






















This paper proposes a new method for hyperparameter optimization (HPO) that balances exploration and exploitation. While evolutionary algorithms (EAs) show promise in HPO, they often struggle with effective exploitation. To address this, we integrate a linear surrogate model into a genetic algorithm (GA), allowing for smooth integration of multiple strategies. This combination improves exploitation performance, achieving an average improvement of 1.89 percent (max 6.55 percent, min -3.45 percent) over existing HPO methods.
此内容由惯性聚合(RSS阅读器)自动聚合整理,仅供阅读参考。 原文来自 — 版权归原作者所有。