= ParamILS
{c}
<ParamILS> is a framework for <algorithm configuration> based on <iterated local search>. It explores an <algorithm parameter space> using improvements to nearby configurations, perturbations and occasional restarts. <BasicILS> compares configurations on a fixed number of matched training runs; <FocusedILS> adapts how many runs support a comparison.<ParamILS: An Automatic Algorithm Configuration Framework by Frank Hutter and others>[Section 3, Procedures 1–3]{ref}
The distinctive efficiency idea of <adaptive capping> is to stop spending evaluation time on a candidate once it cannot win the comparison under the stated cost model. Consider an original example with three fixed instances and mean nonnegative runtime as the <objective function>. A fully evaluated incumbent has total time $12$ seconds, hence mean $4$ seconds. If the first two candidate runs already total $13$ seconds, even a zero-time final run leaves mean at least $13/3>4$. Rejecting that candidate does not require finishing the last run.
This argument depends on its assumptions: the comparison uses the same fixed instance set and a lower bound on unfinished cost. It cannot be transferred unchanged to arbitrary quality scores or a different timeout penalty. The paper distinguishes trajectory-preserving capping from a more aggressive heuristic variant; faster rejection does not always carry the same guarantee.<ParamILS: An Automatic Algorithm Configuration Framework by Frank Hutter and others>[Section 4.1.1–4.1.2, bounds and two capping policies]{ref}
The method turns tuning into a measured optimization experiment, but a good observed configuration is not a proof of a <global minimum>. The neighborhood, evaluation budget and deployment workload remain relevant to what the returned setting establishes.
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