An investigation on the use of Large Language Models for hyperparameter tuning in Evolutionary Algorithms

Author:

Custode Leonardo Lucio1ORCID,Caraffini Fabio2ORCID,Yaman Anil3ORCID,Iacca Giovanni1ORCID

Affiliation:

1. University of Trento, Trento, Italy

2. Swansea University, Swansea, United Kingdom

3. Vrije Universiteit Amsterdam, Amsterdam, Netherlands

Funder

European Union

Publisher

ACM

Reference49 articles.

1. Benchmarking the (1+1) evolution strategy with one-fifth success rule on the BBOB-2009 function testbed

2. Beibin Li and Konstantina Mellou and Bo Zhang and Jeevan Pathuri and Ishai Menache. 2023. Large Language Models for Supply Chain Optimization. arXiv:2307.03875.

3. Boiko, Daniil A and MacKnight, Robert and Kline, Ben and Gomes, Gabe. 2023. Autonomous chemical research with large language models. Nature 624, 7992 (2023), 570--578.

4. Nuo Chen Ning Wu Shining Liang Ming Gong Linjun Shou Dongmei Zhang and Jia Li. 2024. Is Bigger and Deeper Always Better? Probing LLaMA Across Scales and Layers. arXiv:2312.04333.

5. Chengrun Yang and Xuezhi Wang and Yifeng Lu and Hanxiao Liu and Quoc V. Le and Denny Zhou and Xinyun Chen. 2023. Large Language Models as Optimizers. arXiv:2309.03409.

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