovic, S., Salhi, A.: A combined meta-heuristic with hyper-heuristic approach to the scheduling of the hybrid flow shop with sequence dependent setup times and uniform machines. In: Multidisciplinary International Conference on Scheduling: Theory and Applications. MISTA: Paris, France (2007)

    Google Scholar 

27. Burke, E.K., Hyde, M.R., Kendall, G.: Evolving bin packing heuristics with genetic programming. In: International Conference on Parallel Problem Solving from Nature (2006)

    Google Scholar 

28. Duflo, G., Kieffer, E., Brust, M.R., Danoy, G., Bouvry, P.: A GP hyper-heuristic approach for generating tsp heuristics. In: 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (2019)

    Google Scholar 

29. Rego, C., Gamboa, D., Glover, F., Osterman, C.: Traveling salesman problem heuristics: leading methods, implementations and latest advances. Eur. J. Oper. Res.211(3), 427–441 (2011)

    Article MathSciNet  Google Scholar 

30. Drechsler, R., Becker, B.: Learning heuristics by genetic algorithms. In: ASP-DAC’95/CHDL’95/VLSI’95 with EDA Technofair (1995)

    Google Scholar 

31. Branke, J., Nguyen, S., Pickardt, C.W., Zhang, M.: Automated design of production scheduling heuristics: a review. IEEE Trans. Evol. Comput.20(1), 110–124 (2015)

    Article  Google Scholar 

32. Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. (2017)

    Google Scholar 

33. Achiam, J., et al.: Gpt-4 technical report. arXiv preprintarXiv:2303.08774 (2023)

34. Zhao, W.X., et al.: A survey of large language models. arXiv preprintarXiv:2303.18223 (2023)

35. Tian, H., et al.: chatgpt the ultimate programming assistant–how far is it?. arXiv preprintarXiv:2304.11938 (2023)

36. Yu, C., Liu, X., Tang, C., Feng, W., Lv, J.: GPT-NAS: neural architecture search with the generative pre-trained model. arXiv preprintarXiv:2305.05351 (2023)

37. Zhang, S., Gong, C., Wu, L., Liu, X., Zhou, M.: Automl-GPT: automatic machine learning with gpt. arXiv preprintarXiv:2305.02499 (2023)

38. Zhou, Y., et al.: Large language models are human-level prompt engineers. arXiv preprintarXiv:2211.01910 (2022)

39. Wang, X., et al.: Promptagent: strategic planning with language models enables expert-level prompt optimization. arXiv preprintarXiv:2310.16427 (2023)

40. Zelikman, E., Lorch, E., Mackey, L., Kalai, A.T.: Self-taught optimizer (stop): recursively self-improving code generation. arXiv preprintarXiv:2310.02304 (2023)

41. Liu, S., Chen, C., Qu, X., Tang, K., Ong, Y.-S.: Large language models as evolutionary optimizers. arXiv preprintarXiv:2310.19046 (2023)

42. Liu, F., et al.: Large language model for multi-objective evolutionary optimization. arXiv preprintarXiv:2310.12541 (2023)

43. Chen, A., Dohan, D., So, D.: EvoPrompting: language models for code-level neural architecture search. Adv. Neural Inf. Process. Syst. (2024)

    Google Scholar 

44. Meyerson, E., et al.: Language model crossover: variation through few-shot prompting. arXiv preprintarXiv:2302.12170 (2023)

45. Hemberg, E., Moskal, S., O’Reilly, U.-M.: Evolving code with a large language model. arXiv preprintarXiv:2401.07102 (2024)

46. Yang, C., et al.: Large language models as optimizers. arXiv preprintarXiv:2309.03409 (2023)

47. Guo, Q., et al.: Connecting large language models with evolutionary algorithms yields powerful prompt optimizers. arXiv preprintarXiv:2309.08532 (2023)

48. Lehman, J., Gordon, J., Jain, S., Ndousse, K., Yeh, C., Stanley, K.O.: Evolution through large models (2022)

    Google Scholar 

49. Wu, X., Wu, S.-H., Wu, J., Feng, L., Tan, K.C.: Evolutionary computation in the era of large language model: survey and roadmap. arXiv preprintarXiv:2401.10034 (2024)

50. Code models overview (2023)

    Google Scholar 

51. Li, R., et al.: Starcoder: may the source be with you!. arXiv preprintarXiv:2305.06161 (2023)

52. Wei, J., et al.: Chain-of-thought prompting elicits reasoning in large language models. Adv. Neural Inf. Process. Syst. (2022)

    Google Scholar 

53. Guo, D., et al.: Deepseek-coder: when the large language model meets programming–the rise of code intelligence. arXiv preprintarXiv:2401.14196 (2024)

54. Roziere, B., et al.: Code llama: open foundation models for code. arXiv preprintarXiv:2308.12950 (2023)

55. Holland, J.H.: Genetic algorithms. Sci. Am.267(1), 66–73 (1992)

    Article  Google Scholar 

56. Shinn, N., Cassano, F., Gopinath, A., Narasimhan, K., Yao, S.: Reflexion: language agents with verbal reinforcement learning. Adv. Neural Inf. Process. Syst. (2024)

    Google Scholar 

57. Grochow, J.: New applications of the polynomial method: the cap set conjecture and beyond. Bull. Am. Math. Soc.56(1), 29–64 (2019)

    Article MathSciNet  Google Scholar 

58. Beasley, J.E.: Or-library: distributing test problems by electronic mail. J. Oper. Res. Soc.41(11), 1069–1072 (1990)

    Article  Google Scholar 

59. Castiñeiras, I., De Cauwer, M., O’Sullivan, B.: Weibull-based benchmarks for bin packing. In: International Conference on Principles and Practice of Constraint Programming (2012)

    Google Scholar 

60. Liu, F., et al.: An example of evolutionary computation+ large language model beating human: design of efficient guided local search. arXiv preprintarXiv:2401.02051 (2024)

61. Kool, W., Van Hoof, H., Welling, M.: Attention, learn to solve routing problems!. arXiv preprintarXiv:1803.08475 (2018)

62. Chen, M., et al.: Evaluating large language models trained on code. arXiv preprintarXiv:2107.03374 (2021)

63. Hendrycks, D., et al.: Measuring massive multitask language understanding. arXiv preprintarXiv:2009.03300 (2020)

64. Guo, D., Lu, S., Duan, N., Wang, Y., Zhou, M., Yin, J.: UniXcoder: unified cross-modal pre-training for code representation. In: Annual Meeting of the Association for Computational Linguistics (2022)

    Google Scholar 

65. Anthropic. The claude 3 model family: Opus, sonnet, haiku (2024)

    Google Scholar 

66. Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Conference of the North American Chapter of the Association for Computational Linguistics (2019)

    Google Scholar 

67. Ma, Y.J., et al.: Eureka: human-level reward design via coding large language models. In: International Conference on Learning Representations (2024)

    Google Scholar 

Download references