Evolutionary Regression and Modelling
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InCollection{chen:2023:hbeml,
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author = "Qi Chen and Bing Xue and Will Browne and
Mengjie Zhang",
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title = "Evolutionary Regression and Modelling",
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booktitle = "Handbook of Evolutionary Machine Learning",
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publisher = "Springer Nature",
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year = "2023",
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editor = "Wolfgang Banzhaf and Penousal Machado and
Mengjie Zhang",
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series = "Genetic and Evolutionary Computation (GEVO)",
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pages = "121--149",
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address = "Singapore",
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edition = "1",
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month = "2 " # nov,
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-981-99-3813-1",
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ISSN = "1932-0167",
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DOI = "doi:10.1007/978-981-99-3814-8_5",
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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.",
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notes = "also known as \cite{Chen2024}",
- }
Genetic Programming entries for
Qi Chen
Bing Xue
Will N Browne
Mengjie Zhang
Citations