In recent years, genetic programming (GP)-based evolutionary feature
construction has achieved significant success. However, a primary challenge
with evolutionary feature construction is its tendency to overfit the training
data, resulting in poor generalization on unseen data. In this research, we
draw inspiration from PAC-Bayesian theory and propose using sharpness-aware
minimization in function space to discover symbolic features that exhibit
robust performance within a smooth loss landscape in the semantic space. By
optimizing sharpness in conjunction with cross-validation loss, as well as
designing a sharpness reduction layer, the proposed method effectively
mitigates the overfitting problem of GP, especially when dealing with a limited
number of instances or in the presence of label noise. Experimental results on
58 real-world regression datasets show that our approach outperforms standard
GP as well as six state-of-the-art complexity measurement methods for GP in
controlling overfitting. Furthermore, the ensemble version of GP with
sharpness-aware minimization demonstrates superior performance compared to nine
fine-tuned machine learning and symbolic regression algorithms, including
XGBoost and LightGBM.

通过使用 PAC-Bayesian 理论中的 sharpness-aware 最小化方法，在语义空间中寻找表现稳健的符号特征，该方法能够有效减轻遗传规划的过拟合问题，并在限定实例数量和标签噪声存在的情况下表现优于标准遗传规划和其他先进的复杂度测量方法。

回归中进化特征构建的锐度感知最小化

Sharpness-Aware Minimization for Evolutionary Feature Construction in  Regression

Event coreference resolution is an important research problem with many
applications. Despite the recent remarkable success of pretrained language
models, we argue that it is still highly beneficial to utilize symbolic
features for the task. However, as the input for coreference resolution
typically comes from upstream components in the information extraction
pipeline, the automatically extracted symbolic features can be noisy and
contain errors. Also, depending on the specific context, some features can be
more informative than others. Motivated by these observations, we propose a
novel context-dependent gated module to adaptively control the information
flows from the input symbolic features. Combined with a simple noisy training
method, our best models achieve state-of-the-art results on two datasets: ACE
2005 and KBP 2016.

本文提出了一种基于上下文的门控模块，通过控制符号特征的信息流动，以适应性地进行事件指代消解，结合一种简单且鲁棒的训练方法，在 ACE 2005 和 KBP 2016 的两个数据集上实现了最先进的结果。