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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