Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12188/28326
Title: | When Evolutionary Computing Meets Astro- and Geoinformatics | Authors: | Dagdia, Zaineb Chelly Mirchev, Miroslav |
Keywords: | evolutionary computation bio-inspired computing metaheuristics astroinformatics geoinformatics |
Issue Date: | 2020 | Publisher: | Elsevier | Abstract: | Knowledge 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. | Description: | A book chapter part of Knowledge Discovery in Big Data from Astronomy and Earth Observation | URI: | http://hdl.handle.net/20.500.12188/28326 | DOI: | 10.1016/b978-0-12-819154-5.00026-6 |
Appears in Collections: | Faculty of Computer Science and Engineering: Journal Articles |
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File | Description | Size | Format | |
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Knowledge_Discovery_in_Big_data_in_Astro_Geo_sciences__EA_chapter_.pdf | 650.87 kB | Adobe PDF | View/Open |
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