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



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...

This is a preview of subscription content, log in to check access.


  1. Achard P, De Schutter E (2006) Complex parameter landscape for a complex neuron model. PLoS Comput Biol 2(7):e94PubMedCentralPubMedGoogle Scholar
  2. Collins RJ, Jefferson DR (1992) The evolution of sexual selection and female choice. In: Varela FJ, Bourgine P (eds) Toward a practice of autonomous systems: proceedings of the first European conference on artificial life. MIT Press, Cambridge, MAGoogle Scholar
  3. Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, Chichester/New YorkGoogle Scholar
  4. Druckmann S et al (2007) A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data. Front Neurosci 1(1):7–18PubMedCentralPubMedGoogle Scholar
  5. Fisher RA (1958) The genetical theory of natural selection. Dover, New YorkGoogle Scholar
  6. Fogel LJ et al (1966) Artificial intelligence through simulated evolution. Wiley, New YorkGoogle Scholar
  7. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, ReadingGoogle Scholar
  8. Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press, Cambridge, MAGoogle Scholar
  9. Keren N et al (2009) Experimentally guided modelling of dendritic excitability in rat neocortical pyramidal neurones. J Physiol 587(Pt 7):1413–1437PubMedCentralPubMedGoogle Scholar
  10. Kirkpatrick M (1982) Sexual selection and the evolution of female choice. Evolution 36:1–12Google Scholar
  11. Mitchell M (1996) An introduction to genetic algorithms. MIT Press, Cambridge, MAGoogle Scholar
  12. Rechenberg I (1965) Cybernetic solution path of an experimental problem. Ministry of Aviation, Royal Aircraft Establishment, FarnboroughGoogle Scholar
  13. Schwefel HP (1977) Numerische Optimierung von Computer-Modellen mittels der Evolutionsstrategie. Birkhauser, BaselGoogle Scholar
  14. Smolinski TG, Prinz AA (2009) Multi-objective evolutionary algorithms for model neuron parameter value selection matching biological behavior under different simulation scenarios. BMC Neurosci 10:260Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Janelia Farm Research CampusHoward Hughes Medical InstituteAshburnUSA