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A survey of evolutionary algorithms for supervised ensemble learning

Published online by Cambridge University Press:  01 March 2023

Henry E. L. Cagnini Open the ORCID record for Henry E. L. Cagnini [Opens in a new window] ,
Silvia C. N. Das Dôres ,
Alex A. Freitas  and
Rodrigo C. Barros
Henry E. L. Cagnini
Affiliation:
School of Technology, PUCRS, Avenida Ipiranga 6681, Porto Alegre, RS 90619-900, Brazil; e-mails: [email protected], [email protected], [email protected]
Silvia C. N. Das Dôres
Affiliation:
School of Technology, PUCRS, Avenida Ipiranga 6681, Porto Alegre, RS 90619-900, Brazil; e-mails: [email protected], [email protected], [email protected]
Alex A. Freitas
Affiliation:
Computing School, University of Kent, Giles Ln, Canterbury CT2 7NZ, UK; e-mail: [email protected]
Rodrigo C. Barros
Affiliation:
School of Technology, PUCRS, Avenida Ipiranga 6681, Porto Alegre, RS 90619-900, Brazil; e-mails: [email protected], [email protected], [email protected]
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Abstract

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.

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Review
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The Knowledge Engineering Review , Volume 38 , 2023 , e1
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© The Author(s), 2023. Published by Cambridge University Press

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