Skip to main content
Kent Academic Repository

A survey of evolutionary algorithms for supervised ensemble learning

Cagnini, Henry E. L., das Dores, S.C.N., Freitas, Alex A., Barros, Rodrigo C. (2023) A survey of evolutionary algorithms for supervised ensemble learning. Knowledge Engineering Review, 38 (e1). pp. 1-43. ISSN 0269-8889. E-ISSN 1469-8005. (doi:10.1017/S0269888923000024) (KAR id:100315)


This paper presents a comprehensive review of evolutionary algorithms that learn an ensemble of predictive models for supervised machine learning (classification and regression). We propose a detailed four-level taxonomy of studies in this area. The first level of the taxonomy categorizes studies based on which stage of the ensemble learning process is addressed by the evolutionary algorithm: the generation of base models, model selection, or the integration of outputs. The next three levels of the taxonomy further categorize studies based on methods used to address each stage. In addition, we categorize studies according to the main types of objectives optimized by the evolutionary algorithm, the type of base learner used and the type of evolutionary algorithm used. We also discuss controversial topics, like the pros and cons of the selection stage of ensemble learning, and the need for using a diversity measure for the ensemble’s members in the fitness function. Finally, as conclusions, we summarize our findings about patterns in the frequency of use of different methods and suggest several new research directions for evolutionary ensemble learning.

Item Type: Article
DOI/Identification number: 10.1017/S0269888923000024
Uncontrolled keywords: machine learning, classification, ensembles, evolutionary algorithms
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Funders: University of Kent (
Depositing User: Alex Freitas
Date Deposited: 04 Mar 2023 11:18 UTC
Last Modified: 01 Sep 2023 23:00 UTC
Resource URI: (The current URI for this page, for reference purposes)

University of Kent Author Information

  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.