论文标题
集合遗传编程
Ensemble Genetic Programming
论文作者
论文摘要
合奏学习是一种强大的范式，在最先进的机器学习方法中已使用了随机森林和XGBoost。受这些方法成功的启发，我们采用了一种称为Ensemble GP的新基因编程方法。集合GP的EVO循环循环遵循与其他基因编程系统相同的步骤，但人口结构，适应性评估和遗传操作员的差异。我们已经测试了这种方法的Oneight二进制分类问题，并以较小的模型实现了标准GP的结果明显更好。尽管其他类似方法的M3GP和XGBoost是最好的总体，但Ensemble GP能够在一个特别硬的问题上获得异常良好的概括结果，而其他方法都无法成功。
Ensemble learning is a powerful paradigm that has been usedin the top state-of-the-art machine learning methods like Random Forestsand XGBoost. Inspired by the success of such methods, we have devel-oped a new Genetic Programming method called Ensemble GP. The evo-lutionary cycle of Ensemble GP follows the same steps as other GeneticProgramming systems, but with differences in the population structure,fitness evaluation and genetic operators. We have tested this method oneight binary classification problems, achieving results significantly betterthan standard GP, with much smaller models. Although other methodslike M3GP and XGBoost were the best overall, Ensemble GP was able toachieve exceptionally good generalization results on a particularly hardproblem where none of the other methods was able to succeed.
