24 papers
DCL-GPGLS: Dynamic Curriculum Learning for Genetic Programming Guided Local Search in Large-Scale Vehicle Routing
Saining Liu, Yi Mei, Mengjie Zhang
Genetic Programming Guided Local Search (GPGLS) uses genetic programming to evolve utility functions for guided local search in large-scale vehicle routing problems (LSVRPs). Evalu…
Genetic Programming with Behaviour-based Niching for Learning Guided Local Search in Vehicle Routing Problems
Saining Liu, Yi Mei, Mengjie Zhang
Genetic Programming Guided Local Search (GPGLS) learns utility functions that guide local search for vehicle routing. Its evolving programs can have similar fitness while inducing…
Surrogate-Assisted Genetic Programming with Phenotypic Characterisation in Dynamic Multi-Mode Project Scheduling
Yuan Tian, Yi Mei, Mengjie Zhang
Dynamic multi-mode resource-constrained project scheduling requires decisions to be made under precedence constraints, limited resources, multiple execution modes, and uncertain ac…
Accelerated Genetic Programming Hyper-Heuristics for Simulation-Based Scheduling via Agentic AI
Heyang Thomas Li, Alexander Pletzer, Yuan Tian +2
Python is widely used in scientific research because it enables rapid development and provides rich ecosystems for data analysis, artificial intelligence (AI), and machine learning…
Guiding Large Language Models with Genetic Programming-Evolved Heuristic Knowledge for Dynamic Multi-Mode Project Scheduling
Yuan Tian, Yi Mei, Mengjie Zhang
In dynamic multi-mode project scheduling, activities have alternative execution modes and uncertain durations, while precedence relations and limited resources constrain their exec…
AutoSG: LLM-Driven Solver Generation Solely from Task Prompts for Expensive Optimization
Haoran Gu, Handing Wang, Yi Mei +1
Expensive optimization tasks are ubiquitous in real-world applications, demanding highly specialized solvers. While LLM-driven automated solver generation shows promise, current pa…
MotiMotion: Motion-Controlled Video Generation with Visual Reasoning
Lee Hsin-Ying, Hanwen Jiang, Yiqun Mei +3
Current motion-controlled image-to-video generation models rigidly follow user-provided trajectories that are often sparse, imprecise, and causally incomplete. Such reliance often…
Machine Learning-based Two-Stage Graph Sparsification for the Travelling Salesman Problem
Bo-Cheng Lin, Yi Mei, Mengjie Zhang
High-performance TSP solvers such as Lin-Kernighan-Helsgaun (LKH) search within a \emph{candidate graph} -- a small subset of edges pre-selected for the solver -- rather than over…
Surrogate-Assisted Genetic Programming with Rank-Based Phenotypic Characterisation for Dynamic Multi-Mode Project Scheduling
Yuan Tian, Yi Mei, Mengjie Zhang
The dynamic multi-mode resource-constrained project scheduling problem (DMRCPSP) is of practical importance, as it requires making real-time decisions under changing project states…
Investigation of the Generalisation Ability of Genetic Programming-evolved Scheduling Rules in Dynamic Flexible Job Shop Scheduling
Luyao Zhu, Fangfang Zhang, Yi Mei +1
Dynamic Flexible Job Shop Scheduling (DFJSS) is a complex combinatorial optimisation problem that requires simultaneous machine assignment and operation sequencing decisions in dyn…
Scalable Knee-Point Guided Activity Group Selection in Multi-Tree Genetic Programming for Dynamic Multi-Mode Project Scheduling
Yuan Tian, Yi Mei, Mengjie Zhang
The dynamic multi-mode resource-constrained project scheduling problem is a challenging scheduling problem that requires making decisions on both the execution order of activities…
Keep Rehearsing and Refining: Lifelong Learning Vehicle Routing under Continually Drifting Tasks
Jiyuan Pei, Yi Mei, Jialin Liu +2
Existing neural solvers for vehicle routing problems (VRPs) are typically trained either in a one-off manner on a fixed set of pre-defined tasks or in a lifelong manner with tasks…
HeatACO: A Heatmap-Guided Max--Min Ant System for Large-Scale Travelling Salesman Problems
Bo-Cheng Lin, Yi Mei, Mengjie Zhang
Non-autoregressive neural solvers predict an edge-confidence heatmap for the Travelling Salesman Problem (TSP) in one forward pass, but a decoder must still produce a feasible Hami…
Overlooked Safety Vulnerability in LLMs: Malicious Intelligent Optimization Algorithm Request and its Jailbreak
Haoran Gu, Handing Wang, Yi Mei +2
The widespread deployment of large language models (LLMs) has raised growing concerns about their misuse risks and associated safety issues. While prior studies have examined the s…
GAMA: A Neural Neighborhood Search Method with Graph-aware Multi-modal Attention for Vehicle Routing Problem
Xiangling Chen, Yi Mei, Mengjie Zhang
Recent advances in neural neighborhood search methods have shown potential in tackling Vehicle Routing Problems (VRPs). However, most existing approaches rely on simplistic state r…
SymLight: Exploring Interpretable and Deployable Symbolic Policies for Traffic Signal Control
Xiao-Cheng Liao, Yi Mei, Mengjie Zhang
Deep Reinforcement Learning have achieved significant success in automatically devising effective traffic signal control (TSC) policies. Neural policies, however, tend to be over-p…
DyRo-MCTS: A Robust Monte Carlo Tree Search Approach to Dynamic Job Shop Scheduling
Ruiqi Chen, Yi Mei, Fangfang Zhang +1
Dynamic job shop scheduling, a fundamental combinatorial optimisation problem in various industrial sectors, poses substantial challenges for effective scheduling due to frequent d…
Lifelong Learning with Behavior Consolidation for Vehicle Routing
Jiyuan Pei, Yi Mei, Jialin Liu +2
Recent neural solvers have demonstrated promising performance in learning to solve routing problems. However, existing studies are primarily based on one-off training on one or a s…
Bridging Fitness With Search Spaces By Fitness Supremums: A Theoretical Study on LGP
Zhixing Huang, Yi Mei, Fangfang Zhang +2
Genetic programming has undergone rapid development in recent years. However, theoretical studies of genetic programming are far behind. One of the major obstacles to theoretical s…
LiBOG: Lifelong Learning for Black-Box Optimizer Generation
Jiyuan Pei, Yi Mei, Jialin Liu +1
Meta-Black-Box Optimization (MetaBBO) garners attention due to its success in automating the configuration and generation of black-box optimizers, significantly reducing the human…
Curriculum Learning in Genetic Programming Guided Local Search for Large-scale Vehicle Routing Problems
Saining Liu, Yi Mei, Mengjie Zhang
Manually designing (meta-)heuristics for the Vehicle Routing Problem (VRP) is a challenging task that requires significant domain expertise. Recently, data-driven approaches have e…
Decoding One Safety Trigger Token for Balancing Safety and Usability in Large Language Models
Haoran Gu, Handing Wang, Yi Mei +2
Large Language Models (LLMs) have been extensively used across diverse domains, including virtual assistants, automated code generation, and scientific research. However, they rema…
ParetoHqD: Fast Offline Multiobjective Alignment of Large Language Models using Pareto High-quality Data
Haoran Gu, Handing Wang, Yi Mei +2
Aligning large language models with multiple human expectations and values is crucial for ensuring that they adequately serve a variety of user needs. To this end, offline multiobj…
Generalized Phase Pressure Control Enhanced Reinforcement Learning for Traffic Signal Control
Xiao-Cheng Liao, Yi Mei, Mengjie Zhang +1
Appropriate traffic state representation is crucial for learning traffic signal control policies. However, most of the current traffic state representations are heuristically desig…