Pruning Techniques for Mixed Ensembles of Genetic Programming Models

TL;DR

This paper introduces a new ensemble strategy combining syntax-based and semantics-based genetic programming models, with pruning techniques based on correlation and entropy to enhance generalization and reduce computational costs.

Contribution

It proposes a novel ensemble construction method blending different GP approaches and introduces pruning criteria based on correlation and entropy for better ensemble performance.

Findings

Pruning based on correlation and entropy improves ensemble generalization.

The proposed method reduces computational burden in ensemble building.

Experimental results show effectiveness on complex problems.

Abstract

The objective of this paper is to define an effective strategy for building an ensemble of Genetic Programming (GP) models. Ensemble methods are widely used in machine learning due to their features: they average out biases, they reduce the variance and they usually generalize better than single models. Despite these advantages, building ensemble of GP models is not a well-developed topic in the evolutionary computation community. To fill this gap, we propose a strategy that blends individuals produced by standard syntax-based GP and individuals produced by geometric semantic genetic programming, one of the newest semantics-based method developed in GP. In fact, recent literature showed that combining syntax and semantics could improve the generalization ability of a GP model. Additionally, to improve the diversity of the GP models used to build up the ensemble, we propose different pruning criteria that are based on correlation and entropy, a commonly used measure in information theory. Experimental results,obtained over different complex problems, suggest that the pruning criteria based on correlation and entropy could be effective in improving the generalization ability of the ensemble model and in reducing the computational burden required to build it.