Reevo: Large language models as hyper-heuristics with reflective evolution
The omnipresence of NP-hard combinatorial optimization problems (COPs) compels domain
experts to engage in trial-and-error heuristic design. The long-standing endeavor of design …
experts to engage in trial-and-error heuristic design. The long-standing endeavor of design …
An example of evolutionary computation+ large language model beating human: Design of efficient guided local search
It is often very tedious for human experts to design efficient algorithms. Recently, we have
proposed a novel Algorithm Evolution using Large Language Model (AEL) framework for …
proposed a novel Algorithm Evolution using Large Language Model (AEL) framework for …
Routefinder: Towards foundation models for vehicle routing problems
This paper introduces RouteFinder, a comprehensive foundation model framework to tackle
different Vehicle Routing Problem (VRP) variants. Our core idea is that a foundation model …
different Vehicle Routing Problem (VRP) variants. Our core idea is that a foundation model …
Algorithm evolution using large language model
Optimization can be found in many real-life applications. Designing an effective algorithm for
a specific optimization problem typically requires a tedious amount of effort from human …
a specific optimization problem typically requires a tedious amount of effort from human …
Ant colony sampling with gflownets for combinatorial optimization
We present the Generative Flow Ant Colony Sampler (GFACS), a novel meta-heuristic
method that hierarchically combines amortized inference and parallel stochastic search. Our …
method that hierarchically combines amortized inference and parallel stochastic search. Our …
Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model
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 …
However, manual design of heuristics can be often very labour extensive and requires rich …
Rl4co: an extensive reinforcement learning for combinatorial optimization benchmark
We introduce RL4CO, an extensive reinforcement learning (RL) for combinatorial
optimization (CO) benchmark. RL4CO employs state-of-the-art software libraries as well as …
optimization (CO) benchmark. RL4CO employs state-of-the-art software libraries as well as …
Neural Combinatorial Optimization Algorithms for Solving Vehicle Routing Problems: A Comprehensive Survey with Perspectives
Although several surveys on Neural Combinatorial Optimization (NCO) solvers specifically
designed to solve Vehicle Routing Problems (VRPs) have been conducted. These existing …
designed to solve Vehicle Routing Problems (VRPs) have been conducted. These existing …
Self-improved learning for scalable neural combinatorial optimization
The end-to-end neural combinatorial optimization (NCO) method shows promising
performance in solving complex combinatorial optimization problems without the need for …
performance in solving complex combinatorial optimization problems without the need for …
A survey on deep learning-based algorithms for the traveling salesman problem
J Sui, S Ding, X Huang, Y Yu, R Liu, B **a… - Frontiers of Computer …, 2025 - Springer
This paper presents an overview of deep learning (DL)-based algorithms designed for
solving the traveling salesman problem (TSP), categorizing them into four categories: end-to …
solving the traveling salesman problem (TSP), categorizing them into four categories: end-to …