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Found 7 papers, 6 papers with code
Ant Colony Sampling with GFlowNets for Combinatorial Optimization
This paper introduces the Generative Flow Ant Colony Sampler (GFACS), a neural-guided probabilistic search algorithm for solving combinatorial optimization (CO).
Genetic-guided GFlowNets for Sample Efficient Molecular Optimization
The challenge of discovering new molecules with desired properties is crucial in domains like drug discovery and material design.
RL4CO: a Unified Reinforcement Learning for Combinatorial Optimization Library
To address these challenges, we introduce RL4CO, a unified Reinforcement Learning (RL) for Combinatorial Optimization (CO) library.
A Neural Separation Algorithm for the Rounded Capacity Inequalities
We design a learning-based separation heuristic algorithm with graph coarsening that learns the solutions of the exact separation problem with a graph neural network (GNN), which is trained with small instances of 50 to 100 customers.
Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization
First, SML transforms the context embedding for subsequent adaptation of SAGE based on scale information.
Equity-Transformer: Solving NP-hard Min-Max Routing Problems as Sequential Generation with Equity Context
Notably, our method achieves significant reductions of runtime, approximately 335 times, and cost values of about 53\% compared to a competitive heuristic (LKH3) in the case of 100 vehicles with 1, 000 cities of mTSP.
Symmetric Replay Training: Enhancing Sample Efficiency in Deep Reinforcement Learning for Combinatorial Optimization
Deep reinforcement learning (DRL) has significantly advanced the field of combinatorial optimization (CO).
