Controlling Functional Complexity for Overfitting Reduction and Improved Interpretability in GP
提出一种通过多目标优化同时最小化损失和惩罚功能复杂度的方法，减少遗传规划的过拟合并提高模型可解释性，实验表明在泛化和特征选择上优于现有方法。
Like other machine learning methods, Genetic Programming (GP) frequently faces the issue of overfitting when applied to supervised learning tasks. Traditional regularization techniques, though well-studied, are challenging to apply to GP due to the free-form nature of the evolved models. This work proposes a novel approach that prevents overfitting while inherently improving the interpretability of GP models. It involves a dual optimization process that minimizes loss while penalizing functional complexity using multi-objective selection mechanisms. The improved complexity measure used in this study approximates the mathematical curvature of a function in linear time. While loss minimization is common in GP, penalizing functional complexity is an additional step aimed at evolving robust and smooth functions, less prone to overfitting and potentially more interpretable. Experimental results demonstrate the effectiveness of the two variants of our method, benchmarked against standard GP and two of the seemingly best overfitting-reduction methods found in the literature. By focusing on both loss and complexity, our approach achieves state-of-the-art generalization on difficult problems and a strong feature selection that improves interpretability, making it a unified improvement of GP.