evolutionary algorithm

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evolutionary algorithm

(EA) An algorithm which incorporates aspects of natural selection or survival of the fittest. An evolutionary algorithm maintains a population of structures (usually randomly generated initially), that evolves according to rules of selection, recombination, mutation and survival, referred to as genetic operators. A shared "environment" determines the fitness or performance of each individual in the population. The fittest individuals are more likely to be selected for reproduction (retention or duplication), while recombination and mutation modify those individuals, yielding potentially superior ones.

EAs are one kind of evolutionary computation and differ from genetic algorithms. A GA generates each individual from some encoded form known as a "chromosome" and it is these which are combined or mutated to breed new individuals.

EAs are useful for optimisation when other techniques such as gradient descent or direct, analytical discovery are not possible. Combinatoric and real-valued function optimisation in which the optimisation surface or fitness landscape is "rugged", possessing many locally optimal solutions, are well suited for evolutionary algorithms.
This article is provided by FOLDOC - Free Online Dictionary of Computing (foldoc.org)
References in periodicals archive ?
Until now, artificial evolution has typically been run on a computer which is external to the swarm, with the best strategy then copied to the robots.
Professor Noel Sharkey, a world expert on machine learning and biorobotics is conducting the world's first experiment into the artificial evolution of a robot food chain.
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About a year ago Stephanie Forrest of the University of New Mexico and Terry Jones of the Santa Fe Institute - the nerve center of "complexity" research - were fooling around with ECHO, an artificial evolution program developed by Chris Langton, the father of "artificial life." Like most research in the emerging field of complexity, ECHO involves creating "digital organisms," small snippets of computer code equipped with instructions to help them survive and replicate in the silicon environment.
The techniques rely on guided trial-and-error strategies inspired by Charles Darwin's theory of evolution by natural selection and known as evolutionary algorithms or artificial evolution (SN: 7/23/94, p.
As a generic example of artificial evolution we consider genetic algorithms.
Chapter 2 deals with the nature of selection on single characters in more complex systems, with very detailed reviews of artificial selection experiments for a variety of traits and of the artificial evolution of novel metabolic functions in bacteria, with a much briefer account of some of the classic studies of selection in the wild.