AWhat changed
Extends sequential model-based configuration to categorical parameters and instance sets using random-forest performance models and aggressive incumbent/challenger intensification.
Scientific scope
Extends sequential model-based configuration to categorical parameters and instance sets using random-forest performance models and aggressive incumbent/challenger intensification. Random forests, Bayesian/model-based optimization and variance-reduction principles are inherited. Early comparisons can occur before statistical significance; the original lacks adaptive capping and evaluates small-cutoff SAT/MIP scenarios. Predictive forest variance is a modeling heuristic, not universal calibrated uncertainty or global-optimality certification.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Random forests, Bayesian/model-based optimization and variance-reduction principles are inherited. Early comparisons can occur before statistical significance; the original lacks adaptive capping and evaluates small-cutoff SAT/MIP scenarios. Predictive forest variance is a modeling heuristic, not universal calibrated uncertainty or global-optimality certification.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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