Simplification of genetic programs: a literature survey
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Javed:2022:DMKD,
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author = "Noman Javed and Fernand Gobet and Peter Lane",
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title = "Simplification of genetic programs: a literature
survey",
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journal = "Data Mining and Knowledge Discovery",
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year = "2022",
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volume = "36",
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number = "4",
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pages = "1279--1300",
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month = jul,
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note = "Special Issue on Explainable and Interpretable Machine
Learning and Data Mining",
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keywords = "genetic algorithms, genetic programming,
Simplification, Bloat control, Explainability,
Genetically Evolving Models in Science, GEMS",
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ISSN = "1384-5810",
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URL = "http://eprints.lse.ac.uk/114852/",
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URL = "https://rdcu.be/cUozw",
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DOI = "doi:10.1007/s10618-022-00830-7",
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size = "22 pages",
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abstract = "Genetic programming (GP), a widely used evolutionary
computing technique, suffers from bloat—the problem
of excessive growth in individuals sizes. As a result,
its ability to efficiently explore complex search
spaces reduces. The resulting solutions are less robust
and generalisable. Moreover, it is difficult to
understand and explain models which contain bloat. This
phenomenon is well researched, primarily from the angle
of controlling bloat: instead, our focus in this paper
is to review the literature from an explainability
point of view, by looking at how simplification can
make GP models more explainable by reducing their
sizes. Simplification is a code editing technique whose
primary purpose is to make GP models more explainable.
However, it can offer bloat control as an additional
benefit when implemented and applied with caution.
Researchers have proposed several simplification
techniques and adopted various strategies to implement
them. We organise the literature along multiple axes to
identify the relative strengths and weaknesses of
simplification techniques and to identify emerging
trends and areas for future exploration. We highlight
design and integration challenges and propose several
avenues for research. One of them is to consider
simplification as a standalone operator, rather than an
extension of the standard crossover or mutation
operators. Its role is then more clearly complementary
to other GP operators, and it can be integrated as an
optional feature into an existing GP setup. Another
proposed avenue is to explore complexity measures in
simplification. So far, size is the most discussed
measure, with only two pieces of prior work pointing
out the benefits of using time as a measure when
controlling bloat.",
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notes = "CPNSS, London School of Economics and Political
Science, London, UK",
- }
Genetic Programming entries for
Noman Javed
Fernand Gobet
Peter C R Lane
Citations