Evolutionary Ensemble Learning

author = "Malcolm I. Heywood",

title = "Evolutionary Ensemble Learning",

series = "Handbook of Evolutionary Machine Learning",

publisher = "Springer",

year = "2024",

editor = "Wolfgang Banzhaf and Penousal Machado and Mengjie Zhang",

chapter = "8",

pages = "205--243",

keywords = "genetic algorithms, genetic programming",

isbn13 = "978-981-99-3814-8",

DOI = "

10.1007/978-981-99-3814-8_8",

abstract = "Evolutionary Ensemble Learning (EEL) provides a general approach for scaling evolutionary learning algorithms to increasingly complex tasks. This is generally achieved by developing a diverse complement of models that provide solutions to different (yet overlapping) aspects of the task. This chapter reviews the topic of EEL by considering two basic application contexts that were initially developed independently: (1) ensembles as applied to classification and regression problems and (2) multi-agent systems as typically applied to reinforcement learning tasks. We show that common research themes have developed from the two communities, resulting in outcomes applicable to both application contexts. More recent developments reviewed include EEL frameworks that support variable-sized ensembles, scaling to high cardinality or dimensionality, and operation under dynamic environments. Looking to the future we point out that the versatility of EEL can lead to developments that support interpretable solutions and lifelong/continuous learning.",

notes = "Also known as \cite{Heywood2024}",