most citedEvolutionary Generative Optimization: Towards Fully Data-Driven Evolutionary Optimization via Generative Learning

3 citations · 3 across the 8 of their papers we have counts for

collaborators

9 papers

cs.AI2026

SLAT: Segment-Level Adaptive Trimming for Efficient CoT Reasoning

Jian Yao, Xiongcai Luo, Ran Cheng +1

Recent advances in Large Reasoning Models have significantly improved chain-of-thought (CoT) capabilities via reinforcement learning (RL). However, generated reasoning chains frequ…

cs.LG2026

Towards Adaptive Continual Model Merging via Manifold-Aware Expert Evolution

Haiyun Qiu, Xingyu Wu, Kay Chen Tan

Continual Model Merging (CMM) sequentially integrates task-specific models into a unified architecture without intensive retraining. However, existing CMM methods are hindered by a…

cs.NE2025

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…

cs.NE2025

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.…

cs.NE2025★ 3 cited

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…

cs.AI2025

VAR-MATH: Probing True Mathematical Reasoning in LLMS via Symbolic Multi-Instance Benchmarks

Jian Yao, Ran Cheng, Kay Chen Tan

Recent advances in reinforcement learning (RL) have led to substantial improvements in the mathematical reasoning abilities of LLMs, as measured by standard benchmarks. Yet these g…

cs.DC2025

EvoGit: Decentralized Code Evolution via Git-Based Multi-Agent Collaboration

Beichen Huang, Ran Cheng, Kay Chen Tan

We introduce EvoGit, a decentralized multi-agent framework for collaborative software development driven by autonomous code evolution. EvoGit deploys a population of independent co…

cs.NE2025

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…

cs.NE2025

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…