21 citations · 84 across the 28 of their papers we have counts for
12 papers · 1 filter
URS: A Unified Neural Routing Solver for Cross-Problem Zero-Shot Generalization
Changliang Zhou, Canhong Yu, Shunyu Yao +4
Multi-task neural routing solvers have emerged as a promising paradigm for their ability to solve multiple vehicle routing problems (VRPs) using a single model. However, existing n…
EoH-S: Evolution of Heuristic Set using LLMs for Automated Heuristic Design
Fei Liu, Yilu Liu, Qingfu Zhang +2
Automated Heuristic Design (AHD) using Large Language Models (LLMs) has achieved notable success in recent years. Despite the effectiveness of existing approaches, they only design…
Multimodal LLM-assisted Evolutionary Search for Programmatic Control Policies
Qinglong Hu, Xialiang Tong, Mingxuan Yuan +3
Deep reinforcement learning has achieved impressive success in control tasks. However, its policies, represented as opaque neural networks, are often difficult for humans to unders…
LRM-1B: Towards Large Routing Model
Han Li, Fei Liu, Zhenkun Wang +1
Vehicle routing problems (VRPs) are central to combinatorial optimization with significant practical implications. Recent advancements in neural combinatorial optimization (NCO) ha…
Fine-tuning Large Language Model for Automated Algorithm Design
Fei Liu, Rui Zhang, Xi Lin +2
The integration of large language models (LLMs) into automated algorithm design has shown promising potential. A prevalent approach embeds LLMs within search routines to iterativel…
Learning to Insert for Constructive Neural Vehicle Routing Solver
Fu Luo, Xi Lin, Mengyuan Zhong +4
Neural Combinatorial Optimisation (NCO) is a promising learning-based approach for solving Vehicle Routing Problems (VRPs) without extensive manual design. While existing construct…
TokLIP: Marry Visual Tokens to CLIP for Multimodal Comprehension and Generation
Haokun Lin, Teng Wang, Yixiao Ge +6
Pioneering token-based works such as Chameleon and Emu3 have established a foundation for multimodal unification but face challenges of high training computational overhead and lim…
Fitness Landscape of Large Language Model-Assisted Automated Algorithm Search
Fei Liu, Qingfu Zhang, Jialong Shi +3
Using Large Language Models (LLMs) in an evolutionary or other iterative search framework have demonstrated significant potential in automated algorithm design. However, the underl…
Learning to Reduce Search Space for Generalizable Neural Routing Solver
Changliang Zhou, Xi Lin, Zhenkun Wang +1
Constructive neural combinatorial optimization (NCO) offers a promising paradigm for solving vehicle routing problems (VRPs) by directly learning to construct approximate optimal s…
Destroy and Repair Using Hyper Graphs for Routing
Ke Li, Fei Liu, Zhengkun Wang +1
Recent advancements in Neural Combinatorial Optimization (NCO) have shown promise in solving routing problems like the Traveling Salesman Problem (TSP) and Capacitated Vehicle Rout…
ARS: Automatic Routing Solver with Large Language Models
Kai Li, Fei Liu, Zhenkun Wang +4
Real-world Vehicle Routing Problems (VRPs) are characterized by a variety of practical constraints, making manual solver design both knowledge-intensive and time-consuming. Althoug…
MOS-Attack: A Scalable Multi-objective Adversarial Attack Framework
Ping Guo, Cheng Gong, Xi Lin +4
Crafting adversarial examples is crucial for evaluating and enhancing the robustness of Deep Neural Networks (DNNs), presenting a challenge equivalent to maximizing a non-different…