POCAII通过分离搜索与评估阶段，实现高效低资源超参优化。
POCAII: Parameter Optimization with Conscious Allocation using Iterative Intelligence
- 分阶段优化：前期专注生成候选配置，后期加强评估
- 低预算下优于SMAC、BOHB等主流方法，性能更稳定
- 适合计算资源有限的现实场景，尤其适用于昂贵模型训练
本文首次提出超参数优化算法POCAII。与Hyperband和成功削减（Successive Halving）不同，POCAII明确区分搜索与评估阶段，并在两个阶段均采用合理的探索与利用策略。该设计使算法能灵活分配优化预算：初期聚焦生成多样配置，后期逐步增加评估投入。在低预算条件下，POCAII相较于SMAC、BOHB和DEHB等先进方法表现更优。实证结果表明，该算法具备更高鲁棒性与更低方差，尤其适用于训练成本极高的实际问题，对资源受限的实践者具有广泛适用性。
原文摘要 · Abstract (English)
In this paper we propose for the first time the hyperparameter optimization (HPO) algorithm POCAII. POCAII differs from the Hyperband and Successive Halving literature by explicitly separating the search and evaluation phases and utilizing principled approaches to exploration and exploitation principles during both phases. Such distinction results in a highly flexible scheme for managing a hyperparameter optimization budget by focusing on search (i.e., generating competing configurations) towards the start of the HPO process while increasing the evaluation effort as the HPO comes to an end. POCAII was compared to state of the art approaches SMAC, BOHB and DEHB. Our algorithm shows superior performance in low-budget hyperparameter optimization regimes. Since many practitioners do not have exhaustive resources to assign to HPO, it has wide applications to real-world problems. Moreover, the empirical evidence showed how POCAII demonstrates higher robustness and lower variance in the results. This is again very important when considering realistic scenarios with extremely expensive models to train.
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