HEMO evolves the search harness for multi-objective optimization
The method updates one harness module at a time using solution quality, runtime, and a discounted UCB scheduler.
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The method updates one harness module at a time using solution quality, runtime, and a discounted UCB scheduler.
Branching records pruning constraints that discard all but an exponentially small fraction of assignments on several dense instance classes.
On a cell-therapy supply-chain task it reports a 0.05% mean primal gap and a 17.4× MIP speedup.
The methods target continuous and combinatorial inputs from about 10 to more than 6000 dimensions by using linear structure already in BO.
Learned models predict high-quality QAOA angles in one pass, matching slower per-instance solvers.
COMPASS combines search with learning-accelerated parallel sub-solvers for the Ordered Clustered TSP, scaling to 100K synthetic and 28.5K real e-commerce nodes.
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