From Mechanism to Decision in Evolutionary Test Optimization And Ai Server Test Automation: Sensitivity Analysis for Credible Translation

Abstract

A central challenge in evolutionary test optimization and AI server test automation is to compare studies whose mechanisms and validation settings do not share a single denominator. The present review uses adaptive evolutionary search for optimizing large-scale AI-server tests and a process-level automation framework for testing large AI-server fleets as focal cases for a boundary-aware synthesis. Two target papers are triangulated against 12 locally validated publications. The comparison follows fitness design, exploration-exploitation, constraint handling, stopping rules, reproducibility and deliberately separates mechanistic interpretation from performance ranking, because the latter can conceal incompatible experimental or operational conditions. Across the evidence base, the decisive issue is alignment: fitness design shapes what is observed, exploration-exploitation shapes how it is compared, and reproducibility governs whether the conclusion can be transferred. Uncertainty is most informative when reported as part of the result rather than treated as a postscript. The resulting framework supports reproducible comparison while preserving differences between study designs, and it identifies concrete points at which transfer claims should be narrowed or retested.