21 citations · 91 across the 29 of their papers we have counts for
13 papers · 1 filter
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…
CaDA: Cross-Problem Routing Solver with Constraint-Aware Dual-Attention
Han Li, Fei Liu, Zhi Zheng +2
Vehicle Routing Problems (VRPs) are significant Combinatorial Optimization (CO) problems holding substantial practical importance. Recently, Neural Combinatorial Optimization (NCO)…
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…
Few for Many: Tchebycheff Set Scalarization for Many-Objective Optimization
Xi Lin, Yilu Liu, Xiaoyuan Zhang +3
Multi-objective optimization can be found in many real-world applications where some conflicting objectives can not be optimized by a single solution. Existing optimization methods…
Evolve Cost-aware Acquisition Functions Using Large Language Models
Yiming Yao, Fei Liu, Ji Cheng +1
Many real-world optimization scenarios involve expensive evaluation with unknown and heterogeneous costs. Cost-aware Bayesian optimization stands out as a prominent solution in add…
Smooth Tchebycheff Scalarization for Multi-Objective Optimization
Xi Lin, Xiaoyuan Zhang, Zhiyuan Yang +3
Multi-objective optimization problems can be found in many real-world applications, where the objectives often conflict each other and cannot be optimized by a single solution. In…
Multi-Task Learning for Routing Problem with Cross-Problem Zero-Shot Generalization
Fei Liu, Xi Lin, Zhenkun Wang +3
Vehicle routing problems (VRPs), which can be found in numerous real-world applications, have been an important research topic for several decades. Recently, the neural combinatori…
L-AutoDA: Leveraging Large Language Models for Automated Decision-based Adversarial Attacks
Ping Guo, Fei Liu, Xi Lin +2
In the rapidly evolving field of machine learning, adversarial attacks present a significant challenge to model robustness and security. Decision-based attacks, which only require…
Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model
Fei Liu, Xialiang Tong, Mingxuan Yuan +5
Heuristics are widely used for dealing with complex search and optimization problems. However, manual design of heuristics can be often very labour extensive and requires rich work…