Genetic Programming is Naturally Suited to Evolve Bagging Ensembles

TL;DR

This paper demonstrates that simple modifications to traditional genetic programming algorithms enable efficient evolution of bagging ensembles, matching state-of-the-art methods without added complexity.

Contribution

It shows that minimal changes to fitness evaluation and selection suffice for GP to effectively evolve ensembles, simplifying the process.

Findings

The proposed method performs well against state-of-the-art algorithms.

Minor modifications enable efficient ensemble evolution.

The approach scales with ensemble size and maintains evolvability.

Abstract

Learning ensembles by bagging can substantially improve the generalization performance of low-bias, high-variance estimators, including those evolved by Genetic Programming (GP). To be efficient, modern GP algorithms for evolving (bagging) ensembles typically rely on several (often inter-connected) mechanisms and respective hyper-parameters, ultimately compromising ease of use. In this paper, we provide experimental evidence that such complexity might not be warranted. We show that minor changes to fitness evaluation and selection are sufficient to make a simple and otherwise-traditional GP algorithm evolve ensembles efficiently. The key to our proposal is to exploit the way bagging works to compute, for each individual in the population, multiple fitness values (instead of one) at a cost that is only marginally higher than the one of a normal fitness evaluation. Experimental comparisons on classification and regression tasks taken and reproduced from prior studies show that our algorithm fares very well against state-of-the-art ensemble and non-ensemble GP algorithms. We further provide insights into the proposed approach by (i) scaling the ensemble size, (ii) ablating the changes to selection, (iii) observing the evolvability induced by traditional subtree variation. Code: https://github.com/marcovirgolin/2SEGP.