2 citations · 2 across the 7 of their papers we have counts for
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A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving
Yisong Zhang, Ran Cheng, Guoxing Yi +1
Large language models (LLMs) are increasingly integrated with evolutionary computation to support optimization tasks. This survey primarily focuses on evolutionary optimization, i.…
Beyond Speedups: Hardware-Aware Evaluation of Evolutionary Algorithms on GPUs
Xinmeng Yu, Tao Jiang, Ran Cheng +2
Evolutionary algorithms (EAs) are increasingly executed on graphics processing units (GPUs) to exploit population-level parallelism. This shift changes the resource model under whi…
Learning to Evolve for Optimization via Stability-Inducing Neural Unrolling
Jiaxin Gao, Yaohua Liu, Ran Cheng +1
Evolutionary algorithms serve as a powerful paradigm for tackling optimization challenges, yet their reliance on manually engineered heuristics inherently limits their adaptability…
Evolutionary Generative Optimization: Towards Fully Data-Driven Evolutionary Optimization via Generative Learning
Tao Jiang, Kebin Sun, Zhenyu Liang +3
Recent advances in data-driven evolutionary algorithms (EAs) have demonstrated the potential of leveraging historical data to improve optimization accuracy and adaptability. Despit…
EvoRL: A GPU-accelerated Framework for Evolutionary Reinforcement Learning
Bowen Zheng, Ran Cheng, Kay Chen Tan
Evolutionary Reinforcement Learning (EvoRL) has emerged as a promising approach to overcoming the limitations of traditional reinforcement learning (RL) by integrating the Evolutio…
ParetoLens: A Visual Analytics Framework for Exploring Solution Sets of Multi-objective Evolutionary Algorithms
Yuxin Ma, Zherui Zhang, Ran Cheng +2
In the domain of multi-objective optimization, evolutionary algorithms are distinguished by their capability to generate a diverse population of solutions that navigate the trade-o…