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Encyclopedia of Computational Neuroscience pp 1152–1158Cite as

Evolutionary Algorithms

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Synonyms

Evolutionary strategies/genetic algorithms

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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Author information

Authors and Affiliations

  1. Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA

    Shaul Druckmann

Authors
  1. Shaul Druckmann
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Corresponding author

Correspondence to Shaul Druckmann .

Editor information

Editors and Affiliations

  1. Department of Biology, Emory University, Atlanta, GA, USA

    Dieter Jaeger

  2. Department of Biomedical Engineering, Florida International University, Miami, FL, USA

    Ranu Jung

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© 2015 Springer Science+Business Media New York

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Druckmann, S. (2015). Evolutionary Algorithms. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6675-8_159

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