MILP-Evo: Closed-Loop Fully Automatic Design of MILP Solvers

Machine learning methods have shown that data-driven policies can accelerate mixed-integer linear programming (MILP) solvers, but many such approaches remain difficult to inspect, adapt, and deploy because the learned policy is represented as an external predictor or other opaque model. By contrast, explicit solver logic is easier to understand and integrate, but is usually hand-designed rather than learned from solver feedback. We study the automatic design of executable white-box components for MILP solvers, using their end-to-end behavior on MILP instances to guide iterative refinement. To this end, we introduce MILP-Evo, a framework implemented through PySCIPOpt, and instantiate it on the joint design of a cut selector and a branching rule. Candidate implementations are executed directly within SCIP and refined using their runtime outcomes and search-process diagnostics. The resulting solver components can be inspected, modified, and deployed within standard solver workflows. Across four families, MILP-Evo performs strongly on independent set, combinatorial auctions, and set cover, and remains competitive on facility location.