Modifying standard gradient boosting by replacing the embedded weak learner in favor of a strong(er) one, we present SyRBo: symbolic-regression boosting. Experiments over 98 regression datasets show that by adding a small number of boosting stages—between 2 and 5—to a symbolic regressor, statistically significant improvements can often be attained. We note that coding SyRBo on top of any symbolic regressor is straightforward, and the added cost is simply a few more evolutionary rounds. SyRBo is essentially a simple add-on that can be readily added to an extant symbolic regressor, often with beneficial results.

中文翻译：

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符号回归提升

通过替换嵌入的弱学习者以支持强者来修改标准梯度增强，我们提出了SyRBo：符号回归增强。对98个回归数据集进行的实验表明，通过将少量增强阶段（介于2到5之间）添加到符号回归器中，通常可以实现统计学上的显着改进。我们注意到，在任何符号回归器之上编码SyRBo都是很简单的，而增加的成本只是另外几轮的进化。SyRBo本质上是一个简单的附件，可以很容易地添加到现存的符号回归器中，通常会带来有益的结果。