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of 54
pro vyhledávání: '"Ma Zeyuan"'
Recent research in Meta-Black-Box Optimization (MetaBBO) have shown that meta-trained neural networks can effectively guide the design of black-box optimizers, significantly reducing the need for expert tuning and delivering robust performance across
Externí odkaz:
http://arxiv.org/abs/2408.10672
Publikováno v:
Redai dili, Vol 41, Iss 2, Pp 423-430 (2021)
Color is a vital characteristic of soil. It is also an important index to reveal the sedimentary environment and climate change of soil in a certain period. A quantitative description of color by the CIELAB color system can effectively avoid the sign
Externí odkaz:
https://doaj.org/article/f64797b46d9e43038c4a3fddbf2bc342
Solving multimodal optimization problems (MMOP) requires finding all optimal solutions, which is challenging in limited function evaluations. Although existing works strike the balance of exploration and exploitation through hand-crafted adaptive str
Externí odkaz:
http://arxiv.org/abs/2404.08242
Evolutionary computation (EC) algorithms, renowned as powerful black-box optimizers, leverage a group of individuals to cooperatively search for the optimum. The exploration-exploitation tradeoff (EET) plays a crucial role in EC, which, however, has
Externí odkaz:
http://arxiv.org/abs/2404.08239
Autor:
Guo, Hongshu, Ma, Yining, Ma, Zeyuan, Chen, Jiacheng, Zhang, Xinglin, Cao, Zhiguang, Zhang, Jun, Gong, Yue-Jiao
Evolutionary algorithms, such as Differential Evolution, excel in solving real-parameter optimization challenges. However, the effectiveness of a single algorithm varies across different problem instances, necessitating considerable efforts in algori
Externí odkaz:
http://arxiv.org/abs/2403.02131
Autor:
Ma, Zeyuan, Guo, Hongshu, Chen, Jiacheng, Peng, Guojun, Cao, Zhiguang, Ma, Yining, Gong, Yue-Jiao
Recent research explores optimization using large language models (LLMs) by either iteratively seeking next-step solutions from LLMs or directly prompting LLMs for an optimizer. However, these approaches exhibit inherent limitations, including low op
Externí odkaz:
http://arxiv.org/abs/2403.01131
Recent Meta-learning for Black-Box Optimization (MetaBBO) methods harness neural networks to meta-learn configurations of traditional black-box optimizers. Despite their success, they are inevitably restricted by the limitations of predefined hand-cr
Externí odkaz:
http://arxiv.org/abs/2402.02355
Autor:
Ma, Zeyuan, Guo, Hongshu, Chen, Jiacheng, Li, Zhenrui, Peng, Guojun, Gong, Yue-Jiao, Ma, Yining, Cao, Zhiguang
Recently, Meta-Black-Box Optimization with Reinforcement Learning (MetaBBO-RL) has showcased the power of leveraging RL at the meta-level to mitigate manual fine-tuning of low-level black-box optimizers. However, this field is hindered by the lack of
Externí odkaz:
http://arxiv.org/abs/2310.08252
Publikováno v:
In Neurocomputing 1 January 2025 611
Autor:
Li, Yanmei, Zhou, Longjian, Xia, Qiuyu, Nie, Yingying, Ma, Zeyuan, Liu, Yayue, Yang, Zhiyou, Hong, Pengzhi, Zhang, Yi
Publikováno v:
In Journal of Functional Foods November 2024 122