When Evolutionary Computing Meets Astro- and Geoinformatics
Created by W.Langdon from
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- @InCollection{DAGDIA:2020:KDBDAEO,
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author = "Zaineb Chelly Dagdia and Miroslav Mirchev",
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title = "When Evolutionary Computing Meets Astro- and
Geoinformatics",
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year = "2020",
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editor = "Petr Skoda and Fathalrahman Adam",
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booktitle = "Knowledge Discovery in Big Data from Astronomy and
Earth Observation",
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publisher = "Elsevier",
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pages = "283--306",
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chapter = "15",
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keywords = "genetic algorithms, genetic programming, Evolutionary
computation, Bio-inspired computing, Metaheuristics,
Astroinformatics, Geoinformatics",
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isbn13 = "978-0-12-819154-5",
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DOI = "doi:10.1016/B978-0-12-819154-5.00026-6",
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URL = "http://www.sciencedirect.com/science/article/pii/B9780128191545000266",
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URL = "http://www.sciencedirect.com/science/article/pii/B9780128191545000266",
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URL = "https://hal.inria.fr/hal-02880731",
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URL = "https://hal.inria.fr/hal-02880731/document",
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URL = "https://hal.inria.fr/hal-02880731/file/chapter13.pdf",
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URL = "https://www.sciencedirect.com/science/article/pii/B9780128191545000266",
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abstract = "Knowledge discovery from data typically includes
solving some type of an optimization problem that can
be efficiently addressed using algorithms belonging to
the class of evolutionary and bio-inspired computation.
In this chapter, we give an overview of the various
kinds of evolutionary algorithms, such as genetic
algorithms, evolutionary strategy, evolutionary and
genetic programming, differential evolution, and
coevolutionary algorithms, as well as several other
bio-inspired approaches, like swarm intelligence and
artificial immune systems. After elaborating on the
methodology, we provide numerous examples of
applications in astronomy and geoscience and show how
these algorithms can be applied within a distributed
environment, by making use of parallel computing, which
is essential when dealing with Big Data",
- }
Genetic Programming entries for
Zaineb Chelly Dagdia
Miroslav Mirchev
Citations