Evolutionary Regression and Modelling

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InCollection{chen:2023:hbeml,

• author = "Qi Chen and Bing Xue and Will Browne and Mengjie Zhang",
• title = "Evolutionary Regression and Modelling",
• booktitle = "Handbook of Evolutionary Machine Learning",
• publisher = "Springer Nature",
• year = "2023",
• editor = "Wolfgang Banzhaf and Penousal Machado and Mengjie Zhang",
• series = "Genetic and Evolutionary Computation (GEVO)",
• pages = "121--149",
• address = "Singapore",
• edition = "1",
• month = "2 " # nov,
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-981-99-3813-1",
• ISSN = "1932-0167",
• DOI = "doi:10.1007/978-981-99-3814-8_5",
• abstract = "Regression and modelling, which identify the relationship between the dependent and independent variables, play an important role in knowledge discovery from data. Symbolic regression goes a step further by learning explicitly symbolic models from data that are potentially interpretable. This chapter provides an overview of evolutionary computation techniques for regression and modelling including coefficient learning and symbolic regression. We introduce the ideas behind various evolutionary computation methods for regression and present a review of the efforts on enhancing learning capability, generalisation, interpretability and imputation of missing data in evolutionary computation for regression.",
• notes = "also known as \cite{Chen2024}",

}

Genetic Programming entries for Qi Chen Bing Xue Will N Browne Mengjie Zhang

Citations