用遗传算法自动优化可解释的回归模型，提升精度同时简化结构。
Genetic Generalized Additive Models
- 用多目标遗传算法联合优化模型复杂度与预测误差
- 在加州房价数据上精度更高或复杂度更低
- 适合需要高可解释性的建模场景
广义加性模型（GAMs）在预测精度与可解释性之间取得平衡，但手动配置结构困难。我们提出使用多目标遗传算法NSGA-II自动优化GAMs，同时最小化预测误差（RMSE）和一个包含稀疏性、平滑性与不确定性惩罚的复杂度项。在加州房价数据集上的实验表明，NSGA-II发现的GAMs在精度上优于基准线性GAMs，或在保持性能的同时显著降低复杂度。所得模型更简洁、更平滑，置信区间更窄，增强了可解释性。该框架为透明、高性能模型的自动化优化提供通用方案。代码见https://github.com/KaaustaaubShankar/GeneticAdditiveModels。
原文摘要 · Abstract (English)
Generalized Additive Models (GAMs) balance predictive accuracy and interpretability, but manually configuring their structure is challenging. We propose using the multi-objective genetic algorithm NSGA-II to automatically optimize GAMs, jointly minimizing prediction error (RMSE) and a Complexity Penalty that captures sparsity, smoothness, and uncertainty. Experiments on the California Housing dataset show that NSGA-II discovers GAMs that outperform baseline LinearGAMs in accuracy or match performance with substantially lower complexity. The resulting models are simpler, smoother, and exhibit narrower confidence intervals, enhancing interpretability. This framework provides a general approach for automated optimization of transparent, high-performing models. The code can be found at https://github.com/KaaustaaubShankar/GeneticAdditiveModels.
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