Abstract
This paper presents a novel approach to constructing ensembles for prediction using a bootstrap aggregation (bagging) model. The proposed method uses analogies from ecological modelling to view bootstrap samples as a local adaptation resource in a spatially structured population. Through local competition and breeding, adaptation towards specific bootstrap samples takes place and the resulting ensemble emerges from a single global population in a single run. This makes better use of available computational resources, and negates the need for multiple runs typically required by a bagging approach. We examine the robustness of the method with respect to the number of bootstrap samples in the ensemble, and demonstrate that the resulting method also has a positive effect on bloat control. Finally, the effectiveness of the method relative to existing bagging approaches such as random forests is explored and encouraging performance is demonstrated on a range of benchmark problems.