Pruning techniques for mixed ensembles of genetic programming models

Mauro Castelli, Ivo Gonçalves, Luca Manzoni, Leonardo Vanneschi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)
8 Downloads (Pure)


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.

Original languageEnglish
Title of host publicationGenetic Programming
Subtitle of host publication21st European Conference, EuroGP 2018, Proceedings
EditorsMauro Castelli, Lukas Sekanina, Mengjie Zhang, Stefano Cagnoni, Pablo García-Sánchez
PublisherSpringer Verlag
Number of pages16
ISBN (Print)9783319775524
Publication statusPublished - 1 Jan 2018
Event21st European Conference on Genetic Programming, EuroGP 2018 - Parma, Italy
Duration: 4 Apr 20186 Apr 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10781 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference21st European Conference on Genetic Programming, EuroGP 2018


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