@inproceedings {pub6696,
	title = {Determining Destroy Sets in Large Neighborhood Search by Generative Flow Networks},
	author = {Maria Bresich AND Jingyi Peng AND Guenther Raidl AND Steffen Limmer},
	year = {2026},
	month = {August},
	abstract = {Large Neighborhood Search (LNS) is an often applied meta-heuristic to heuristically solve challenging combinatorial optimization problems. Typically, it relies on the scheme of iteratively removing parts of a solution by one or more destroy operators and cleverly augmenting the solution again by repair operators. Destroy operators are usually handcrafted and often a mixture of uniform random selection and problem-specific heuristics, and their choice can dramatically impact the performance. Inspired by the recent success of Generative Flow Networks (GFlowNets), we propose their usage to compile promising destroy sets. These generative deep learning models stand out in that their goal is not to learn a single high-reward structure but to produce diverse structures according to a complex probability density function. As considering many different but promising destroy sets is fundamental in LNS, we believe GFlowNets to be well suited for this purpose. We study an intentionally generic and simple LNS framework and apply it to three hard graph problems. The only problem-specific part is the repair operator that is realized by basic iterated random construction heuristics. Our GFlowNets use graph isomorphism networks and are trained offline on
representative unlabeled problem instances. When applying the models, the effectiveness of the LNS is almost consistently increased in terms of faster convergence to better final solutions compared to a corresponding baseline LNS. Moreover, a comparison to integer linear programming models solved by Gurobi shows that they are clearly dominated by our GFlowNet-guided LNS on the considered hard benchmark instances.},
	publisher = {Springer, LNCS, LNAI, LNBI},
	booktitle = {Parallel Problem Solving from Nature -- PPSN XIX},
	pages = {265-281}
}