Improving Module Identification and Use in Grammatical Evolution
Created by W.Langdon from
gp-bibliography.bib Revision:1.8051
- @InProceedings{Murphy:2020:CEC,
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author = "Aidan Murphy and Conor Ryan",
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title = "Improving Module Identification and Use in Grammatical
Evolution",
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booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC
2020",
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year = "2020",
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editor = "Yaochu Jin",
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pages = "paper id24431",
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address = "internet",
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month = "19-24 " # jul,
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organization = "IEEE Computational Intelligence Society",
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming, grammatical
evolution",
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isbn13 = "978-1-7281-6929-3",
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DOI = "doi:10.1109/CEC48606.2020.9185571",
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abstract = "Exploiting patterns within a solution or reusing
certain functionality is often necessary to solve
certain problems. This paper proposes a new method for
identifying useful modules. Modules are only considered
if they are prevalent in the population and they are
seen to have a positive effect on an individual's
fitness. This is achieved by finding the covariance of
an individual's fitness with the presence of a
particular subtree in the overall expression.While
there are many successful systems that dynamically add
modules during Genetic Programming (GP) runs, doing so
is not trivial for Grammatical Evolution (GE), due to
the fact that it employs a mapping process to produce
individuals from binary strings, which makes it
difficult to dynamically change the mapping process
during a run. We adopt a multi-run approach which only
has a single stage of module addition to mitigate the
problems associated with continuously adding newly
found functionality to a grammar. Based on the
well-known Price Equation, our system explores the
covariance between traits to identify useful modules,
which are added to the grammar, before the system is
restarted. Grammar Augmentation through Module
Encapsulation (GAME) was tested on seven problems from
three different domains and was observed to
significantly improve the performance on 3 problems and
never showing harmful effects on any problem. GAME
found the best individual in 6 of the 7 experiments.",
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notes = "https://wcci2020.org/
University of Limerick & Lero, Ireland.
Also known as \cite{9185571}",
- }
Genetic Programming entries for
Aidan Murphy
Conor Ryan
Citations