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dc.contributor.authorDagdia, Zaineb Chellyen_US
dc.contributor.authorMirchev, Miroslaven_US
dc.date.accessioned2023-10-27T06:48:15Z-
dc.date.available2023-10-27T06:48:15Z-
dc.date.issued2020-
dc.identifier.urihttp://hdl.handle.net/20.500.12188/28326-
dc.descriptionA book chapter part of Knowledge Discovery in Big Data from Astronomy and Earth Observationen_US
dc.description.abstractKnowledge discovery from data typically include 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 co-evolutionary 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.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectevolutionary computationen_US
dc.subjectbio-inspired computingen_US
dc.subjectmetaheuristicsen_US
dc.subjectastroinformaticsen_US
dc.subjectgeoinformaticsen_US
dc.titleWhen Evolutionary Computing Meets Astro- and Geoinformaticsen_US
dc.typeBook chapteren_US
dc.identifier.doi10.1016/b978-0-12-819154-5.00026-6-
dc.identifier.urlhttps://api.elsevier.com/content/article/PII:B9780128191545000266?httpAccept=text/xml-
dc.identifier.urlhttps://api.elsevier.com/content/article/PII:B9780128191545000266?httpAccept=text/plain-
item.fulltextWith Fulltext-
item.grantfulltextopen-
crisitem.author.deptFaculty of Computer Science and Engineering-
Appears in Collections:Faculty of Computer Science and Engineering: Journal Articles
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