Geometric semantic genetic programming (GSGP) is a variant of genetic programming (GP) that directly searches the semantic space of programs to produce candidate solutions. GSGP has shown considerable success in improving the performance of GP in terms of program correctness, however this comes at the expense of exponential program growth. Subsequent attempts to address this growth have not fully-exploited the fact that GSGP searches by producing linear combinations of existing solutions. This paper examines this property of GSGP and frames the method as an ensemble learning approach by redefining mutation and crossover as examples of boosting and stacking, respectively. The ensemble interpretation allows for simple integration of regularisation techniques that significantly reduce the size of the resultant programs. Additionally, this paper examines the quality of parse tree base learners within this ensemble learning interpretation of GSGP and suggests that future research could substantially improve the quality of GSGP by examining more effective initialisation techniques. The resulting ensemble learning interpretation leads to variants of GSGP that substantially improve upon the performance of traditional GSGP in regression contexts, and produce a method that frequently outperforms gradient boosting.

几何语义遗传规划（GSGP）是遗传规划（GP）的一种变体，它直接搜索程序的语义空间以产生候选解决方案。GSGP 在提高 GP 的程序正确性方面的性能方面取得了相当大的成功，但这是以指数级程序增长为代价的。随后解决这种增长问题的尝试并未充分利用 GS​​GP 通过生成现有解决方案的线性组合进行搜索的事实。本文研究了 GSGP 的这一特性，并通过重新定义突变和交叉分别作为提升和堆叠的示例，将该方法构建为集成学习方法。集成解释允许简单地集成正则化技术，从而显着减小所得程序的大小。此外，本文还研究了 GSGP 集成学习解释中解析树基学习器的质量，并建议未来的研究可以通过检查更有效的初始化技术来大幅提高 GSGP 的质量。由此产生的集成学习解释导致了 GSGP 的变体，这些变体大大提高了传统 GSGP 在回归环境中的性能，并产生了一种经常优于梯度提升的方法。