Transfer learning in constructive induction with Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Munoz:GPEM:TL,
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author = "Luis Munoz and Leonardo Trujillo and Sara Silva",
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title = "Transfer learning in constructive induction with
Genetic Programming",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2020",
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volume = "21",
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number = "4",
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pages = "529--569",
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month = dec,
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keywords = "genetic algorithms, genetic programming, Transfer
learning, Constructive induction of features",
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ISSN = "1389-2576",
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DOI = "doi:10.1007/s10710-019-09368-y",
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size = "41 pages",
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abstract = "Transfer learning (TL) is the process by which some
aspects of a machine learning model generated on a
source task is transferred to a target task, to
simplify the learning required to solve the target. TL
in Genetic Programming (GP) has not received much
attention, since it is normally assumed that an evolved
symbolic expression is specifically tailored to a
problem's data and thus cannot be used in other
problems. The goal of this work is to present a broad
and diverse study of TL in GP, considering a varied set
of source and target tasks, and dealing with questions
that have received little, or no attention, in previous
GP literature. In particular, this work studies the
performance of transferred solutions when the source
and target tasks are from different domains, and when
they do not share a similar input feature space.
Additionally, the relationship between the success and
failure of transferred solutions is studied,
considering different source and target tasks. Finally,
the pred",
- }
Genetic Programming entries for
Luis Munoz Delgado
Leonardo Trujillo
Sara Silva
Citations