2 code implementations • 11 Mar 2024 • Minsu Kim, Sanghyeok Choi, Jiwoo Son, Hyeonah Kim, Jinkyoo Park, Yoshua Bengio
This paper introduces the Generative Flow Ant Colony Sampler (GFACS), a novel neural-guided meta-heuristic algorithm for combinatorial optimization.
no code implementations • 5 Feb 2024 • Hyeonah Kim, Minsu Kim, Sanghyeok Choi, Jinkyoo Park
This paper proposes a novel variant of GFlowNet, genetic-guided GFlowNet (Genetic GFN), which integrates an iterative genetic search into GFlowNet.
1 code implementation • 29 Jun 2023 • Hyeonah Kim, Jinkyoo Park, Changhyun Kwon
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.
1 code implementation • 29 Jun 2023 • Federico Berto, Chuanbo Hua, Junyoung Park, Minsu Kim, Hyeonah Kim, Jiwoo Son, Haeyeon Kim, Joungho Kim, Jinkyoo Park
To address these challenges, we introduce RL4CO, a unified Reinforcement Learning (RL) for Combinatorial Optimization (CO) library.
1 code implementation • 5 Jun 2023 • Jiwoo Son, Minsu Kim, Sanghyeok Choi, Hyeonah Kim, Jinkyoo Park
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.
1 code implementation • 5 Jun 2023 • Jiwoo Son, Minsu Kim, Hyeonah Kim, Jinkyoo Park
First, SML transforms the context embedding for subsequent adaptation of SAGE based on scale information.
no code implementations • 2 Jun 2023 • Hyeonah Kim, Minsu Kim, Sungsoo Ahn, Jinkyoo Park
Deep reinforcement learning (DRL) has significantly advanced the field of combinatorial optimization (CO).