Transfer learning in constructive induction with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{Munoz:GPEM:TL,

• author = "Luis Munoz and Leonardo Trujillo and Sara Silva",
• title = "Transfer learning in constructive induction with Genetic Programming",
• journal = "Genetic Programming and Evolvable Machines",
• year = "2020",
• volume = "21",
• number = "4",
• pages = "529--569",
• month = dec,
• keywords = "genetic algorithms, genetic programming, Transfer learning, Constructive induction of features",
• ISSN = "1389-2576",
• DOI = "doi:10.1007/s10710-019-09368-y",
• size = "41 pages",
• 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

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