arXiv:2605. 25246v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used for optimization modeling and solver-code generation, yet practical operations research and optimization problems often require a harder capability: designing scalable algorithms that exploit problem structure and outperform direct formulation-and-solve baselines.
By Minwei Kong, Chonghe Jiang, Ao Qu, Wenbin Ouyang, Zhaoming Zeng, Xiaotong Guo, Zhekai Li, Junyi Li, Yi Fan, Xinshou Zheng, Xi Jing, Yikai Zhang, Zhiwei Liang, Seonghoo Kim, Runqing Yang, Zijian Zhou, Sirui Li, Han Zheng, Wangyang Ying, Ou Zheng, Chonghuan Wang, Jinglong Zhao, Hanzhang Qin, Cathy Wu, Paul Pu Liang, Jinhua Zhao, Hai Wang
arXiv:2608. 08085v1 Announce Type: cross Abstract: Code performance optimization is a vital aspect of modern software development, as it enables faster response times and reduced resource usage.
By Ji\v{r}\'i Klepl, Maty'a\v{s} Brabec, Martin Kruli\v{s}
arXiv:2608. 02612v1 Announce Type: cross Abstract: Formulating an optimization problem strongly affects the quality of the final solution, yet good formulations usually require substantial expertise.
By Yutaro Yamada, Kei Hiroshima, Nozomu Yoshinari, Kento Uchida, Shinichi Shirakawa
arXiv:2509. 08269v5 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly integrated with evolutionary computation to support optimization tasks.
By Yisong Zhang, Ran Cheng, Guoxing Yi, Kay Chen Tan
The paper introduces LLM-EBG, an evolutionary framework that uses a large language model as a generative operator to automatically create optimization benchmarks. By generating unconstrained single-objective continuous minimization problems expressed as mathematical formulas, the framework can produce benchmarks that consistently favor a target algorithm over a comparison algorithm in over 80% of trials. Landscape analysis shows that these generated problems exhibit distinct geometric traits, such as sensitivity to variable scaling, reflecting the search behaviors of different optimization methods.
By Yuhiro Ono, Tomohiro Harada, Yukiya Miura
arXiv:2609.00023v1 Announce Type: cross
Abstract: In this paper, we introduce ES-AHD, a novel framework that fundamentally integrates Evolution Strategy (ES) into Large Language Model (LLM)-driven Au...
By Yutao Lai, Kezhao Lai, Hai-Lin Liu, Yuping Wang, Ping Guo