An ensemble learning interpretation of geometric semantic genetic programming
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- @Article{dick:2024:GPEM,
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author = "Grant Dick",
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title = "An ensemble learning interpretation of geometric
semantic genetic programming",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2024",
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volume = "25",
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pages = "Article no 9",
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month = "11 " # mar,
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note = "Online first",
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keywords = "genetic algorithms, genetic programming, Boosting,
Base learner, Geometric interpretation",
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ISSN = "1389-2576",
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DOI = "doi:10.1007/s10710-024-09482-6",
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size = "26 Pages",
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abstract = "Geometric semantic genetic programming (GSGP) is a
variant of genetic programming (GP) that directly
searches the semantic space of programs to produce
candidate solutions. GSGP has shown considerable
success in improving the performance of GP in terms of
program correctness, however this comes at the expense
of exponential program growth. Subsequent attempts to
address this growth have not fully-exploited the fact
that GSGP searches by producing linear combinations of
existing solutions. This paper examines this property
of GSGP and frames the method as an ensemble learning
approach by redefining mutation and crossover as
examples of boosting and stacking, respectively. The
ensemble interpretation allows for simple integration
of regularisation techniques that significantly reduce
the size of the resultant programs. Additionally, this
paper examines the quality of parse tree base learners
within this ensemble learning interpretation of GSGP
and suggests that future research could substantially
improve the quality of GSGP by examining more effective
initialisation techniques. The resulting ensemble
learning interpretation leads to variants of GSGP that
substantially improve upon the performance of
traditional GSGP in regression contexts, and produce a
method that frequently outperforms gradient boosting.",
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notes = "Department of Information Science, University of
Otago, Dunedin, New Zealand",
- }
Genetic Programming entries for
Grant Dick
Citations