HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Book sections

When Evolutionary Computing Meets Astro- and Geoinformatics

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.
Complete list of metadata

Cited literature [98 references]  Display  Hide  Download

https://hal.inria.fr/hal-02880731
Contributor : Zaineb Chelly Dagdia Connect in order to contact the contributor
Submitted on : Thursday, June 25, 2020 - 3:52:23 PM
Last modification on : Tuesday, May 3, 2022 - 3:13:44 AM
Long-term archiving on: : Wednesday, September 23, 2020 - 4:03:21 PM

File

chapter13.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-02880731, version 1

Citation

Zaineb Chelly Dagdia, Miroslav Mirchev. When Evolutionary Computing Meets Astro- and Geoinformatics. Knowledge Discovery in Big Data from Astronomy and Earth Observation, pp.283-306, 2020. ⟨hal-02880731⟩

Share

Metrics

Record views

55

Files downloads

287