no code implementations • 12 Apr 2024 • Hongqiao Lian, Zeyuan Ma, Hongshu Guo, Ting Huang, Yue-Jiao Gong
In this paper, we propose RLEMMO, a Meta-Black-Box Optimization framework, which maintains a population of solutions and incorporates a reinforcement learning agent for flexibly adjusting individual-level searching strategies to match the up-to-date optimization status, hence boosting the search performance on MMOP.
no code implementations • 12 Apr 2024 • Zeyuan Ma, Jiacheng Chen, Hongshu Guo, Yining Ma, Yue-Jiao Gong
Evolutionary computation (EC) algorithms, renowned as powerful black-box optimizers, leverage a group of individuals to cooperatively search for the optimum.
no code implementations • 4 Mar 2024 • Hongshu Guo, Yining Ma, Zeyuan Ma, Jiacheng Chen, Xinglin Zhang, Zhiguang Cao, Jun Zhang, Yue-Jiao Gong
As a proof-of-principle study, we apply this framework to a group of Differential Evolution algorithms.
no code implementations • 2 Mar 2024 • Zeyuan Ma, Hongshu Guo, Jiacheng Chen, Guojun Peng, Zhiguang Cao, Yining Ma, Yue-Jiao Gong
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.
1 code implementation • 4 Feb 2024 • Jiacheng Chen, Zeyuan Ma, Hongshu Guo, Yining Ma, Jie Zhang, Yue-Jiao Gong
Recent Meta-learning for Black-Box Optimization (MetaBBO) methods harness neural networks to meta-learn configurations of traditional black-box optimizers.
1 code implementation • NeurIPS 2023 • Zeyuan Ma, Hongshu Guo, Jiacheng Chen, Zhenrui Li, Guojun Peng, Yue-Jiao Gong, Yining Ma, Zhiguang Cao
To fill this gap, we introduce MetaBox, the first benchmark platform expressly tailored for developing and evaluating MetaBBO-RL methods.
1 code implementation • 14 Mar 2023 • Moritz Neun, Christian Eichenberger, Henry Martin, Markus Spanring, Rahul Siripurapu, Daniel Springer, Leyan Deng, Chenwang Wu, Defu Lian, Min Zhou, Martin Lumiste, Andrei Ilie, Xinhua Wu, Cheng Lyu, Qing-Long Lu, Vishal Mahajan, Yichao Lu, Jiezhang Li, Junjun Li, Yue-Jiao Gong, Florian Grötschla, Joël Mathys, Ye Wei, He Haitao, Hui Fang, Kevin Malm, Fei Tang, Michael Kopp, David Kreil, Sepp Hochreiter
We only provide vehicle count data from spatially sparse stationary vehicle detectors in these three cities as model input for this task.
2 code implementations • 18 Nov 2022 • Jiezhang Li, Junjun Li, Yue-Jiao Gong
For this reason, we propose a multi-task learning network that can simultaneously predict the congestion classes and the speed of each road segment.
1 code implementation • IEEE Transactions on Cybernetics 2017 • Yong-Feng Ge, Wei-Jie Yu, Ying Lin, Yue-Jiao Gong, Zhi-Hui Zhan, Wei-neng Chen, Jun Zhang et al.
In this way, the number of subpopulations is adaptively adjusted and better performing subpopulations obtain more individuals.
no code implementations • 4 Aug 2014 • Yue-Jiao Gong, Jun Zhang
This mechanism helps to instantly respond to the current traffic condition of the roundabout so as to improve real-timeness.