Selective Equation Constructor: A Scalable Genetic Algorithm
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- @InProceedings{Heller:2018:INISTA,
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author = "Lauren Heller and Michail Tsikerdekis",
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booktitle = "2018 Innovations in Intelligent Systems and
Applications (INISTA)",
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title = "Selective Equation Constructor: A Scalable Genetic
Algorithm",
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year = "2018",
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abstract = "Efforts to improve machine learning performance begin
with defining a valuable feature set. However, datasets
with copious amounts of attributes can have relevant
information that is obscured by its high
dimensionality, which can be caused by repetitive
characteristics or irrelevant qualities. Genetic
algorithms provide improvements to feature sets through
dimensionality reduction and feature construction. Most
genetic algorithms follow the theoretical framework of
evolutionary theory where a population of features
randomly evolves through generations through a series
of random operations such as crossover and mutation.
While successful, the randomness of feature
modification operations and derived constructed
features may yield children that under-perform compared
to their ancestors, yet their properties are used in
future generations. We developed a new genetic
algorithm called Selective Equation Constructor (SEC)
that evolves constructed features selectively in order
to limit the shortcomings of other genetic algorithms.
The algorithm leads to faster computation and better
results compared to similar algorithms. Analysis of the
results indicates increases in classification accuracy,
decreased run time, and reduction in attribute count.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/INISTA.2018.8466278",
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month = jul,
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notes = "Also known as \cite{8466278}",
- }
Genetic Programming entries for
Lauren Heller
Michail Tsikerdekis
Citations