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Evolutionary algorithm
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Evolutionary algorithm

An evolutionary algorithm (also EA, Evolutionary Computation, Artificial Evolution) is an algorithm using evolutionary techniques inspired by mechanisms from biological evolution such as natural selection, mutation and recombination to find an optimal configuration for a specific system within specific constraints.

Evolutionary algorithms include:

Most of these techniques are similar in spirit, but differ largely in the details of their implementation and the nature of the particular problem domains they have been applied to. Evolutionary algorithms are often used to design engineering systems in the place of manual design where the complexity of the optimisation problem is beyond human comprehension. Recently they have also been used to augment human design.

Table of contents
1 EC as framework for evolutionary modeling
2 Limitations in current EA systems
3 See also
4 External links

EC as framework for evolutionary modeling

Evolutionary computation and algorithms have also been used as an experimental framework within which to validate theories about evolution and natural selection, particularly through the work in artificial life. Techniques from evolutionary algorithms applied to the modelling of biological evolution mostly model microevolutionary processes, however some computer simulations commonly called artificial life such as Tierra attempt to model macroevolutionary dynamics.

Limitations in current EA systems

A limitation of evolutionary algorithms is their lack of developmental mappings from genotype to phenotype. In nature, the fertilized egg cell undergoes a complex process known as embryogenesis to become a mature phenotype. This indirect encoding is believed to make the genetic search more robust (i.e. reduce the probability of fatal mutations), and also may improve the evolvability of the organism. Recent work in the field of artificial embryogeny, or artificial developmental systems, seeks to address these concerns.

See also

External links