arXiv:2610.22397v1 Announce Type: new Abstract: Engineering design tools increasingly embed optimizers as ready-made components that users select by algorithm name, while operator settings and bound, integer and duplicate handling stay fixed inside each tool. This study tests whether the name predicts the result. A tool-agnostic harness exposes one deterministic black box, an emulator of a published 22-variable mixed-integer building-performance problem, to the native optimizers of three Grasshopper plug-ins, to canonical NSGA-II, SMS-EMOA and GDE3 from Python libraries, and to the model-based RBFMOpt. Eighteen configurations were run at 5,000 evaluations under three population allocations, with statistics fixed in advance. Four NSGA-II implementations differed by up to 5% in hypervolume, more than three canonical algorithms of different families differed from one another, and their response to population size differed by up to 10 percentage points; for two MOEA/D implementations, by 2...