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--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.

翻译：修改标准梯度, 替换嵌入的弱学习者, 代之以强( er), 我们介绍 SyRBo: 符号回归推进。 超过 98 个回归数据集的实验显示, 通过将少量的推进阶段 -- -- 2-5 -- -- 添加到一个符号回归器中, 通常可以实现统计上显著的改进。 我们注意到, 任何符号回归器之上的SyRBo编码是直截了当的, 增加的成本只是几轮更进化的回合。 SyRBo基本上是一个简单的附加点, 可以随时添加到一个外延的符号回归器中, 通常会产生有益的效果 。