Encyclopedia of Computational Neuroscience

2015 Edition
| Editors: Dieter Jaeger, Ranu Jung

Evolutionary Algorithms

Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-6675-8_159

Synonyms

Definition

Evolutionary algorithms are a family of computational approaches inspired by natural selection that address two main topics: developing algorithms for optimization and understanding biological evolution. The former use is the more common, evolutionary algorithms being one of the main tools for parallel optimization. Indeed, they have been applied successfully to diverse optimization problems such as modeling the electrical properties of neurons (Achard and De Schutter 2006; Druckmann et al. 2007; Keren et al. 2009; Smolinski and Prinz 2009), automated writing of programs for specific tasks, and financial market modeling.

Detailed Description

The idea that natural evolution could be used as an inspiration for optimization algorithms rose in the 1950s and 1960s (Rechenberg 1965; Fogel et al. 1966; Schwefel 1977). One of the most widespread forms, genetic algorithms (GAs), was invented by J. Holland in the 1960s. At their...

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References

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Janelia Farm Research CampusHoward Hughes Medical InstituteAshburnUSA