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Spotlights is presented, a system that performs optimization-opportunity discovery as a distinct and empirically evaluable step between a broad engineering objective and subsequent implementation and validation.
This work evaluates three evolutionary search strategies on five optimization tasks, commonly used by papers in the genre to report results, and suggests that the best way to split a fixed budget between more seeds and more iterations changes with the strategy, the task, and the total budget.
This work contributes a cost-controlled characterization of when cheap-tier search substitutes for target-tier search, and where it fails.
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