Abstract
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.