Ant Colony Sampling with GFlowNets for Combinatorial Optimization
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917939755614208 |
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| author | Kim, Minsu Choi, Sanghyeok Kim, Hyeonah Son, Jiwoo Park, Jinkyoo Bengio, Yoshua |
| author_facet | Kim, Minsu Choi, Sanghyeok Kim, Hyeonah Son, Jiwoo Park, Jinkyoo Bengio, Yoshua |
| contents | 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 first leverages Generative Flow Networks (GFlowNets) to amortize a \emph{multi-modal} prior distribution over combinatorial solution space that encompasses both high-reward and diversified solutions. This prior is iteratively updated via parallel stochastic search in the spirit of Ant Colony Optimization (ACO), leading to the posterior distribution that generates near-optimal solutions. Extensive experiments across seven combinatorial optimization problems demonstrate GFACS's promising performances. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_07041 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Ant Colony Sampling with GFlowNets for Combinatorial Optimization Kim, Minsu Choi, Sanghyeok Kim, Hyeonah Son, Jiwoo Park, Jinkyoo Bengio, Yoshua Machine Learning Neural and Evolutionary Computing 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 first leverages Generative Flow Networks (GFlowNets) to amortize a \emph{multi-modal} prior distribution over combinatorial solution space that encompasses both high-reward and diversified solutions. This prior is iteratively updated via parallel stochastic search in the spirit of Ant Colony Optimization (ACO), leading to the posterior distribution that generates near-optimal solutions. Extensive experiments across seven combinatorial optimization problems demonstrate GFACS's promising performances. |
| title | Ant Colony Sampling with GFlowNets for Combinatorial Optimization |
| topic | Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2403.07041 |