21 citations · 84 across the 28 of their papers we have counts for
31 papers
Beyond Average Performance: Dynamic Instance Clustering and Specialized Algorithm Design in LLM-Assisted Evolutionary Search
Qinglong Hu, Qingfu Zhang, Fei Liu +3
Large Language Model-assisted Evolutionary Search (LES) has emerged as a powerful paradigm for automated algorithm design. However, existing LES methods primarily optimize for aver…
A Sliding-Window-Based Reinforcement Learning for Dynamic Assembly Flow Shop Scheduling with Multi-Product Delivery
Junhao Qiu, Jianjun Liu, Ting Liu +3
Multi-product kitting delivery imposes significant challenges for real-time scheduling in hybrid manufacturing systems that integrate processing and assembly, as dynamic order arri…
LLM-Enabled Automated Algorithm Design for Multiuser Fluid Antenna Communications
Gan Zheng, Fei Liu, Qingfu Zhang
Fluid antenna is a new reconfigurable antenna technology that can dynamically adjust the positions or ports of radiating elements and therefore provides a new degree of freedom for…
Survey on Neural Routing Solvers
Yunpeng Ba, Xi Lin, Changliang Zhou +7
Neural routing solvers (NRSs) that leverage deep learning to tackle vehicle routing problems have demonstrated notable potential for practical applications. By learning implicit he…
Enhancing CVRP Solver through LLM-driven Automatic Heuristic Design
Zhuoliang Xie, Fei Liu, Zhenkun Wang +1
The Capacitated Vehicle Routing Problem (CVRP), a fundamental combinatorial optimization challenge, focuses on optimizing fleet operations under vehicle capacity constraints. While…
EvoDR: Evolving Dispatching Rules via Large Language Model for Dynamic Flexible Assembly Flow Shop Scheduling
Junhao Qiu, Haoyang Zhuang, Fei Liu +2
Dynamic flexible assembly flow shop scheduling with multi-product delivery is a critical combinatorial problem, characterized by kitting supply and machine flexibility. Genetic pro…
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…
LLM4AD: A Platform for Algorithm Design with Large Language Model
Fei Liu, Rui Zhang, Zhuoliang Xie +10
We introduce LLM4AD, a unified Python platform for algorithm design (AD) with large language models (LLMs). LLM4AD is a generic framework with modularized blocks for search methods…
AutoTurb: Using Large Language Models for Automatic Algebraic Model Discovery of Turbulence Closure
Yu Zhang, Kefeng Zheng, Fei Liu +2
Symbolic regression (SR) methods have been extensively investigated to explore explicit algebraic Reynolds stress models (EARSM) for turbulence closure of Reynolds-averaged Navier-…
A Systematic Survey on Large Language Models for Algorithm Design
Fei Liu, Yiming Yao, Ping Guo +9
Algorithm design is crucial for effective problem-solving across various domains. The advent of Large Language Models (LLMs) has notably enhanced the automation and innovation with…
Multi-objective Evolution of Heuristic Using Large Language Model
Shunyu Yao, Fei Liu, Xi Lin +3
Heuristics are commonly used to tackle various search and optimization problems. Design heuristics usually require tedious manual crafting with domain knowledge. Recent works have…
Understanding the Importance of Evolutionary Search in Automated Heuristic Design with Large Language Models
Rui Zhang, Fei Liu, Xi Lin +3
Automated heuristic design (AHD) has gained considerable attention for its potential to automate the development of effective heuristics. The recent advent of large language models…
Prompt Learning for Generalized Vehicle Routing
Fei Liu, Xi Lin, Weiduo Liao +4
Neural combinatorial optimization (NCO) is a promising learning-based approach to solving various vehicle routing problems without much manual algorithm design. However, the curren…