GOAL: A Generalist Combinatorial Optimization Agent Learner
<|reference_start|>GOAL: A Generalist Combinatorial Optimization Agent Learner: Machine Learning-based heuristics have recently shown impressive performance in solving a variety of hard combinatorial optimization problems (COPs). However they generally rely on a separate neural model, specialized and trained for each single problem. Any variation of a problem requires adjustment of its model and re-training from scratch. In this paper, we propose GOAL (for Generalist combinatorial Optimization Agent Learning), a generalist model capable of efficiently solving multiple COPs and which can be fine-tuned to solve new COPs. GOAL consists of a single backbone plus light-weight problem-specific adapters, mostly for input and output processing. The backbone is based on a new form of mixed-attention blocks which allows to handle problems defined on graphs with arbitrary combinations of node, edge and instance-level features. Additionally, problems which involve heterogeneous nodes or edges, such as in multi-partite graphs, are handled through a novel multi-type transformer architecture, where the attention blocks are duplicated to attend only the relevant combination of types while relying on the same shared parameters. We train GOAL on a set of routing, scheduling and classic graph problems and show that it is only slightly inferior to the specialized baselines while being the first multi-task model that solves a variety of COPs. Finally, we showcase the strong transfer learning capacity of GOAL by fine-tuning or learning the adapters for new problems, with only few shots and little data.<|reference_end|>
@article{drakulic2024goal:,
    title={GOAL: A Generalist Combinatorial Optimization Agent Learning},
    author={Darko Drakulic, Sofia Michel, Jean-Marc Andreoli},
    journal={arXiv preprint arXiv:2406.15079},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.15079},
    primaryClass={cs.LG}
}