Neuroevolution Guide, Meaning , Facts, Information and Description

Neuroevolution, or neuro-evolution, is the use of genetic algorithms to train artificial neural networks. It is useful for applications such as games and robotic motor control, where it is easy to measure a network's performance at a task but difficult or impossible to create a syllabus of correct input-output pairs for use with a supervised learning algorithm.

There are many different neuroevolutionary algorithms. A distinction is made between those that only evolve the values of the connection weights for a network of pre-specified topology, vs. those that evolve the topology of the network in addition to the weights. (However, there are no standardized terms for the distinction.)

Direct-encoding methods use floating-point numbers in the genetic algorithm's chromosomes to directly specify the values of a network's connection weights. More sophisticated indirect encoding methods are also possible.

See also: NeuroEvolution of Augmented Topologies (NEAT)

External links

• University of Texas neuroevolution page (has downloadable papers on methods and applications)

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