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arXiv Machine Learning By Gwen maudet, Mathis Da Cunha, Gr\'egoire Danoy

Interpretable Cutting Plane Selection for Mixed-Integer Linear Programming Using Genetic Programming

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The paper introduces GPSCS, a learning-based framework that uses genetic programming to evolve a symbolic scoring function for selecting cutting planes in mixed-integer linear programming. It also employs a statistics-based method to decide how many top-ranked cuts to add during each separation round. Experiments on diverse synthetic and real-world benchmarks show that GPSCS reduces solving time by 6% over SCIP’s default strategy and outperforms a neural-network approach and a no-cut baseline, indicating strong generalization and robustness.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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