Bayesian optimization is widely used for expensive black-box optimization problems. While methodological advances often focus on individual components, practical performance depends on how these components work together. We present tidyHEBO, a Bayesian optimization model. tidyHEBO jointly learns output warping and Gaussian-process parameters, evaluates acquisition functions on the original objective scale, and preserves promising novel candidates throughout evolutionary search. On the Olympus benchmark, tidyHEBO ranked first among the evaluated methods, achieving the best average ranks for typical optimization quality, worst-tail outcomes, and run-to-run variability. It also obtained results comparable to specialized optimizers on Needle-in-a-Haystack problems. On the Bayesmark benchmark, tidyHEBO also demonstrated strong results in hyperparameter tuning. Across these benchmarks, tidyHEBO combines strong optimization results with robustness across diverse black-box problems.

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