Genetic algorithms in astronomy and astrophysics

Abstract

Genetic algorithms (GAs) emulate the process of biological evolution, in a computational setting, in order to generate good solutions to difficult search and optimisation problems. GA-based optimisers tend to be extremely robust and versatile compared to most traditional techniques used to solve optimisation problems. This review paper provides a very brief introduction to GAs and outlines their utility in astronomy and astrophysics.

6 pages, 3 figures. Review paper in Proc. SAIP2011 (ISBN: 978-1-86888-688-3), ed. I. Basson and A. E. Botha, available online at http://www.saip.org.za

References in corpus (1)

• Genetic algorithm for robotic telescope scheduling

Cited by in corpus (5)

• Hints of dark energy anisotropic stress using Machine Learning
• Machine Learning meets the redshift evolution of the CMB Temperature
• Dark energy by natural evolution: Constraining dark energy using Approximate Bayesian Computation
• Testing the $\Lambda$CDM paradigm with growth rate data and machine learning
• Evolutionary algorithm-based analysis of gravitational microlensing lightcurves